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Sample records for finland granger causality

  1. Granger causality revisited

    PubMed Central

    Friston, Karl J.; Bastos, André M.; Oswal, Ashwini; van Wijk, Bernadette; Richter, Craig; Litvak, Vladimir

    2014-01-01

    This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality — providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes — as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling. PMID:25003817

  2. Granger causality revisited.

    PubMed

    Friston, Karl J; Bastos, André M; Oswal, Ashwini; van Wijk, Bernadette; Richter, Craig; Litvak, Vladimir

    2014-11-01

    This technical paper offers a critical re-evaluation of (spectral) Granger causality measures in the analysis of biological timeseries. Using realistic (neural mass) models of coupled neuronal dynamics, we evaluate the robustness of parametric and nonparametric Granger causality. Starting from a broad class of generative (state-space) models of neuronal dynamics, we show how their Volterra kernels prescribe the second-order statistics of their response to random fluctuations; characterised in terms of cross-spectral density, cross-covariance, autoregressive coefficients and directed transfer functions. These quantities in turn specify Granger causality - providing a direct (analytic) link between the parameters of a generative model and the expected Granger causality. We use this link to show that Granger causality measures based upon autoregressive models can become unreliable when the underlying dynamics is dominated by slow (unstable) modes - as quantified by the principal Lyapunov exponent. However, nonparametric measures based on causal spectral factors are robust to dynamical instability. We then demonstrate how both parametric and nonparametric spectral causality measures can become unreliable in the presence of measurement noise. Finally, we show that this problem can be finessed by deriving spectral causality measures from Volterra kernels, estimated using dynamic causal modelling.

  3. Redundant variables and Granger causality

    NASA Astrophysics Data System (ADS)

    Angelini, L.; de Tommaso, M.; Marinazzo, D.; Nitti, L.; Pellicoro, M.; Stramaglia, S.

    2010-03-01

    We discuss the use of multivariate Granger causality in presence of redundant variables: the application of the standard analysis, in this case, leads to under estimation of causalities. Using the un-normalized version of the causality index, we quantitatively develop the notions of redundancy and synergy in the frame of causality and propose two approaches to group redundant variables: (i) for a given target, the remaining variables are grouped so as to maximize the total causality and (ii) the whole set of variables is partitioned to maximize the sum of the causalities between subsets. We show the application to a real neurological experiment, aiming to a deeper understanding of the physiological basis of abnormal neuronal oscillations in the migraine brain. The outcome by our approach reveals the change in the informational pattern due to repetitive transcranial magnetic stimulations.

  4. Redundant variables and Granger causality.

    PubMed

    Angelini, L; de Tommaso, M; Marinazzo, D; Nitti, L; Pellicoro, M; Stramaglia, S

    2010-03-01

    We discuss the use of multivariate Granger causality in presence of redundant variables: the application of the standard analysis, in this case, leads to under estimation of causalities. Using the un-normalized version of the causality index, we quantitatively develop the notions of redundancy and synergy in the frame of causality and propose two approaches to group redundant variables: (i) for a given target, the remaining variables are grouped so as to maximize the total causality and (ii) the whole set of variables is partitioned to maximize the sum of the causalities between subsets. We show the application to a real neurological experiment, aiming to a deeper understanding of the physiological basis of abnormal neuronal oscillations in the migraine brain. The outcome by our approach reveals the change in the informational pattern due to repetitive transcranial magnetic stimulations.

  5. Nonlinear connectivity by Granger causality.

    PubMed

    Marinazzo, Daniele; Liao, Wei; Chen, Huafu; Stramaglia, Sebastiano

    2011-09-15

    The communication among neuronal populations, reflected by transient synchronous activity, is the mechanism underlying the information processing in the brain. Although it is widely assumed that the interactions among those populations (i.e. functional connectivity) are highly nonlinear, the amount of nonlinear information transmission and its functional roles are not clear. The state of the art to understand the communication between brain systems are dynamic causal modeling (DCM) and Granger causality. While DCM models nonlinear couplings, Granger causality, which constitutes a major tool to reveal effective connectivity, and is widely used to analyze EEG/MEG data as well as fMRI signals, is usually applied in its linear version. In order to capture nonlinear interactions between even short and noisy time series, a few approaches have been proposed. We review them and focus on a recently proposed flexible approach has been recently proposed, consisting in the kernel version of Granger causality. We show the application of the proposed approach on EEG signals and fMRI data.

  6. Conditional Granger causality and partitioned Granger causality: differences and similarities.

    PubMed

    Malekpour, Sheida; Sethares, William A

    2015-12-01

    Neural information modeling and analysis often requires a measurement of the mutual influence among many signals. A common technique is the conditional Granger causality (cGC) which measures the influence of one time series on another time series in the presence of a third. Geweke has translated this condition into the frequency domain and has explored the mathematical relationships between the time and frequency domain expressions. Chen has observed that in practice, the expressions may return (meaningless) negative numbers, and has proposed an alternative which is based on a partitioned matrix scheme, which we call partitioned Granger causality (pGC). There has been some confusion in the literature about the relationship between cGC and pGC; some authors treat them as essentially identical measures, while others have noted that some properties (such as the relationship between the time and frequency domain expressions) do not hold for the pGC. This paper presents a series of matrix equalities that simplify the calculation of the pGC. In this simplified expression, the essential differences and similarities between the cGC and the pGC become clear; in essence, the pGC is dependent on only a subset of the parameters in the model estimation, and the noise residuals (which are uncorrelated in the cGC) need not be uncorrelated in the pGC. The mathematical results are illustrated with a simulation, and the measures are applied to an EEG dataset.

  7. Effect of measurement noise on Granger causality.

    PubMed

    Nalatore, Hariharan; Sasikumar, N; Rangarajan, Govindan

    2014-12-01

    Most of the signals recorded in experiments are inevitably contaminated by measurement noise. Hence, it is important to understand the effect of such noise on estimating causal relations between such signals. A primary tool for estimating causality is Granger causality. Granger causality can be computed by modeling the signal using a bivariate autoregressive (AR) process. In this paper, we greatly extend the previous analysis of the effect of noise by considering a bivariate AR process of general order p. From this analysis, we analytically obtain the dependence of Granger causality on various noise-dependent system parameters. In particular, we show that measurement noise can lead to spurious Granger causality and can suppress true Granger causality. These results are verified numerically. Finally, we show how true causality can be recovered numerically using the Kalman expectation maximization algorithm.

  8. Granger causality for state-space models

    NASA Astrophysics Data System (ADS)

    Barnett, Lionel; Seth, Anil K.

    2015-04-01

    Granger causality has long been a prominent method for inferring causal interactions between stochastic variables for a broad range of complex physical systems. However, it has been recognized that a moving average (MA) component in the data presents a serious confound to Granger causal analysis, as routinely performed via autoregressive (AR) modeling. We solve this problem by demonstrating that Granger causality may be calculated simply and efficiently from the parameters of a state-space (SS) model. Since SS models are equivalent to autoregressive moving average models, Granger causality estimated in this fashion is not degraded by the presence of a MA component. This is of particular significance when the data has been filtered, downsampled, observed with noise, or is a subprocess of a higher dimensional process, since all of these operations—commonplace in application domains as diverse as climate science, econometrics, and the neurosciences—induce a MA component. We show how Granger causality, conditional and unconditional, in both time and frequency domains, may be calculated directly from SS model parameters via solution of a discrete algebraic Riccati equation. Numerical simulations demonstrate that Granger causality estimators thus derived have greater statistical power and smaller bias than AR estimators. We also discuss how the SS approach facilitates relaxation of the assumptions of linearity, stationarity, and homoscedasticity underlying current AR methods, thus opening up potentially significant new areas of research in Granger causal analysis.

  9. Synergy, redundancy and unnormalized Granger causality.

    PubMed

    Stramaglia, S; Angelini, L; Cortes, J M; Marinazzo, D

    2015-08-01

    We analyze by means of Granger causality the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. Whilst fully conditioned Granger causality is not affected by synergy, the pairwise analysis fails to put in evidence synergetic effects. We show that maximization of the total Granger causality to a given target, over all the possible partitions of the set of driving variables, puts in evidence redundant multiplets of variables influencing the target, provided that an unnormalized definition of Granger causality is adopted. Along the same lines we also introduce a pairwise index of synergy (w.r.t. to information flow to a third variable) which is zero when two independent sources additively influence a common target; thus, this definition differs from previous definitions of synergy.

  10. Granger-causality maps of diffusion processes.

    PubMed

    Wahl, Benjamin; Feudel, Ulrike; Hlinka, Jaroslav; Wächter, Matthias; Peinke, Joachim; Freund, Jan A

    2016-02-01

    Granger causality is a statistical concept devised to reconstruct and quantify predictive information flow between stochastic processes. Although the general concept can be formulated model-free it is often considered in the framework of linear stochastic processes. Here we show how local linear model descriptions can be employed to extend Granger causality into the realm of nonlinear systems. This novel treatment results in maps that resolve Granger causality in regions of state space. Through examples we provide a proof of concept and illustrate the utility of these maps. Moreover, by integration we convert the local Granger causality into a global measure that yields a consistent picture for a global Ornstein-Uhlenbeck process. Finally, we recover invariance transformations known from the theory of autoregressive processes. PMID:26986337

  11. Granger-causality maps of diffusion processes.

    PubMed

    Wahl, Benjamin; Feudel, Ulrike; Hlinka, Jaroslav; Wächter, Matthias; Peinke, Joachim; Freund, Jan A

    2016-02-01

    Granger causality is a statistical concept devised to reconstruct and quantify predictive information flow between stochastic processes. Although the general concept can be formulated model-free it is often considered in the framework of linear stochastic processes. Here we show how local linear model descriptions can be employed to extend Granger causality into the realm of nonlinear systems. This novel treatment results in maps that resolve Granger causality in regions of state space. Through examples we provide a proof of concept and illustrate the utility of these maps. Moreover, by integration we convert the local Granger causality into a global measure that yields a consistent picture for a global Ornstein-Uhlenbeck process. Finally, we recover invariance transformations known from the theory of autoregressive processes.

  12. Canonical Granger causality between regions of interest.

    PubMed

    Ashrafulla, Syed; Haldar, Justin P; Joshi, Anand A; Leahy, Richard M

    2013-12-01

    Estimating and modeling functional connectivity in the brain is a challenging problem with potential applications in the understanding of brain organization and various neurological and neuropsychological conditions. An important objective in connectivity analysis is to determine the connections between regions of interest in the brain. However, traditional functional connectivity analyses have frequently focused on modeling interactions between time series recordings at individual sensors, voxels, or vertices despite the fact that a single region of interest will often include multiple such recordings. In this paper, we present a novel measure of interaction between regions of interest rather than individual signals. The proposed measure, termed canonical Granger causality, combines ideas from canonical correlation and Granger causality analysis to yield a measure that reflects directed causality between two regions of interest. In particular, canonical Granger causality uses optimized linear combinations of signals from each region of interest to enable accurate causality measurements from substantially less data compared to alternative multivariate methods that have previously been proposed for this scenario. The optimized linear combinations are obtained using a variation of a technique developed for optimization on the Stiefel manifold. We demonstrate the advantages of canonical Granger causality in comparison to alternative causality measures for a range of different simulated datasets. We also apply the proposed measure to local field potential data recorded in a macaque brain during a visuomotor task. Results demonstrate that canonical Granger causality can be used to identify causal relationships between striate and prestriate cortexes in cases where standard Granger causality is unable to identify statistically significant interactions.

  13. A copula approach to assessing Granger causality.

    PubMed

    Hu, Meng; Liang, Hualou

    2014-10-15

    In neuroscience, as in many other fields of science and engineering, it is crucial to assess the causal interactions among multivariate time series. Granger causality has been increasingly used to identify causal influence between time series based on multivariate autoregressive models. Such an approach is based on linear regression framework with implicit Gaussian assumption of model noise residuals having constant variance. As a consequence, this measure cannot detect the cause-effect relationship in high-order moments and nonlinear causality. Here, we propose an effective model-free, copula-based Granger causality measure that can be used to reveal nonlinear and high-order moment causality. We first formulate Granger causality as the log-likelihood ratio in terms of conditional distribution, and then derive an efficient estimation procedure using conditional copula. We use resampling techniques to build a baseline null-hypothesis distribution from which statistical significance can be derived. We perform a series of simulations to investigate the performance of our copula-based Granger causality, and compare its performance against other state-of-the-art techniques. Our method is finally applied to neural field potential time series recorded from visual cortex of a monkey while performing a visual illusion task.

  14. On the spectral formulation of Granger causality.

    PubMed

    Chicharro, D

    2011-12-01

    Spectral measures of causality are used to explore the role of different rhythms in the causal connectivity between brain regions. We study several spectral measures related to Granger causality, comprising the bivariate and conditional Geweke measures, the directed transfer function, and the partial directed coherence. We derive the formulation of dependence and causality in the spectral domain from the more general formulation in the information-theory framework. We argue that the transfer entropy, the most general measure derived from the concept of Granger causality, lacks a spectral representation in terms of only the processes associated with the recorded signals. For all the spectral measures we show how they are related to mutual information rates when explicitly considering the parametric autoregressive representation of the processes. In this way we express the conditional Geweke spectral measure in terms of a multiple coherence involving innovation variables inherent to the autoregressive representation. We also link partial directed coherence with Sims' criterion of causality. Given our results, we discuss the causal interpretation of the spectral measures related to Granger causality and stress the necessity to explicitly consider their specific formulation based on modeling the signals as linear Gaussian stationary autoregressive processes.

  15. Localizing epileptic seizure onsets with Granger causality

    NASA Astrophysics Data System (ADS)

    Adhikari, Bhim M.; Epstein, Charles M.; Dhamala, Mukesh

    2013-09-01

    Accurate localization of the epileptic seizure onset zones (SOZs) is crucial for successful surgery, which usually depends on the information obtained from intracranial electroencephalography (IEEG) recordings. The visual criteria and univariate methods of analyzing IEEG recordings have not always produced clarity on the SOZs for resection and ultimate seizure freedom for patients. Here, to contribute to improving the localization of the SOZs and to understanding the mechanism of seizure propagation over the brain, we applied spectral interdependency methods to IEEG time series recorded from patients during seizures. We found that the high-frequency (>80 Hz) Granger causality (GC) occurs before the onset of any visible ictal activity and causal relationships involve the recording electrodes where clinically identifiable seizures later develop. These results suggest that high-frequency oscillatory network activities precede and underlie epileptic seizures, and that GC spectral measures derived from IEEG can assist in precise delineation of seizure onset times and SOZs.

  16. Manifest Variable Granger Causality Models for Developmental Research: A Taxonomy

    ERIC Educational Resources Information Center

    von Eye, Alexander; Wiedermann, Wolfgang

    2015-01-01

    Granger models are popular when it comes to testing hypotheses that relate series of measures causally to each other. In this article, we propose a taxonomy of Granger causality models. The taxonomy results from crossing the four variables Order of Lag, Type of (Contemporaneous) Effect, Direction of Effect, and Segment of Dependent Series…

  17. Reliability of the Granger causality inference

    NASA Astrophysics Data System (ADS)

    Zhou, Douglas; Zhang, Yaoyu; Xiao, Yanyang; Cai, David

    2014-04-01

    How to characterize information flows in physical, biological, and social systems remains a major theoretical challenge. Granger causality (GC) analysis has been widely used to investigate information flow through causal interactions. We address one of the central questions in GC analysis, that is, the reliability of the GC evaluation and its implications for the causal structures extracted by this analysis. Our work reveals that the manner in which a continuous dynamical process is projected or coarse-grained to a discrete process has a profound impact on the reliability of the GC inference, and different sampling may potentially yield completely opposite inferences. This inference hazard is present for both linear and nonlinear processes. We emphasize that there is a hazard of reaching incorrect conclusions about network topologies, even including statistical (such as small-world or scale-free) properties of the networks, when GC analysis is blindly applied to infer the network topology. We demonstrate this using a small-world network for which a drastic loss of small-world attributes occurs in the reconstructed network using the standard GC approach. We further show how to resolve the paradox that the GC analysis seemingly becomes less reliable when more information is incorporated using finer and finer sampling. Finally, we present strategies to overcome these inference artifacts in order to obtain a reliable GC result.

  18. Spatio-temporal Granger causality: a new framework

    PubMed Central

    Luo, Qiang; Lu, Wenlian; Cheng, Wei; Valdes-Sosa, Pedro A.; Wen, Xiaotong; Ding, Mingzhou; Feng, Jianfeng

    2015-01-01

    That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data. PMID:23643924

  19. Spatio-temporal Granger causality: a new framework.

    PubMed

    Luo, Qiang; Lu, Wenlian; Cheng, Wei; Valdes-Sosa, Pedro A; Wen, Xiaotong; Ding, Mingzhou; Feng, Jianfeng

    2013-10-01

    That physiological oscillations of various frequencies are present in fMRI signals is the rule, not the exception. Herein, we propose a novel theoretical framework, spatio-temporal Granger causality, which allows us to more reliably and precisely estimate the Granger causality from experimental datasets possessing time-varying properties caused by physiological oscillations. Within this framework, Granger causality is redefined as a global index measuring the directed information flow between two time series with time-varying properties. Both theoretical analyses and numerical examples demonstrate that Granger causality is a monotonically increasing function of the temporal resolution used in the estimation. This is consistent with the general principle of coarse graining, which causes information loss by smoothing out very fine-scale details in time and space. Our results confirm that the Granger causality at the finer spatio-temporal scales considerably outperforms the traditional approach in terms of an improved consistency between two resting-state scans of the same subject. To optimally estimate the Granger causality, the proposed theoretical framework is implemented through a combination of several approaches, such as dividing the optimal time window and estimating the parameters at the fine temporal and spatial scales. Taken together, our approach provides a novel and robust framework for estimating the Granger causality from fMRI, EEG, and other related data.

  20. Assessing thalamocortical functional connectivity with Granger causality.

    PubMed

    Chen, Cheng; Maybhate, Anil; Israel, David; Thakor, Nitish V; Jia, Xiaofeng

    2013-09-01

    Assessment of network connectivity across multiple brain regions is critical to understanding the mechanisms underlying various neurological disorders. Conventional methods for assessing dynamic interactions include cross-correlation and coherence analysis. However, these methods do not reveal the direction of information flow, which is important for studying the highly directional neurological system. Granger causality (GC) analysis can characterize the directional influences between two systems. We tested GC analysis for its capability to capture directional interactions within both simulated and in vivo neural networks. The simulated networks consisted of Hindmarsh-Rose neurons; GC analysis was used to estimate the causal influences between two model networks. Our analysis successfully detected asymmetrical interactions between these networks ( , t -test). Next, we characterized the relationship between the "electrical synaptic strength" in the model networks and interactions estimated by GC analysis. We demonstrated the novel application of GC to monitor interactions between thalamic and cortical neurons following ischemia induced brain injury in a rat model of cardiac arrest (CA). We observed that during the post-CA acute period the GC interactions from the thalamus to the cortex were consistently higher than those from the cortex to the thalamus ( 1.983±0.278 times higher, p = 0.021). In addition, the dynamics of GC interactions between the thalamus and the cortex were frequency dependent. Our study demonstrated the feasibility of GC to monitor the dynamics of thalamocortical interactions after a global nervous system injury such as CA-induced ischemia, and offers preferred alternative applications in characterizing other inter-regional interactions in an injured brain.

  1. Granger causality and transfer entropy are equivalent for Gaussian variables.

    PubMed

    Barnett, Lionel; Barrett, Adam B; Seth, Anil K

    2009-12-01

    Granger causality is a statistical notion of causal influence based on prediction via vector autoregression. Developed originally in the field of econometrics, it has since found application in a broader arena, particularly in neuroscience. More recently transfer entropy, an information-theoretic measure of time-directed information transfer between jointly dependent processes, has gained traction in a similarly wide field. While it has been recognized that the two concepts must be related, the exact relationship has until now not been formally described. Here we show that for Gaussian variables, Granger causality and transfer entropy are entirely equivalent, thus bridging autoregressive and information-theoretic approaches to data-driven causal inference.

  2. Kernel canonical-correlation Granger causality for multiple time series

    NASA Astrophysics Data System (ADS)

    Wu, Guorong; Duan, Xujun; Liao, Wei; Gao, Qing; Chen, Huafu

    2011-04-01

    Canonical-correlation analysis as a multivariate statistical technique has been applied to multivariate Granger causality analysis to infer information flow in complex systems. It shows unique appeal and great superiority over the traditional vector autoregressive method, due to the simplified procedure that detects causal interaction between multiple time series, and the avoidance of potential model estimation problems. However, it is limited to the linear case. Here, we extend the framework of canonical correlation to include the estimation of multivariate nonlinear Granger causality for drawing inference about directed interaction. Its feasibility and effectiveness are verified on simulated data.

  3. Statistical analysis of single-trial Granger causality spectra.

    PubMed

    Brovelli, Andrea

    2012-01-01

    Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated two hypothetical experimental conditions: the first mimicked a constant and unidirectional coupling, whereas the second modelled a linear increase in coupling across trials. The statistical analysis of single-trial Granger causality spectra, based on t-tests and linear regression, successfully recovered the underlying pattern of directional influence. In addition, we characterised the minimum number of trials and coupling strengths required for significant detection of directionality. Finally, we demonstrated the relevance for neurophysiology by analysing two local field potentials (LFPs) simultaneously recorded from the prefrontal and premotor cortices of a macaque monkey performing a conditional visuomotor task. Our results suggest that the combination of single-trial Granger causality spectra and statistical inference provides a valuable tool for the analysis of large-scale cortical networks and brain connectivity.

  4. Mitigating the effects of measurement noise on Granger causality

    SciTech Connect

    Nalatore, Hariharan; Ding Mingzhou; Rangarajan, Govindan

    2007-03-15

    Computing Granger causal relations among bivariate experimentally observed time series has received increasing attention over the past few years. Such causal relations, if correctly estimated, can yield significant insights into the dynamical organization of the system being investigated. Since experimental measurements are inevitably contaminated by noise, it is thus important to understand the effects of such noise on Granger causality estimation. The first goal of this paper is to provide an analytical and numerical analysis of this problem. Specifically, we show that, due to noise contamination (1) spurious causality between two measured variables can arise and (2) true causality can be suppressed. The second goal of the paper is to provide a denoising strategy to mitigate this problem. Specifically, we propose a denoising algorithm based on the combined use of the Kalman filter theory and the expectation-maximization algorithm. Numerical examples are used to demonstrate the effectiveness of the denoising approach.

  5. On directed information theory and Granger causality graphs.

    PubMed

    Amblard, Pierre-Olivier; Michel, Olivier J J

    2011-02-01

    Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.

  6. Granger causality--statistical analysis under a configural perspective.

    PubMed

    von Eye, Alexander; Wiedermann, Wolfgang; Mun, Eun-Young

    2014-03-01

    The concept of Granger causality can be used to examine putative causal relations between two series of scores. Based on regression models, it is asked whether one series can be considered the cause for the second series. In this article, we propose extending the pool of methods available for testing hypotheses that are compatible with Granger causation by adopting a configural perspective. This perspective allows researchers to assume that effects exist for specific categories only or for specific sectors of the data space, but not for other categories or sectors. Configural Frequency Analysis (CFA) is proposed as the method of analysis from a configural perspective. CFA base models are derived for the exploratory analysis of Granger causation. These models are specified so that they parallel the regression models used for variable-oriented analysis of hypotheses of Granger causation. An example from the development of aggression in adolescence is used. The example shows that only one pattern of change in aggressive impulses over time Granger-causes change in physical aggression against peers.

  7. Inferring connectivity in networked dynamical systems: Challenges using Granger causality

    NASA Astrophysics Data System (ADS)

    Lusch, Bethany; Maia, Pedro D.; Kutz, J. Nathan

    2016-09-01

    Determining the interactions and causal relationships between nodes in an unknown networked dynamical system from measurement data alone is a challenging, contemporary task across the physical, biological, and engineering sciences. Statistical methods, such as the increasingly popular Granger causality, are being broadly applied for data-driven discovery of connectivity in fields from economics to neuroscience. A common version of the algorithm is called pairwise-conditional Granger causality, which we systematically test on data generated from a nonlinear model with known causal network structure. Specifically, we simulate networked systems of Kuramoto oscillators and use the Multivariate Granger Causality Toolbox to discover the underlying coupling structure of the system. We compare the inferred results to the original connectivity for a wide range of parameters such as initial conditions, connection strengths, community structures, and natural frequencies. Our results show a significant systematic disparity between the original and inferred network, unless the true structure is extremely sparse or dense. Specifically, the inferred networks have significant discrepancies in the number of edges and the eigenvalues of the connectivity matrix, demonstrating that they typically generate dynamics which are inconsistent with the ground truth. We provide a detailed account of the dynamics for the Erdős-Rényi network model due to its importance in random graph theory and network science. We conclude that Granger causal methods for inferring network structure are highly suspect and should always be checked against a ground truth model. The results also advocate the need to perform such comparisons with any network inference method since the inferred connectivity results appear to have very little to do with the ground truth system.

  8. Nonlinear parametric model for Granger causality of time series

    NASA Astrophysics Data System (ADS)

    Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

    2006-06-01

    The notion of Granger causality between two time series examines if the prediction of one series could be improved by incorporating information of the other. In particular, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. We propose a radial basis function approach to nonlinear Granger causality. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefulness of this measure of causality is shown in two applications. In the first application, a physiological one, we consider time series of heart rate and blood pressure in congestive heart failure patients and patients affected by sepsis: we find that sepsis patients, unlike congestive heart failure patients, show symmetric causal relationships between the two time series. In the second application, we consider the feedback loop in a model of excitatory and inhibitory neurons: we find that in this system causality measures the combined influence of couplings and membrane time constants.

  9. On the Inference of Functional Circadian Networks Using Granger Causality

    PubMed Central

    Pourzanjani, Arya; Herzog, Erik D.; Petzold, Linda R.

    2015-01-01

    Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals. PMID:26413748

  10. A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity.

    PubMed

    Schmidt, Christoph; Pester, Britta; Schmid-Hertel, Nicole; Witte, Herbert; Wismüller, Axel; Leistritz, Lutz

    2016-01-01

    Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure.

  11. On the Inference of Functional Circadian Networks Using Granger Causality.

    PubMed

    Pourzanjani, Arya; Herzog, Erik D; Petzold, Linda R

    2015-01-01

    Being able to infer one way direct connections in an oscillatory network such as the suprachiastmatic nucleus (SCN) of the mammalian brain using time series data is difficult but crucial to understanding network dynamics. Although techniques have been developed for inferring networks from time series data, there have been no attempts to adapt these techniques to infer directional connections in oscillatory time series, while accurately distinguishing between direct and indirect connections. In this paper an adaptation of Granger Causality is proposed that allows for inference of circadian networks and oscillatory networks in general called Adaptive Frequency Granger Causality (AFGC). Additionally, an extension of this method is proposed to infer networks with large numbers of cells called LASSO AFGC. The method was validated using simulated data from several different networks. For the smaller networks the method was able to identify all one way direct connections without identifying connections that were not present. For larger networks of up to twenty cells the method shows excellent performance in identifying true and false connections; this is quantified by an area-under-the-curve (AUC) 96.88%. We note that this method like other Granger Causality-based methods, is based on the detection of high frequency signals propagating between cell traces. Thus it requires a relatively high sampling rate and a network that can propagate high frequency signals.

  12. A Multivariate Granger Causality Concept towards Full Brain Functional Connectivity

    PubMed Central

    Schmid-Hertel, Nicole; Witte, Herbert; Wismüller, Axel; Leistritz, Lutz

    2016-01-01

    Detecting changes of spatially high-resolution functional connectivity patterns in the brain is crucial for improving the fundamental understanding of brain function in both health and disease, yet still poses one of the biggest challenges in computational neuroscience. Currently, classical multivariate Granger Causality analyses of directed interactions between single process components in coupled systems are commonly restricted to spatially low- dimensional data, which requires a pre-selection or aggregation of time series as a preprocessing step. In this paper we propose a new fully multivariate Granger Causality approach with embedded dimension reduction that makes it possible to obtain a representation of functional connectivity for spatially high-dimensional data. The resulting functional connectivity networks may consist of several thousand vertices and thus contain more detailed information compared to connectivity networks obtained from approaches based on particular regions of interest. Our large scale Granger Causality approach is applied to synthetic and resting state fMRI data with a focus on how well network community structure, which represents a functional segmentation of the network, is preserved. It is demonstrated that a number of different community detection algorithms, which utilize a variety of algorithmic strategies and exploit topological features differently, reveal meaningful information on the underlying network module structure. PMID:27064897

  13. Investigating Driver Fatigue versus Alertness Using the Granger Causality Network

    PubMed Central

    Kong, Wanzeng; Lin, Weicheng; Babiloni, Fabio; Hu, Sanqing; Borghini, Gianluca

    2015-01-01

    Driving fatigue has been identified as one of the main factors affecting drivers’ safety. The aim of this study was to analyze drivers’ different mental states, such as alertness and drowsiness, and find out a neurometric indicator able to detect drivers’ fatigue level in terms of brain networks. Twelve young, healthy subjects were recruited to take part in a driver fatigue experiment under different simulated driving conditions. The Electroencephalogram (EEG) signals of the subjects were recorded during the whole experiment and analyzed by using Granger-Causality-based brain effective networks. It was that the topology of the brain networks and the brain’s ability to integrate information changed when subjects shifted from the alert to the drowsy stage. In particular, there was a significant difference in terms of strength of Granger causality (GC) in the frequency domain and the properties of the brain effective network i.e., causal flow, global efficiency and characteristic path length between such conditions. Also, some changes were more significant over the frontal brain lobes for the alpha frequency band. These findings might be used to detect drivers’ fatigue levels, and as reference work for future studies. PMID:26251909

  14. Characterizing time series: when Granger causality triggers complex networks

    NASA Astrophysics Data System (ADS)

    Ge, Tian; Cui, Yindong; Lin, Wei; Kurths, Jürgen; Liu, Chong

    2012-08-01

    In this paper, we propose a new approach to characterize time series with noise perturbations in both the time and frequency domains by combining Granger causality and complex networks. We construct directed and weighted complex networks from time series and use representative network measures to describe their physical and topological properties. Through analyzing the typical dynamical behaviors of some physical models and the MIT-BIHMassachusetts Institute of Technology-Beth Israel Hospital. human electrocardiogram data sets, we show that the proposed approach is able to capture and characterize various dynamics and has much potential for analyzing real-world time series of rather short length.

  15. How to detect the Granger-causal flow direction in the presence of additive noise?

    PubMed

    Vinck, Martin; Huurdeman, Lisanne; Bosman, Conrado A; Fries, Pascal; Battaglia, Francesco P; Pennartz, Cyriel M A; Tiesinga, Paul H

    2015-03-01

    Granger-causality metrics have become increasingly popular tools to identify directed interactions between brain areas. However, it is known that additive noise can strongly affect Granger-causality metrics, which can lead to spurious conclusions about neuronal interactions. To solve this problem, previous studies have proposed the detection of Granger-causal directionality, i.e. the dominant Granger-causal flow, using either the slope of the coherency (Phase Slope Index; PSI), or by comparing Granger-causality values between original and time-reversed signals (reversed Granger testing). We show that for ensembles of vector autoregressive (VAR) models encompassing bidirectionally coupled sources, these alternative methods do not correctly measure Granger-causal directionality for a substantial fraction of VAR models, even in the absence of noise. We then demonstrate that uncorrelated noise has fundamentally different effects on directed connectivity metrics than linearly mixed noise, where the latter may result as a consequence of electric volume conduction. Uncorrelated noise only weakly affects the detection of Granger-causal directionality, whereas linearly mixed noise causes a large fraction of false positives for standard Granger-causality metrics and PSI, but not for reversed Granger testing. We further show that we can reliably identify cases where linearly mixed noise causes a large fraction of false positives by examining the magnitude of the instantaneous influence coefficient in a structural VAR model. By rejecting cases with strong instantaneous influence, we obtain an improved detection of Granger-causal flow between neuronal sources in the presence of additive noise. These techniques are applicable to real data, which we demonstrate using actual area V1 and area V4 LFP data, recorded from the awake monkey performing a visual attention task.

  16. Granger causality stock market networks: Temporal proximity and preferential attachment

    NASA Astrophysics Data System (ADS)

    Výrost, Tomáš; Lyócsa, Štefan; Baumöhl, Eduard

    2015-06-01

    The structure of return spillovers is examined by constructing Granger causality networks using daily closing prices of 20 developed markets from 2nd January 2006 to 31st December 2013. The data is properly aligned to take into account non-synchronous trading effects. The study of the resulting networks of over 94 sub-samples revealed three significant findings. First, after the recent financial crisis the impact of the US stock market has declined. Second, spatial probit models confirmed the role of the temporal proximity between market closing times for return spillovers, i.e. the time distance between national stock markets matters. Third, a preferential attachment between stock markets exists, i.e. the probability of the presence of spillover effects between any given two markets increases with their degree of connectedness to others.

  17. The opportune time to invest in residential properties - Engle-Granger cointegration test and Granger causality test approach

    NASA Astrophysics Data System (ADS)

    Chee-Yin, Yip; Hock-Eam, Lim

    2014-12-01

    This paper examines using housing supply as proxy to house prices, the causal relationship on house prices among 8 states in Malaysia by applying the Engle-Granger cointegration test and Granger causality test approach. The target states are Perak, Selangor, Penang, Federal Territory of Kuala Lumpur (WPKL or Kuala Lumpur), Kedah, Negeri Sembilan, Sabah and Sarawak. The primary aim of this study is to estimate how long (in months) house prices in Perak lag behind that of Selangor, Penang and WPKL. We classify the 8 states into two categories - developed and developing states. We use Engle-Granger cointegration test and Granger causality test to examine the long run and short run equilibrium relationship among the two categories.. It is found that the causal relationship is bidirectional in Perak and Sabah, Perak and Selangor while it is unidirectional for Perak and Sarawak, Perak and Penang, Perak and WPKL. The speed of deviation adjustment is about 273%, suggesting that the pricing dynamic of Perak has a 32- month or 2 3/4- year lag behind that of WPKL, Selangor and Penang. Such information will be useful to investors, house buyers and speculators.

  18. Causality and persistence in ecological systems: a nonparametric spectral granger causality approach.

    PubMed

    Detto, Matteo; Molini, Annalisa; Katul, Gabriel; Stoy, Paul; Palmroth, Sari; Baldocchi, Dennis

    2012-04-01

    Abstract Directionality in coupling, defined as the linkage relating causes to their effects at a later time, can be used to explain the core dynamics of ecological systems by untangling direct and feedback relationships between the different components of the systems. Inferring causality from measured ecological variables sampled through time remains a formidable challenge further made difficult by the action of periodic drivers overlapping the natural dynamics of the system. Periodicity in the drivers can often mask the self-sustained oscillations originating from the autonomous dynamics. While linear and direct causal relationships are commonly addressed in the time domain, using the well-established machinery of Granger causality (G-causality), the presence of periodic forcing requires frequency-based statistics (e.g., the Fourier transform), able to distinguish coupling induced by oscillations in external drivers from genuine endogenous interactions. Recent nonparametric spectral extensions of G-causality to the frequency domain pave the way for the scale-by-scale decomposition of causality, which can improve our ability to link oscillatory behaviors of ecological networks to causal mechanisms. The performance of both spectral G-causality and its conditional extension for multivariate systems is explored in quantifying causal interactions within ecological networks. Through two case studies involving synthetic and actual time series, it is demonstrated that conditional G-causality outperforms standard G-causality in identifying causal links and their concomitant timescales.

  19. Discovering graphical Granger causality using the truncating lasso penalty

    PubMed Central

    Shojaie, Ali; Michailidis, George

    2010-01-01

    Motivation: Components of biological systems interact with each other in order to carry out vital cell functions. Such information can be used to improve estimation and inference, and to obtain better insights into the underlying cellular mechanisms. Discovering regulatory interactions among genes is therefore an important problem in systems biology. Whole-genome expression data over time provides an opportunity to determine how the expression levels of genes are affected by changes in transcription levels of other genes, and can therefore be used to discover regulatory interactions among genes. Results: In this article, we propose a novel penalization method, called truncating lasso, for estimation of causal relationships from time-course gene expression data. The proposed penalty can correctly determine the order of the underlying time series, and improves the performance of the lasso-type estimators. Moreover, the resulting estimate provides information on the time lag between activation of transcription factors and their effects on regulated genes. We provide an efficient algorithm for estimation of model parameters, and show that the proposed method can consistently discover causal relationships in the large p, small n setting. The performance of the proposed model is evaluated favorably in simulated, as well as real, data examples. Availability: The proposed truncating lasso method is implemented in the R-package ‘grangerTlasso’ and is freely available at http://www.stat.lsa.umich.edu/∼shojaie/ Contact: shojaie@umich.edu PMID:20823316

  20. Neural Connectivity in Epilepsy as Measured by Granger Causality

    PubMed Central

    Coben, Robert; Mohammad-Rezazadeh, Iman

    2015-01-01

    Epilepsy is a chronic neurological disorder characterized by repeated seizures or excessive electrical discharges in a group of brain cells. Prevalence rates include about 50 million people worldwide and 10% of all people have at least one seizure at one time in their lives. Connectivity models of epilepsy serve to provide a deeper understanding of the processes that control and regulate seizure activity. These models have received initial support and have included measures of EEG, MEG, and MRI connectivity. Preliminary findings have shown regions of increased connectivity in the immediate regions surrounding the seizure foci and associated low connectivity in nearby regions and pathways. There is also early evidence to suggest that these patterns change during ictal events and that these changes may even by related to the occurrence or triggering of seizure events. We present data showing how Granger causality can be used with EEG data to measure connectivity across brain regions involved in ictal events and their resolution. We have provided two case examples as a demonstration of how to obtain and interpret such data. EEG data of ictal events are processed, converted to independent components and their dipole localizations, and these are used to measure causality and connectivity between these locations. Both examples have shown hypercoupling near the seizure foci and low causality across nearby and associated neuronal pathways. This technique also allows us to track how these measures change over time and during the ictal and post-ictal periods. Areas for further research into this technique, its application to epilepsy, and the formation of more effective therapeutic interventions are recommended. PMID:26236211

  1. A study of industrial electricity consumption based on partial Granger causality network

    NASA Astrophysics Data System (ADS)

    Yao, Can-Zhong; Lin, Qing-Wen; Lin, Ji-Nan

    2016-11-01

    The paper studies the industrial energy transferring paths among the industries of China by distinguishing direct causality from the indirect. With complementary graphs, we propose that industrial causal relationship can be heterogeneous, and provide insights for refining robust industrial causality framework. First, by analyzing the in-weight and out-weight of the industries in Granger causality networks we find that public utilities have significant causality with other industries, and the industries with higher degree value tend to have stronger causality with others. Further, we eliminate the exogenous links by partial Granger causality model and find both Granger and partial Granger networks have consistent hub industries while some outliers emerge in partial Granger causality networks. Besides, compared with GX, GZ, HN and YN, the correlation between the volume of electricity consumption and the weight of each industry is more significant in the networks of GD and NF. By studying the characteristics of complementary graphs, we show that the industrial energy transferring paths in GD are more multidimensional, and the corresponding interdependent relationship among industries is more robust. Finally, using bootstrap method we verify the reliability of each industrial relationship network. Results exhibit that GD, GX and NF have more reliable causal relationship networks than other provinces, revealing their industrial structure to be more stable.

  2. Exploring Granger causality between global average observed time series of carbon dioxide and temperature

    SciTech Connect

    Kodra, Evan A; Chatterjee, Snigdhansu; Ganguly, Auroop R

    2010-01-01

    Detection and attribution methodologies have been developed over the years to delineate anthropogenic from natural drivers of climate change and impacts. A majority of prior attribution studies, which have used climate model simulations and observations or reanalysis datasets, have found evidence for humaninduced climate change. This papers tests the hypothesis that Granger causality can be extracted from the bivariate series of globally averaged land surface temperature (GT) observations and observed CO2 in the atmosphere using a reverse cumulative Granger causality test. This proposed extension of the classic Granger causality test is better suited to handle the multisource nature of the data and provides further statistical rigor. The results from this modified test show evidence for Granger causality from a proxy of total radiative forcing (RC), which in this case is a transformation of atmospheric CO2, to GT. Prior literature failed to extract these results via the standard Granger causality test. A forecasting test shows that a holdout set of GT can be better predicted with the addition of lagged RC as a predictor, lending further credibility to the Granger test results. However, since second-order-differenced RC is neither normally distributed nor variance stationary, caution should be exercised in the interpretation of our results.

  3. Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery.

    PubMed

    Hu, Sanqing; Wang, Hui; Zhang, Jianhai; Kong, Wanzeng; Cao, Yu; Kozma, Robert

    2016-07-01

    In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown to be more sensitive to reveal true causality than GC. We then apply GC and NC to motor imagery (MI) which is an important mental process in cognitive neuroscience and psychology and has received growing attention for a long time. We study causality flow during MI using scalp electroencephalograms from nine subjects in Brain-computer interface competition IV held in 2008. We are interested in three regions: Cz (central area of the cerebral cortex), C3 (left area of the cerebral cortex), and C4 (right area of the cerebral cortex) which are considered to be optimal locations for recognizing MI states in the literature. Our results show that: 1) there is strong directional connectivity from Cz to C3/C4 during left- and right-hand MIs based on GC and NC; 2) during left-hand MI, there is directional connectivity from C4 to C3 based on GC and NC; 3) during right-hand MI, there is strong directional connectivity from C3 to C4 which is much clearly revealed by NC than by GC, i.e., NC largely improves the classification rate; and 4) NC is demonstrated to be much more sensitive to reveal causal influence between different brain regions than GC.

  4. A study of causality structure and dynamics in industrial electricity consumption based on Granger network

    NASA Astrophysics Data System (ADS)

    Yao, Can-Zhong; Lin, Ji-Nan; Lin, Qing-Wen; Zheng, Xu-Zhou; Liu, Xiao-Feng

    2016-11-01

    Based on industrial electricity consumption, we model industrial networks by Granger causality method and MST (minimum spanning tree), and then further stick onto an industrial coupling mechanism from energy-consumption perspective. First, we construct Granger causality networks of five provinces in South China of GD, GX, GZ, HN and YN based on their industrial electricity consumption data, and we demonstrate from a network-topology perspective: the distribution of weight of links of all industrial electricity-consumption Granger causality networks approximately follows power-law distribution, revealing a phenomenon that few industries may bring a tremendous influence on the rest. Moreover, correlation analysis between weight and degree of a node shows that in most Granger causality networks, both span and strength of influence of a given industry will significantly increase. Further, we analyze the relationship between the thresholds of Granger causality significance and density of corresponding networks. Results show GD and HN could be classified into a group with relatively greater global differentiation in industries and unbalanced industrial development, however, GX, GZ and YN are grouped as second cluster with relatively balanced industrial development. Furthermore, using Chu-Liu-EdmondsMST algorithm, we extract graphs of MSTs or maximal cliques from industrial electricity-consumption Granger causality networks, and research on energy transmission structure, feedback loop, and bootstrap reliability. By analyzing MSTs, we find that only GD, GX and YN can be extracted with MST graphs, and capture the probable transmission routes of key nodes. Besides we illustrate all three MST graphs are involved with feedback loops structures with various characteristics: GX has complete feed-forward section, feed-back section and feedback loop section; YN has only feed-forward section and feedback loop section; GD has multiple feedback loops section. Finally, we conduct

  5. Attribution of precipitation changes on ground-air temperature offset: Granger causality analysis

    NASA Astrophysics Data System (ADS)

    Cermak, Vladimir; Bodri, Louise

    2016-06-01

    This work examines the causal relationship between the value of the ground-air temperature offset and the precipitation changes for monitored 5-min data series together with their hourly and daily averages obtained at the Sporilov Geophysical Observatory (Prague). Shallow subsurface soil temperatures were monitored under four different land cover types (bare soil, sand, short-cut grass and asphalt). The ground surface temperature (GST) and surface air temperature (SAT) offset, ΔT(GST-SAT), is defined as the difference between the temperature measured at the depth of 2 cm below the surface and the air temperature measured at 5 cm above the surface. The results of the Granger causality test did not reveal any evidence of Granger causality for precipitation to ground-air temperature offsets on the daily scale of aggregation except for the asphalt pavement. On the contrary, a strong evidence of Granger causality for precipitation to the ground-air temperature offsets was found on the hourly scale of aggregation for all land cover types except for the sand surface cover. All results are sensitive to the lag choice of the autoregressive model. On the whole, obtained results contain valuable information on the delay time of ΔT(GST-SAT) caused by the rainfall events and confirmed the importance of using autoregressive models to understand the ground-air temperature relationship.

  6. Testing for Granger Causality in the Frequency Domain: A Phase Resampling Method.

    PubMed

    Liu, Siwei; Molenaar, Peter

    2016-01-01

    This article introduces phase resampling, an existing but rarely used surrogate data method for making statistical inferences of Granger causality in frequency domain time series analysis. Granger causality testing is essential for establishing causal relations among variables in multivariate dynamic processes. However, testing for Granger causality in the frequency domain is challenging due to the nonlinear relation between frequency domain measures (e.g., partial directed coherence, generalized partial directed coherence) and time domain data. Through a simulation study, we demonstrate that phase resampling is a general and robust method for making statistical inferences even with short time series. With Gaussian data, phase resampling yields satisfactory type I and type II error rates in all but one condition we examine: when a small effect size is combined with an insufficient number of data points. Violations of normality lead to slightly higher error rates but are mostly within acceptable ranges. We illustrate the utility of phase resampling with two empirical examples involving multivariate electroencephalography (EEG) and skin conductance data.

  7. The Global Drivers of Photosynthesis and Light Use Efficiency Seasonality: A Granger Frequency Causality Analysis

    NASA Technical Reports Server (NTRS)

    Nemani, Ramakrishna R.

    2016-01-01

    Photosynthesis and light use efficiency (LUE) are major factors in the evolution of the continental carbon cycle due to their contribution to gross primary production (GPP). However, while the drivers of photosynthesis and LUE on a plant or canopy scale can often be identified, significant uncertainties exist when modeling these on a global scale. This is due to sparse observations in regions such as the tropics and the lack of a direct global observation dataset. Although others have attempted to address this issue using correlations (Beer, 2010) or calculating GPP from vegetation indices (Running, 2004), in this study we take a new approach. We combine the statistical method of Granger frequency causality and partial Granger frequency causality with remote sensing data products (including sun-induced fluorescence used as a proxy for GPP) to determine the main environmental drivers of GPP across the globe.

  8. Granger causal time-dependent source connectivity in the somatosensory network

    NASA Astrophysics Data System (ADS)

    Gao, Lin; Sommerlade, Linda; Coffman, Brian; Zhang, Tongsheng; Stephen, Julia M.; Li, Dichen; Wang, Jue; Grebogi, Celso; Schelter, Bjoern

    2015-05-01

    Exploration of transient Granger causal interactions in neural sources of electrophysiological activities provides deeper insights into brain information processing mechanisms. However, the underlying neural patterns are confounded by time-dependent dynamics, non-stationarity and observational noise contamination. Here we investigate transient Granger causal interactions using source time-series of somatosensory evoked magnetoencephalographic (MEG) elicited by air puff stimulation of right index finger and recorded using 306-channel MEG from 21 healthy subjects. A new time-varying connectivity approach, combining renormalised partial directed coherence with state space modelling, is employed to estimate fast changing information flow among the sources. Source analysis confirmed that somatosensory evoked MEG was mainly generated from the contralateral primary somatosensory cortex (SI) and bilateral secondary somatosensory cortices (SII). Transient Granger causality shows a serial processing of somatosensory information, 1) from contralateral SI to contralateral SII, 2) from contralateral SI to ipsilateral SII, 3) from contralateral SII to contralateral SI, and 4) from contralateral SII to ipsilateral SII. These results are consistent with established anatomical connectivity between somatosensory regions and previous source modeling results, thereby providing empirical validation of the time-varying connectivity analysis. We argue that the suggested approach provides novel information regarding transient cortical dynamic connectivity, which previous approaches could not assess.

  9. Granger causal time-dependent source connectivity in the somatosensory network.

    PubMed

    Gao, Lin; Sommerlade, Linda; Coffman, Brian; Zhang, Tongsheng; Stephen, Julia M; Li, Dichen; Wang, Jue; Grebogi, Celso; Schelter, Bjoern

    2015-05-21

    Exploration of transient Granger causal interactions in neural sources of electrophysiological activities provides deeper insights into brain information processing mechanisms. However, the underlying neural patterns are confounded by time-dependent dynamics, non-stationarity and observational noise contamination. Here we investigate transient Granger causal interactions using source time-series of somatosensory evoked magnetoencephalographic (MEG) elicited by air puff stimulation of right index finger and recorded using 306-channel MEG from 21 healthy subjects. A new time-varying connectivity approach, combining renormalised partial directed coherence with state space modelling, is employed to estimate fast changing information flow among the sources. Source analysis confirmed that somatosensory evoked MEG was mainly generated from the contralateral primary somatosensory cortex (SI) and bilateral secondary somatosensory cortices (SII). Transient Granger causality shows a serial processing of somatosensory information, 1) from contralateral SI to contralateral SII, 2) from contralateral SI to ipsilateral SII, 3) from contralateral SII to contralateral SI, and 4) from contralateral SII to ipsilateral SII. These results are consistent with established anatomical connectivity between somatosensory regions and previous source modeling results, thereby providing empirical validation of the time-varying connectivity analysis. We argue that the suggested approach provides novel information regarding transient cortical dynamic connectivity, which previous approaches could not assess.

  10. A Novel Extended Granger Causal Model Approach Demonstrates Brain Hemispheric Differences during Face Recognition Learning

    PubMed Central

    Ge, Tian; Kendrick, Keith M.; Feng, Jianfeng

    2009-01-01

    Two main approaches in exploring causal relationships in biological systems using time-series data are the application of Dynamic Causal model (DCM) and Granger Causal model (GCM). These have been extensively applied to brain imaging data and are also readily applicable to a wide range of temporal changes involving genes, proteins or metabolic pathways. However, these two approaches have always been considered to be radically different from each other and therefore used independently. Here we present a novel approach which is an extension of Granger Causal model and also shares the features of the bilinear approximation of Dynamic Causal model. We have first tested the efficacy of the extended GCM by applying it extensively in toy models in both time and frequency domains and then applied it to local field potential recording data collected from in vivo multi-electrode array experiments. We demonstrate face discrimination learning-induced changes in inter- and intra-hemispheric connectivity and in the hemispheric predominance of theta and gamma frequency oscillations in sheep inferotemporal cortex. The results provide the first evidence for connectivity changes between and within left and right inferotemporal cortexes as a result of face recognition learning. PMID:19936225

  11. A novel extended Granger Causal Model approach demonstrates brain hemispheric differences during face recognition learning.

    PubMed

    Ge, Tian; Kendrick, Keith M; Feng, Jianfeng

    2009-11-01

    Two main approaches in exploring causal relationships in biological systems using time-series data are the application of Dynamic Causal model (DCM) and Granger Causal model (GCM). These have been extensively applied to brain imaging data and are also readily applicable to a wide range of temporal changes involving genes, proteins or metabolic pathways. However, these two approaches have always been considered to be radically different from each other and therefore used independently. Here we present a novel approach which is an extension of Granger Causal model and also shares the features of the bilinear approximation of Dynamic Causal model. We have first tested the efficacy of the extended GCM by applying it extensively in toy models in both time and frequency domains and then applied it to local field potential recording data collected from in vivo multi-electrode array experiments. We demonstrate face discrimination learning-induced changes in inter- and intra-hemispheric connectivity and in the hemispheric predominance of theta and gamma frequency oscillations in sheep inferotemporal cortex. The results provide the first evidence for connectivity changes between and within left and right inferotemporal cortexes as a result of face recognition learning. PMID:19936225

  12. Determination of ECoG information flow activity based on Granger causality and Hilbert transformation.

    PubMed

    Demirer, R Murat; Özerdem, Mehmet Siraç; Bayrak, Coskun; Mendi, Engin

    2013-12-01

    Analysis of directional information flow patterns among different regions of the brain is important for investigating the relation between ECoG (electrocorticographic) and mental activity. The objective is to study and evaluate the information flow activity at different frequencies in the primary motor cortex. We employed Granger causality for capturing the future state of the propagation path and direction between recording electrode sites on the cerebral cortex. A grid covered the right motor cortex completely due to its size (approx. 8 cm×8 cm) but grid area extends to the surrounding cortex areas. During the experiment, a subject was asked to imagine performing two activities: movement of the left small finger and/or movement of the tongue. The time series of the electrical brain activity was recorded during these trials using an 8×8 (0.016-300 Hz band with) ECoG platinum electrode grid, which was placed on the contralateral (right) motor cortex. For detection of information flow activity and communication frequencies among the electrodes, we have proposed a method based on following steps: (i) calculation of analytical time series such as amplitude and phase difference acquired from Hilbert transformation, (ii) selection of frequency having highest interdependence for the electrode pairs for the concerned time series over a sliding window in which we assumed time series were stationary, (iii) calculation of Granger causality values for each pair with selected frequency. The information flow (causal influence) activity and communication frequencies between the electrodes in grid were determined and shown successfully. It is supposed that information flow activity and communication frequencies between the electrodes in the grid are approximately the same for the same pattern. The successful employment of Granger causality and Hilbert transformation for the detection of the propagation path and direction of each component of ECoG among different sub

  13. Granger Causality in Multivariate Time Series Using a Time-Ordered Restricted Vector Autoregressive Model

    NASA Astrophysics Data System (ADS)

    Siggiridou, Elsa; Kugiumtzis, Dimitris

    2016-04-01

    Granger causality has been used for the investigation of the inter-dependence structure of the underlying systems of multi-variate time series. In particular, the direct causal effects are commonly estimated by the conditional Granger causality index (CGCI). In the presence of many observed variables and relatively short time series, CGCI may fail because it is based on vector autoregressive models (VAR) involving a large number of coefficients to be estimated. In this work, the VAR is restricted by a scheme that modifies the recently developed method of backward-in-time selection (BTS) of the lagged variables and the CGCI is combined with BTS. Further, the proposed approach is compared favorably to other restricted VAR representations, such as the top-down strategy, the bottom-up strategy, and the least absolute shrinkage and selection operator (LASSO), in terms of sensitivity and specificity of CGCI. This is shown by using simulations of linear and nonlinear, low and high-dimensional systems and different time series lengths. For nonlinear systems, CGCI from the restricted VAR representations are compared with analogous nonlinear causality indices. Further, CGCI in conjunction with BTS and other restricted VAR representations is applied to multi-channel scalp electroencephalogram (EEG) recordings of epileptic patients containing epileptiform discharges. CGCI on the restricted VAR, and BTS in particular, could track the changes in brain connectivity before, during and after epileptiform discharges, which was not possible using the full VAR representation.

  14. Accounting for respiration is necessary to reliably infer Granger causality from cardiovascular variability series.

    PubMed

    Porta, Alberto; Bassani, Tito; Bari, Vlasta; Pinna, Gian D; Maestri, Roberto; Guzzetti, Stefano

    2012-03-01

    This study was designed to demonstrate the need of accounting for respiration (R) when causality between heart period (HP) and systolic arterial pressure (SAP) is under scrutiny. Simulations generated according to a bivariate autoregressive closed-loop model were utilized to assess how causality changes as a function of the model parameters. An exogenous (X) signal was added to the bivariate autoregressive closed-loop model to evaluate the bias on causality induced when the X source was disregarded. Causality was assessed in the time domain according to a predictability improvement approach (i.e., Granger causality). HP and SAP variability series were recorded with R in 19 healthy subjects during spontaneous and controlled breathing at 10, 15, and 20 breaths/min. Simulations proved the importance of accounting for X signals. During spontaneous breathing, assessing causality without taking into consideration R leads to a significantly larger percentage of closed-loop interactions and a smaller fraction of unidirectional causality from HP to SAP. This finding was confirmed during paced breathing and it was independent of the breathing rate. These results suggest that the role of baroreflex cannot be correctly assessed without accounting for R.

  15. Prior knowledge driven Granger causality analysis on gene regulatory network discovery

    DOE PAGES

    Yao, Shun; Yoo, Shinjae; Yu, Dantong

    2015-08-28

    Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, themore » propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods. In our research, we noticed a “ 1+1>2” effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be related to the yeast’s responses to different levels of glucose. In conclusion, our research improves causality modeling by combining heterogeneous knowledge, which is well aligned with the future direction in system biology. Furthermore, we proposed a method of Monte Carlo significance estimation (MCSE) to calculate the edge significances which provide statistical meanings to the discovered causality networks. All of our data and source codes will be available under the link https://bitbucket.org/dtyu/granger-causality/wiki/Home.« less

  16. Prior knowledge driven Granger causality analysis on gene regulatory network discovery

    SciTech Connect

    Yao, Shun; Yoo, Shinjae; Yu, Dantong

    2015-08-28

    Our study focuses on discovering gene regulatory networks from time series gene expression data using the Granger causality (GC) model. However, the number of available time points (T) usually is much smaller than the number of target genes (n) in biological datasets. The widely applied pairwise GC model (PGC) and other regularization strategies can lead to a significant number of false identifications when n>>T. In this study, we proposed a new method, viz., CGC-2SPR (CGC using two-step prior Ridge regularization) to resolve the problem by incorporating prior biological knowledge about a target gene data set. In our simulation experiments, the propose new methodology CGC-2SPR showed significant performance improvement in terms of accuracy over other widely used GC modeling (PGC, Ridge and Lasso) and MI-based (MRNET and ARACNE) methods. In addition, we applied CGC-2SPR to a real biological dataset, i.e., the yeast metabolic cycle, and discovered more true positive edges with CGC-2SPR than with the other existing methods. In our research, we noticed a “ 1+1>2” effect when we combined prior knowledge and gene expression data to discover regulatory networks. Based on causality networks, we made a functional prediction that the Abm1 gene (its functions previously were unknown) might be related to the yeast’s responses to different levels of glucose. In conclusion, our research improves causality modeling by combining heterogeneous knowledge, which is well aligned with the future direction in system biology. Furthermore, we proposed a method of Monte Carlo significance estimation (MCSE) to calculate the edge significances which provide statistical meanings to the discovered causality networks. All of our data and source codes will be available under the link https://bitbucket.org/dtyu/granger-causality/wiki/Home.

  17. Analysis of sampling artifacts on the Granger causality analysis for topology extraction of neuronal dynamics

    PubMed Central

    Zhou, Douglas; Zhang, Yaoyu; Xiao, Yanyang; Cai, David

    2014-01-01

    Granger causality (GC) is a powerful method for causal inference for time series. In general, the GC value is computed using discrete time series sampled from continuous-time processes with a certain sampling interval length τ, i.e., the GC value is a function of τ. Using the GC analysis for the topology extraction of the simplest integrate-and-fire neuronal network of two neurons, we discuss behaviors of the GC value as a function of τ, which exhibits (i) oscillations, often vanishing at certain finite sampling interval lengths, (ii) the GC vanishes linearly as one uses finer and finer sampling. We show that these sampling effects can occur in both linear and non-linear dynamics: the GC value may vanish in the presence of true causal influence or become non-zero in the absence of causal influence. Without properly taking this issue into account, GC analysis may produce unreliable conclusions about causal influence when applied to empirical data. These sampling artifacts on the GC value greatly complicate the reliability of causal inference using the GC analysis, in general, and the validity of topology reconstruction for networks, in particular. We use idealized linear models to illustrate possible mechanisms underlying these phenomena and to gain insight into the general spectral structures that give rise to these sampling effects. Finally, we present an approach to circumvent these sampling artifacts to obtain reliable GC values. PMID:25126067

  18. Elastic-Net Copula Granger Causality for Inference of Biological Networks

    PubMed Central

    Siyal, Mohammad Yakoob

    2016-01-01

    Aim In bioinformatics, the inference of biological networks is one of the most active research areas. It involves decoding various complex biological networks that are responsible for performing diverse functions in human body. Among these networks analysis, most of the research focus is towards understanding effective brain connectivity and gene networks in order to cure and prevent related diseases like Alzheimer and cancer respectively. However, with recent advances in data procurement technology, such as DNA microarray analysis and fMRI that can simultaneously process a large amount of data, it yields high-dimensional data sets. These high dimensional dataset analyses possess challenges for the analyst. Background Traditional methods of Granger causality inference use ordinary least-squares methods for structure estimation, which confront dimensionality issues when applied to high-dimensional data. Apart from dimensionality issues, most existing methods were designed to capture only the linear inferences from time series data. Method and Conclusion In this paper, we address the issues involved in assessing Granger causality for both linear and nonlinear high-dimensional data by proposing an elegant form of the existing LASSO-based method that we call “Elastic-Net Copula Granger causality”. This method provides a more stable way to infer biological networks which has been verified using rigorous experimentation. We have compared the proposed method with the existing method and demonstrated that this new strategy outperforms the existing method on all measures: precision, false detection rate, recall, and F1 score. We have also applied both methods to real HeLa cell data and StarPlus fMRI datasets and presented a comparison of the effectiveness of both methods. PMID:27792750

  19. Effective connectivity of facial expression network by using Granger causality analysis

    NASA Astrophysics Data System (ADS)

    Zhang, Hui; Li, Xiaoting

    2013-10-01

    Functional magnetic resonance imaging (fMRI) is an advanced non-invasive data acquisition technique to investigate the neural activity in human brain. In addition to localize the functional brain regions that is activated by specific cognitive task, fMRI can also be utilized to measure the task-related functional interactions among the active regions of interest (ROI) in the brain. Among the variety of analysis tools proposed for modeling the connectivity of brain regions, Granger causality analysis (GCA) measure the directions of information interactions by looking for the lagged effect among the brain regions. In this study, we use fMRI and Granger Causality analysis to investigate the effective connectivity of brain network induced by viewing several kinds of expressional faces. We focus on four kinds of facial expression stimuli: fearful, angry, happy and neutral faces. Five face selective regions of interest are localized and the effective connectivity within these regions is measured for the expressional faces. Our result based on 8 subjects showed that there is significant effective connectivity from STS to amygdala, from amygdala to OFA, aFFA and pFFA, from STS to aFFA and from pFFA to aFFA. This result suggested that there is an information flow from the STS to the amygdala when perusing expressional faces. This emotional expressional information flow that is conveyed by STS and amygdala, flow back to the face selective regions in occipital-temporal lobes, which constructed a emotional face processing network.

  20. The Global Drivers of Photosynthesis and Light Use Efficiency Seasonality: A Granger Frequency Causality Analysis

    NASA Astrophysics Data System (ADS)

    Green, J.; Lee, J. E.; Gentine, P.; Berry, J. A.; Konings, A. G.

    2015-12-01

    hotosynthesis and light use efficiency (LUE) are major factors in the evolution of the continental carbon cycle due to their contribution to gross primary production (GPP). However, while the drivers of photosynthesis and LUE on a plant or canopy scale can often be identified, significant uncertainties exist when modeling these on a global scale. This is due to sparse observations in regions such as the tropics and the lack of a direct global observation dataset. Although others have attempted to address this issue using correlations (Beer, 2010) or calculating GPP from vegetation indices (Running, 2004), in this study we take a new approach. We combine the statistical method of Granger frequency causality and partial Granger frequency causality with remote sensing data products (including sun-induced fluorescence used as a proxy for GPP) to determine the main environmental drivers of GPP across the globe. References:Beer, C., M. Reichstein, E. Tomelleri, P. Ciais, M. Jung, N. Carvalhais, C. Ro¨denbeck, M. Altaf Arain, D. Baldocchi, G. B. Bonan, A. Bondeau, A. Cescatti, G. Lasslop, A. Lindroth, M. Lomas, S. Luyssaert, H. Margolis, K. W. Oleson, O. Roupsard, E. Veenendaal, N. Viovy, C. Williams, I. Woodward, and D. Papale, 2010: Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate. doi: 10.1126/science.1184984. Running, S.W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., Hashimoto, H., 2004. A Continuous Satellite Derived Measure of Global Terrestrial Primary Production. BioScience 54(6), 547-560.

  1. The Global Drivers of Photosynthesis and Light Use Efficiency Seasonality: A Granger Frequency Causality Analysis

    NASA Astrophysics Data System (ADS)

    Green, J.; Lee, J. E.; Gentine, P.; Berry, J. A.; Konings, A. G.

    2015-12-01

    ABSTRACTPhotosynthesis and light use efficiency (LUE) are major factors in the evolution of the continental carbon cycle due to their contribution to gross primary production (GPP). However, while the drivers of photosynthesis and LUE on a plant or canopy scale can often be identified, significant uncertainties exist when modeling these on a global scale. This is due to sparse observations in regions such as the tropics and the lack of a direct global observation dataset. Although others have attempted to address this issue using correlations (Beer, 2010) or calculating GPP from vegetation indices (Running, 2004), in this study we take a new approach. We combine the statistical method of Granger frequency causality and partial Granger frequency causality with remote sensing data products (including sun-induced fluorescence used as a proxy for GPP) to determine the main environmental drivers of GPP across the globe. References:Beer, C., M. Reichstein, E. Tomelleri, P. Ciais, M. Jung, N. Carvalhais, C. Ro¨denbeck, M. Altaf Arain, D. Baldocchi, G. B. Bonan, A. Bondeau, A. Cescatti, G. Lasslop, A. Lindroth, M. Lomas, S. Luyssaert, H. Margolis, K. W. Oleson, O. Roupsard, E. Veenendaal, N. Viovy, C. Williams, I. Woodward, and D. Papale, 2010: Terrestrial Gross Carbon Dioxide Uptake: Global Distribution and Covariation with Climate. doi: 10.1126/science.1184984. Running, S.W., Nemani, R. R., Heinsch, F. A., Zhao, M., Reeves, M., Hashimoto, H., 2004. A Continuous Satellite Derived Measure of Global Terrestrial Primary Production. BioScience 54(6), 547-560.

  2. Neural networks with non-uniform embedding and explicit validation phase to assess Granger causality.

    PubMed

    Montalto, Alessandro; Stramaglia, Sebastiano; Faes, Luca; Tessitore, Giovanni; Prevete, Roberto; Marinazzo, Daniele

    2015-11-01

    A challenging problem when studying a dynamical system is to find the interdependencies among its individual components. Several algorithms have been proposed to detect directed dynamical influences between time series. Two of the most used approaches are a model-free one (transfer entropy) and a model-based one (Granger causality). Several pitfalls are related to the presence or absence of assumptions in modeling the relevant features of the data. We tried to overcome those pitfalls using a neural network approach in which a model is built without any a priori assumptions. In this sense this method can be seen as a bridge between model-free and model-based approaches. The experiments performed will show that the method presented in this work can detect the correct dynamical information flows occurring in a system of time series. Additionally we adopt a non-uniform embedding framework according to which only the past states that actually help the prediction are entered into the model, improving the prediction and avoiding the risk of overfitting. This method also leads to a further improvement with respect to traditional Granger causality approaches when redundant variables (i.e. variables sharing the same information about the future of the system) are involved. Neural networks are also able to recognize dynamics in data sets completely different from the ones used during the training phase.

  3. Investigating effective brain connectivity from fMRI data: past findings and current issues with reference to Granger causality analysis.

    PubMed

    Deshpande, Gopikrishna; Hu, Xiaoping

    2012-01-01

    Interactions between brain regions have been recognized as a critical ingredient required to understand brain function. Two modes of interactions have held prominence-synchronization and causal influence. Efforts to ascertain causal influence from functional magnetic resonance imaging (fMRI) data have relied primarily on confirmatory model-driven approaches, such as dynamic causal modeling and structural equation modeling, and exploratory data-driven approaches such as Granger causality analysis. A slew of recent articles have focused on the relative merits and caveats of these approaches. The relevant studies can be classified into simulations, theoretical developments, and experimental results. In the first part of this review, we will consider each of these themes and critically evaluate their arguments, with regard to Granger causality analysis. Specifically, we argue that simulations are bounded by the assumptions and simplifications made by the simulator, and hence must be regarded only as a guide to experimental design and should not be viewed as the final word. On the theoretical front, we reason that each of the improvements to existing, yet disparate, methods brings them closer to each other with the hope of eventually leading to a unified framework specifically designed for fMRI. We then review latest experimental results that demonstrate the utility and validity of Granger causality analysis under certain experimental conditions. In the second part, we will consider current issues in causal connectivity analysis-hemodynamic variability, sampling, instantaneous versus causal relationship, and task versus resting states. We highlight some of our own work regarding these issues showing the effect of hemodynamic variability and sampling on Granger causality. Further, we discuss recent techniques such as the cubature Kalman filtering, which can perform blind deconvolution of the hemodynamic response robustly well, and hence enabling wider application of

  4. Investigating Effective Brain Connectivity from fMRI Data: Past Findings and Current Issues with Reference to Granger Causality Analysis

    PubMed Central

    2012-01-01

    Abstract Interactions between brain regions have been recognized as a critical ingredient required to understand brain function. Two modes of interactions have held prominence—synchronization and causal influence. Efforts to ascertain causal influence from functional magnetic resonance imaging (fMRI) data have relied primarily on confirmatory model-driven approaches, such as dynamic causal modeling and structural equation modeling, and exploratory data-driven approaches such as Granger causality analysis. A slew of recent articles have focused on the relative merits and caveats of these approaches. The relevant studies can be classified into simulations, theoretical developments, and experimental results. In the first part of this review, we will consider each of these themes and critically evaluate their arguments, with regard to Granger causality analysis. Specifically, we argue that simulations are bounded by the assumptions and simplifications made by the simulator, and hence must be regarded only as a guide to experimental design and should not be viewed as the final word. On the theoretical front, we reason that each of the improvements to existing, yet disparate, methods brings them closer to each other with the hope of eventually leading to a unified framework specifically designed for fMRI. We then review latest experimental results that demonstrate the utility and validity of Granger causality analysis under certain experimental conditions. In the second part, we will consider current issues in causal connectivity analysis—hemodynamic variability, sampling, instantaneous versus causal relationship, and task versus resting states. We highlight some of our own work regarding these issues showing the effect of hemodynamic variability and sampling on Granger causality. Further, we discuss recent techniques such as the cubature Kalman filtering, which can perform blind deconvolution of the hemodynamic response robustly well, and hence enabling wider

  5. Large-scale Granger causality analysis on resting-state functional MRI

    NASA Astrophysics Data System (ADS)

    D'Souza, Adora M.; Abidin, Anas Zainul; Leistritz, Lutz; Wismüller, Axel

    2016-03-01

    We demonstrate an approach to measure the information flow between each pair of time series in resting-state functional MRI (fMRI) data of the human brain and subsequently recover its underlying network structure. By integrating dimensionality reduction into predictive time series modeling, large-scale Granger Causality (lsGC) analysis method can reveal directed information flow suggestive of causal influence at an individual voxel level, unlike other multivariate approaches. This method quantifies the influence each voxel time series has on every other voxel time series in a multivariate sense and hence contains information about the underlying dynamics of the whole system, which can be used to reveal functionally connected networks within the brain. To identify such networks, we perform non-metric network clustering, such as accomplished by the Louvain method. We demonstrate the effectiveness of our approach to recover the motor and visual cortex from resting state human brain fMRI data and compare it with the network recovered from a visuomotor stimulation experiment, where the similarity is measured by the Dice Coefficient (DC). The best DC obtained was 0.59 implying a strong agreement between the two networks. In addition, we thoroughly study the effect of dimensionality reduction in lsGC analysis on network recovery. We conclude that our approach is capable of detecting causal influence between time series in a multivariate sense, which can be used to segment functionally connected networks in the resting-state fMRI.

  6. Granger causality-based synaptic weights estimation for analyzing neuronal networks.

    PubMed

    Shao, Pei-Chiang; Huang, Jian-Jia; Shann, Wei-Chang; Yen, Chen-Tung; Tsai, Meng-Li; Yen, Chien-Chang

    2015-06-01

    Granger causality (GC) analysis has emerged as a powerful analytical method for estimating the causal relationship among various types of neural activity data. However, two problems remain not very clear and further researches are needed: (1) The GC measure is designed to be nonnegative in its original form, lacking of the trait for differentiating the effects of excitations and inhibitions between neurons. (2) How is the estimated causality related to the underlying synaptic weights? Based on the GC, we propose a computational algorithm under a best linear predictor assumption for analyzing neuronal networks by estimating the synaptic weights among them. Under this assumption, the GC analysis can be extended to measure both excitatory and inhibitory effects between neurons. The method was examined by three sorts of simulated networks: those with linear, almost linear, and nonlinear network structures. The method was also illustrated to analyze real spike train data from the anterior cingulate cortex (ACC) and the striatum (STR). The results showed, under the quinpirole administration, the significant existence of excitatory effects inside the ACC, excitatory effects from the ACC to the STR, and inhibitory effects inside the STR.

  7. Development of Effective Connectivity during Own- and Other-Race Face Processing: A Granger Causality Analysis

    PubMed Central

    Zhou, Guifei; Liu, Jiangang; Ding, Xiao Pan; Fu, Genyue; Lee, Kang

    2016-01-01

    Numerous developmental studies have suggested that other-race effect (ORE) in face recognition emerges as early as in infancy and develops steadily throughout childhood. However, there is very limited research on the neural mechanisms underlying this developmental ORE. The present study used Granger causality analysis (GCA) to examine the development of children's cortical networks in processing own- and other-race faces. Children were between 3 and 13 years. An old-new paradigm was used to assess their own- and other-race face recognition with ETG-4000 (Hitachi Medical Co., Japan) acquiring functional near infrared spectroscopy (fNIRS) data. After preprocessing, for each participant and under each face condition, we obtained the causal map by calculating the weights of causal relations between the time courses of [oxy-Hb] of each pair of channels using GCA. To investigate further the differential causal connectivity for own-race faces and other-race faces at the group level, a repeated measure analysis of variance (ANOVA) was performed on the GCA weights for each pair of channels with the face race task (own-race face vs. other-race face) as the within-subject variable and the age as a between-subject factor (continuous variable). We found an age-related increase in functional connectivity, paralleling a similar age-related improvement in behavioral face processing ability. More importantly, we found that the significant differences in neural functional connectivity between the recognition of own-race faces and that of other-race faces were modulated by age. Thus, like the behavioral ORE, the neural ORE emerges early and undergoes a protracted developmental course. PMID:27713696

  8. Correntropy-based partial directed coherence for testing multivariate Granger causality in nonlinear processes

    NASA Astrophysics Data System (ADS)

    Kannan, Rohit; Tangirala, Arun K.

    2014-06-01

    Identification of directional influences in multivariate systems is of prime importance in several applications of engineering and sciences such as plant topology reconstruction, fault detection and diagnosis, and neurosciences. A spectrum of related directionality measures, ranging from linear measures such as partial directed coherence (PDC) to nonlinear measures such as transfer entropy, have emerged over the past two decades. The PDC-based technique is simple and effective, but being a linear directionality measure has limited applicability. On the other hand, transfer entropy, despite being a robust nonlinear measure, is computationally intensive and practically implementable only for bivariate processes. The objective of this work is to develop a nonlinear directionality measure, termed as KPDC, that possesses the simplicity of PDC but is still applicable to nonlinear processes. The technique is founded on a nonlinear measure called correntropy, a recently proposed generalized correlation measure. The proposed method is equivalent to constructing PDC in a kernel space where the PDC is estimated using a vector autoregressive model built on correntropy. A consistent estimator of the KPDC is developed and important theoretical results are established. A permutation scheme combined with the sequential Bonferroni procedure is proposed for testing hypothesis on absence of causality. It is demonstrated through several case studies that the proposed methodology effectively detects Granger causality in nonlinear processes.

  9. Effective connectivity of neural pathways underlying disgust by multivariate Granger causality analysis

    NASA Astrophysics Data System (ADS)

    Yan, Hao; Wang, Yonghui; Tian, Jie; Liu, Yijun

    2011-03-01

    The disgust system arises phylogenetically in response to dangers to the internal milieu from pathogens and their toxic products. Functional imaging studies have demonstrated that a much wider range of neural structures was involved in triggering disgust reactions. However, less is known regarding how and what neural pathways these neural structures interact. To address this issue, we adopted an effective connectivity based analysis, namely the multivariate Granger causality approach, to explore the causal interactions within these brain networks. Results presented that disgust can induce a wide range of brain activities, such as the insula, the anterior cingulate cortex, the parahippocampus lobe, the dorsal lateral prefrontal cortex, the superior occipital gyrus, and the supplementary motor cortex. These brain areas constitute as a whole, with much denser connectivity following disgust stimuli, in comparison with that of the neutral condition. Moreover, the anterior insula, showing multiple casual interactions with limbic and subcortical areas, was implicated as a central hub in organizing multiple information processing in the disgust system.

  10. Detecting directional influence in fMRI connectivity analysis using PCA based Granger causality

    PubMed Central

    Zhou, Zhenyu; Ding, Mingzhou; Chen, Yonghong; Wright, Paul; Lu, Zuhong; Liu, Yijun

    2009-01-01

    A fMRI connectivity analysis approach combining both principal component analysis (PCA) and Granger causality method (GCM) is proposed to study directional influence between functional brain regions. Both simulated data and human fMRI data obtained during behavioral tasks were used to validate this method. If PCA is first used to reduce number of fMRI time series, then more energy and information features in the signal can be preserved than using averaged values from brain regions of interest. Subsequently, GCM can be applied to principal components extracted in order to further investigate effective connectivity. The simulation demonstrated that by using GCM with PCA, between-region causalities were better represented than using GCM with average values. Furthermore, after localizing an emotion task-induced activation in the anterior cingulate cortex, inferior frontal sulcus and amygdala, the directional influences among these brain regions were resolved using our new approach. These results indicate that using PCA may improve upon application of existing GCMs in study of human brain effective connectivity. PMID:19595679

  11. Conditional Granger Causality Analysis of Effective Connectivity during Motor Imagery and Motor Execution in Stroke Patients.

    PubMed

    Wang, Li; Zhang, Jingna; Zhang, Ye; Yan, Rubing; Liu, Hongliang; Qiu, Mingguo

    2016-01-01

    Aims. Motor imagery has emerged as a promising technique for the improvement of motor function following stroke, but the mechanism of functional network reorganization in patients during this process remains unclear. The aim of this study is to evaluate the cortical motor network patterns of effective connectivity in stroke patients. Methods. Ten stroke patients with right hand hemiplegia and ten normal control subjects were recruited. We applied conditional Granger causality analysis (CGCA) to explore and compare the functional connectivity between motor execution and motor imagery. Results. Compared with the normal controls, the patient group showed lower effective connectivity to the primary motor cortex (M1), the premotor cortex (PMC), and the supplementary motor area (SMA) in the damaged hemisphere but stronger effective connectivity to the ipsilesional PMC and M1 in the intact hemisphere during motor execution. There were tighter connections in the cortical motor network in the patients than in the controls during motor imagery, and the patients showed more effective connectivity in the intact hemisphere. Conclusions. The increase in effective connectivity suggests that motor imagery enhances core corticocortical interactions, promotes internal interaction in damaged hemispheres in stroke patients, and may facilitate recovery of motor function. PMID:27200373

  12. State-Space Analysis of Granger-Geweke Causality Measures with Application to fMRI.

    PubMed

    Solo, Victor

    2016-05-01

    The recent interest in the dynamics of networks and the advent, across a range of applications, of measuring modalities that operate on different temporal scales have put the spotlight on some significant gaps in the theory of multivariate time series. Fundamental to the description of network dynamics is the direction of interaction between nodes, accompanied by a measure of the strength of such interactions. Granger causality and its associated frequency domain strength measures (GEMs) (due to Geweke) provide a framework for the formulation and analysis of these issues. In pursuing this setup, three significant unresolved issues emerge. First, computing GEMs involves computing submodels of vector time series models, for which reliable methods do not exist. Second, the impact of filtering on GEMs has never been definitively established. Third, the impact of downsampling on GEMs has never been established. In this work, using state-space methods, we resolve all these issues and illustrate the results with some simulations. Our analysis is motivated by some problems in (fMRI) brain imaging, to which we apply it, but it is of general applicability.

  13. Conditional Granger Causality Analysis of Effective Connectivity during Motor Imagery and Motor Execution in Stroke Patients

    PubMed Central

    Wang, Li; Zhang, Jingna; Zhang, Ye; Yan, Rubing; Liu, Hongliang; Qiu, Mingguo

    2016-01-01

    Aims. Motor imagery has emerged as a promising technique for the improvement of motor function following stroke, but the mechanism of functional network reorganization in patients during this process remains unclear. The aim of this study is to evaluate the cortical motor network patterns of effective connectivity in stroke patients. Methods. Ten stroke patients with right hand hemiplegia and ten normal control subjects were recruited. We applied conditional Granger causality analysis (CGCA) to explore and compare the functional connectivity between motor execution and motor imagery. Results. Compared with the normal controls, the patient group showed lower effective connectivity to the primary motor cortex (M1), the premotor cortex (PMC), and the supplementary motor area (SMA) in the damaged hemisphere but stronger effective connectivity to the ipsilesional PMC and M1 in the intact hemisphere during motor execution. There were tighter connections in the cortical motor network in the patients than in the controls during motor imagery, and the patients showed more effective connectivity in the intact hemisphere. Conclusions. The increase in effective connectivity suggests that motor imagery enhances core corticocortical interactions, promotes internal interaction in damaged hemispheres in stroke patients, and may facilitate recovery of motor function. PMID:27200373

  14. Assessing Granger Causality in Electrophysiological Data: Removing the Adverse Effects of Common Signals via Bipolar Derivations.

    PubMed

    Trongnetrpunya, Amy; Nandi, Bijurika; Kang, Daesung; Kocsis, Bernat; Schroeder, Charles E; Ding, Mingzhou

    2015-01-01

    Multielectrode voltage data are usually recorded against a common reference. Such data are frequently used without further treatment to assess patterns of functional connectivity between neuronal populations and between brain areas. It is important to note from the outset that such an approach is valid only when the reference electrode is nearly electrically silent. In practice, however, the reference electrode is generally not electrically silent, thereby adding a common signal to the recorded data. Volume conduction further complicates the problem. In this study we demonstrate the adverse effects of common signals on the estimation of Granger causality, which is a statistical measure used to infer synaptic transmission and information flow in neural circuits from multielectrode data. We further test the hypothesis that the problem can be overcome by utilizing bipolar derivations where the difference between two nearby electrodes is taken and treated as a representation of local neural activity. Simulated data generated by a neuronal network model where the connectivity pattern is known were considered first. This was followed by analyzing data from three experimental preparations where a priori predictions regarding the patterns of causal interactions can be made: (1) laminar recordings from the hippocampus of an anesthetized rat during theta rhythm, (2) laminar recordings from V4 of an awake-behaving macaque monkey during alpha rhythm, and (3) ECoG recordings from electrode arrays implanted in the middle temporal lobe and prefrontal cortex of an epilepsy patient during fixation. For both simulation and experimental analysis the results show that bipolar derivations yield the expected connectivity patterns whereas the untreated data (referred to as unipolar signals) do not. In addition, current source density signals, where applicable, yield results that are close to the expected connectivity patterns, whereas the commonly practiced average re-reference method

  15. Assessing Granger Causality in Electrophysiological Data: Removing the Adverse Effects of Common Signals via Bipolar Derivations

    PubMed Central

    Trongnetrpunya, Amy; Nandi, Bijurika; Kang, Daesung; Kocsis, Bernat; Schroeder, Charles E.; Ding, Mingzhou

    2016-01-01

    Multielectrode voltage data are usually recorded against a common reference. Such data are frequently used without further treatment to assess patterns of functional connectivity between neuronal populations and between brain areas. It is important to note from the outset that such an approach is valid only when the reference electrode is nearly electrically silent. In practice, however, the reference electrode is generally not electrically silent, thereby adding a common signal to the recorded data. Volume conduction further complicates the problem. In this study we demonstrate the adverse effects of common signals on the estimation of Granger causality, which is a statistical measure used to infer synaptic transmission and information flow in neural circuits from multielectrode data. We further test the hypothesis that the problem can be overcome by utilizing bipolar derivations where the difference between two nearby electrodes is taken and treated as a representation of local neural activity. Simulated data generated by a neuronal network model where the connectivity pattern is known were considered first. This was followed by analyzing data from three experimental preparations where a priori predictions regarding the patterns of causal interactions can be made: (1) laminar recordings from the hippocampus of an anesthetized rat during theta rhythm, (2) laminar recordings from V4 of an awake-behaving macaque monkey during alpha rhythm, and (3) ECoG recordings from electrode arrays implanted in the middle temporal lobe and prefrontal cortex of an epilepsy patient during fixation. For both simulation and experimental analysis the results show that bipolar derivations yield the expected connectivity patterns whereas the untreated data (referred to as unipolar signals) do not. In addition, current source density signals, where applicable, yield results that are close to the expected connectivity patterns, whereas the commonly practiced average re-reference method

  16. Upsampling to 400-ms resolution for assessing effective connectivity in functional magnetic resonance imaging data with Granger causality.

    PubMed

    McFarlin, Daniel R; Kerr, Deborah L; Nitschke, Jack B

    2013-01-01

    Granger causality analysis of functional magnetic resonance imaging (fMRI) blood-oxygen-level-dependent signal data allows one to infer the direction and magnitude of influence that brain regions exert on one another. We employed a method for upsampling the time resolution of fMRI data that does not require additional interpolation beyond the interpolation that is regularly used for slice-timing correction. The mathematics for this new method are provided, and simulations demonstrate its viability. Using fMRI, 17 snake phobics and 19 healthy controls viewed snake, disgust, and neutral fish video clips preceded by anticipatory cues. Multivariate Granger causality models at the native 2-sec resolution and at the upsampled 400-ms resolution assessed directional associations of fMRI data among 13 anatomical regions of interest identified in prior research on anxiety and emotion. Superior sensitivity was observed for the 400-ms model, both for connectivity within each group and for group differences in connectivity. Context-dependent analyses for the 400-ms multivariate Granger causality model revealed the specific trial types showing group differences in connectivity. This is the first demonstration of effective connectivity of fMRI data using a method for achieving 400-ms resolution without sacrificing accuracy available at 2-sec resolution. PMID:23134194

  17. Componential Granger causality, and its application to identifying the source and mechanisms of the top-down biased activation that controls attention to affective vs sensory processing.

    PubMed

    Ge, Tian; Feng, Jianfeng; Grabenhorst, Fabian; Rolls, Edmund T

    2012-01-16

    We describe a new measure of Granger causality, componential Granger causality, and show how it can be applied to the identification of the directionality of influences between brain areas with functional neuroimaging data. Componential Granger causality measures the effect of y on x, but allows interaction effects between y and x to be measured. In addition, the terms in componential Granger causality sum to 1, allowing causal effects to be directly compared between systems. We show using componential Granger causality analysis applied to an fMRI investigation that there is a top-down attentional effect from the anterior dorsolateral prefrontal cortex to the orbitofrontal cortex when attention is paid to the pleasantness of a taste, and that this effect depends on the activity in the orbitofrontal cortex as shown by the interaction term. Correspondingly there is a top-down attentional effect from the posterior dorsolateral prefrontal cortex to the insular primary taste cortex when attention is paid to the intensity of a taste, and this effect depends on the activity of the insular primary taste cortex as shown by the interaction term. Componential Granger causality thus not only can reveal the directionality of effects between areas (and these can be bidirectional), but also allows the mechanisms to be understood in terms of whether the causal influence of one system on another depends on the state of the system being causally influenced. Componential Granger causality measures the full effects of second order statistics by including variance and covariance effects between each time series, thus allowing interaction effects to be measured, and also provides a systematic framework within which to measure the effects of cross, self, and noise contributions to causality. The findings reveal some of the mechanisms involved in a biased activation theory of selective attention. PMID:21888980

  18. A conditional Granger causality model approach for group analysis in functional MRI

    PubMed Central

    Zhou, Zhenyu; Wang, Xunheng; Klahr, Nelson J.; Liu, Wei; Arias, Diana; Liu, Hongzhi; von Deneen, Karen M.; Wen, Ying; Lu, Zuhong; Xu, Dongrong; Liu, Yijun

    2011-01-01

    Granger causality model (GCM) derived from multivariate vector autoregressive models of data has been employed for identifying effective connectivity in the human brain with functional MR imaging (fMRI) and to reveal complex temporal and spatial dynamics underlying a variety of cognitive processes. In the most recent fMRI effective connectivity measures, pairwise GCM has commonly been applied based on single voxel values or average values from special brain areas at the group level. Although a few novel conditional GCM methods have been proposed to quantify the connections between brain areas, our study is the first to propose a viable standardized approach for group analysis of an fMRI data with GCM. To compare the effectiveness of our approach with traditional pairwise GCM models, we applied a well-established conditional GCM to pre-selected time series of brain regions resulting from general linear model (GLM) and group spatial kernel independent component analysis (ICA) of an fMRI dataset in the temporal domain. Datasets consisting of one task-related and one resting-state fMRI were used to investigate connections among brain areas with the conditional GCM method. With the GLM detected brain activation regions in the emotion related cortex during the block design paradigm, the conditional GCM method was proposed to study the causality of the habituation between the left amygdala and pregenual cingulate cortex during emotion processing. For the resting-state dataset, it is possible to calculate not only the effective connectivity between networks but also the heterogeneity within a single network. Our results have further shown a particular interacting pattern of default mode network (DMN) that can be characterized as both afferent and efferent influences on the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC). These results suggest that the conditional GCM approach based on a linear multivariate vector autoregressive (MVAR) model can achieve

  19. Measuring frequency domain granger causality for multiple blocks of interacting time series.

    PubMed

    Faes, Luca; Nollo, Giandomenico

    2013-04-01

    In the past years, several frequency-domain causality measures based on vector autoregressive time series modeling have been suggested to assess directional connectivity in neural systems. The most followed approaches are based on representing the considered set of multiple time series as a realization of two or three vector-valued processes, yielding the so-called Geweke linear feedback measures, or as a realization of multiple scalar-valued processes, yielding popular measures like the directed coherence (DC) and the partial DC (PDC). In the present study, these two approaches are unified and generalized by proposing novel frequency-domain causality measures which extend the existing measures to the analysis of multiple blocks of time series. Specifically, the block DC (bDC) and block PDC (bPDC) extend DC and PDC to vector-valued processes, while their logarithmic counterparts, denoted as multivariate total feedback [Formula: see text] and direct feedback [Formula: see text], represent into a full multivariate framework the Geweke's measures. Theoretical analysis of the proposed measures shows that they: (i) possess desirable properties of causality measures; (ii) are able to reflect either direct causality (bPDC, [Formula: see text] or total (direct + indirect) causality (bDC, [Formula: see text] between time series blocks; (iii) reduce to the DC and PDC measures for scalar-valued processes, and to the Geweke's measures for pairs of processes; (iv) are able to capture internal dependencies between the scalar constituents of the analyzed vector processes. Numerical analysis showed that the proposed measures can be efficiently estimated from short time series, allow to represent in an objective, compact way the information derived from the causal analysis of several pairs of time series, and may detect frequency domain causality more accurately than existing measures. The proposed measures find their natural application in the evaluation of directional

  20. Measuring frequency domain granger causality for multiple blocks of interacting time series.

    PubMed

    Faes, Luca; Nollo, Giandomenico

    2013-04-01

    In the past years, several frequency-domain causality measures based on vector autoregressive time series modeling have been suggested to assess directional connectivity in neural systems. The most followed approaches are based on representing the considered set of multiple time series as a realization of two or three vector-valued processes, yielding the so-called Geweke linear feedback measures, or as a realization of multiple scalar-valued processes, yielding popular measures like the directed coherence (DC) and the partial DC (PDC). In the present study, these two approaches are unified and generalized by proposing novel frequency-domain causality measures which extend the existing measures to the analysis of multiple blocks of time series. Specifically, the block DC (bDC) and block PDC (bPDC) extend DC and PDC to vector-valued processes, while their logarithmic counterparts, denoted as multivariate total feedback [Formula: see text] and direct feedback [Formula: see text], represent into a full multivariate framework the Geweke's measures. Theoretical analysis of the proposed measures shows that they: (i) possess desirable properties of causality measures; (ii) are able to reflect either direct causality (bPDC, [Formula: see text] or total (direct + indirect) causality (bDC, [Formula: see text] between time series blocks; (iii) reduce to the DC and PDC measures for scalar-valued processes, and to the Geweke's measures for pairs of processes; (iv) are able to capture internal dependencies between the scalar constituents of the analyzed vector processes. Numerical analysis showed that the proposed measures can be efficiently estimated from short time series, allow to represent in an objective, compact way the information derived from the causal analysis of several pairs of time series, and may detect frequency domain causality more accurately than existing measures. The proposed measures find their natural application in the evaluation of directional

  1. Hierarchy of neural organization in the embryonic spinal cord: Granger-causality graph analysis of in vivo calcium imaging data.

    PubMed

    Fallani, Fabrizio De Vico; Corazzol, Martina; Sternberg, Jenna R; Wyart, Claire; Chavez, Mario

    2015-05-01

    The recent development of genetically encoded calcium indicators enables monitoring in vivo the activity of neuronal populations. Most analysis of these calcium transients relies on linear regression analysis based on the sensory stimulus applied or the behavior observed. To estimate the basic properties of the functional neural circuitry, we propose a network approach to calcium imaging recorded at single cell resolution. Differently from previous analysis based on cross-correlation, we used Granger-causality estimates to infer information propagation between the activities of different neurons. The resulting functional network was then modeled as a directed graph and characterized in terms of connectivity and node centralities. We applied our approach to calcium transients recorded at low frequency (4 Hz) in ventral neurons of the zebrafish spinal cord at the embryonic stage when spontaneous coiling of the tail occurs. Our analysis on population calcium imaging data revealed a strong ipsilateral connectivity and a characteristic hierarchical organization of the network hubs that supported established propagation of activity from rostral to caudal spinal cord. Our method could be used for detecting functional defects in neuronal circuitry during development and pathological conditions.

  2. Granger Causality Mapping during Joint Actions Reveals Evidence for Forward Models That Could Overcome Sensory-Motor Delays

    PubMed Central

    Kokal, Idil; Keysers, Christian

    2010-01-01

    Studies investigating joint actions have suggested a central role for the putative mirror neuron system (pMNS) because of the close link between perception and action provided by these brain regions [1], [2], [3]. In contrast, our previous functional magnetic resonance imaging (fMRI) experiment demonstrated that the BOLD response of the pMNS does not suggest that it directly integrates observed and executed actions during joint actions [4]. To test whether the pMNS might contribute indirectly to the integration process by sending information to brain areas responsible for this integration (integration network), here we used Granger causality mapping (GCM) [5]. We explored the directional information flow between the anterior sites of the pMNS and previously identified integrative brain regions. We found that the left BA44 sent more information than it received to both the integration network (left thalamus, right middle occipital gyrus and cerebellum) and more posterior nodes of the pMNS (BA2). Thus, during joint actions, two anatomically separate networks therefore seem effectively connected and the information flow is predominantly from anterior to posterior areas of the brain. These findings suggest that the pMNS is involved indirectly in joint actions by transforming observed and executed actions into a common code and is part of a generative model that could predict the future somatosensory and visual consequences of observed and executed actions in order to overcome otherwise inevitable neural delays. PMID:20975836

  3. Lexical mediation of phonotactic frequency effects on spoken word recognition: A Granger causality analysis of MRI-constrained MEG/EEG data

    PubMed Central

    Gow, David W.; Olson, Bruna B.

    2015-01-01

    Phonotactic frequency effects play a crucial role in a number of debates over language processing and representation. It is unclear however, whether these effects reflect prelexical sensitivity to phonotactic frequency, or lexical “gang effects” in speech perception. In this paper, we use Granger causality analysis of MR-constrained MEG/EEG data to understand how phonotactic frequency influences neural processing dynamics during auditory lexical decision. Effective connectivity analysis showed weaker feedforward influence from brain regions involved in acoustic-phonetic processing (superior temporal gyrus) to lexical areas (supramarginal gyrus) for high phonotactic frequency words, but stronger top-down lexical influence for the same items. Low entropy nonwords (nonwords judged to closely resemble real words) showed a similar pattern of interactions between brain regions involved in lexical and acoustic-phonetic processing. These results contradict the predictions of a feedforward model of phonotactic frequency facilitation, but support the predictions of a lexically mediated account. PMID:25883413

  4. Disorganization of Equilibrium Directional Interactions in the Brain Motor Network of Parkinson's disease: New Insight of Resting State Analysis Using Granger Causality and Graphical Approach

    PubMed Central

    Ghasemi, Mahdieh; Mahloojifar, Ali

    2013-01-01

    Parkinson's disease (PD) is a progressive neurological disorder characterized by tremor, rigidity, and slowness of movements. Particular changes related to various pathological attacks in PD could result in causal interactions of the brain network from resting state functional magnetic resonance imaging (rs-fMRI) data. In this paper, we aimed to disclose the network structure of the directed influences over the brain using multivariate Granger causality analysis and graph theory in patients with PD as compared with control group. rs-fMRI at rest from 10 PD patients and 10 controls were analyzed. Topological properties of the networks showed that information flow in PD is smaller than that in healthy individuals. We found that there is a balanced local network in healthy control group, including positive pair-wise cross connections between caudate and cerebellum and reciprocal connections between motor cortex and caudate in the left and right hemispheres. The results showed that this local network is disrupted in PD due to disturbance of the interactions in the motor networks. These findings suggested alteration of the functional organization of the brain in the resting state that affects the information transmission from and to other brain regions related to both primary dysfunctions and higher-level cognition impairments in PD. Furthermore, we showed that regions with high degree values could be detected as betweenness centrality nodes. Our results demonstrate that properties of small-world connectivity could also recognize and quantify the characteristics of directed influence brain networks in PD. PMID:24098860

  5. Automatic search for fMRI connectivity mapping: an alternative to Granger causality testing using formal equivalences among SEM path modeling, VAR, and unified SEM.

    PubMed

    Gates, Kathleen M; Molenaar, Peter C M; Hillary, Frank G; Ram, Nilam; Rovine, Michael J

    2010-04-15

    Modeling the relationships among brain regions of interest (ROIs) carries unique potential to explicate how the brain orchestrates information processing. However, hurdles arise when using functional MRI data. Variation in ROI activity contains sequential dependencies and shared influences on synchronized activation. Consequently, both lagged and contemporaneous relationships must be considered for unbiased statistical parameter estimation. Identifying these relationships using a data-driven approach could guide theory-building regarding integrated processing. The present paper demonstrates how the unified SEM attends to both lagged and contemporaneous influences on ROI activity. Additionally, this paper offers an approach akin to Granger causality testing, Lagrange multiplier testing, for statistically identifying directional influence among ROIs and employs this approach using an automatic search procedure to arrive at the optimal model. Rationale for this equivalence is offered by explicating the formal relationships among path modeling, vector autoregression, and unified SEM. When applied to simulated data, biases in estimates which do not consider both lagged and contemporaneous paths become apparent. Finally, the use of unified SEM with the automatic search procedure is applied to an empirical data example.

  6. Control over the strength of connections between modules: a double dissociation between stimulus format and task revealed by Granger causality mapping in fMRI

    PubMed Central

    Anderson, Britt; Soliman, Sherif; O’Malley, Shannon; Danckert, James; Besner, Derek

    2015-01-01

    Drawing on theoretical and computational work with the localist dual route reading model and results from behavioral studies, Besner et al. (2011) proposed that the ability to perform tasks that require overriding stimulus-specific defaults (e.g., semantics when naming Arabic numerals, and phonology when evaluating the parity of number words) necessitate the ability to modulate the strength of connections between cognitive modules for lexical representation, semantics, and phonology on a task- and stimulus-specific basis. We used functional magnetic resonance imaging to evaluate this account by assessing changes in functional connectivity while participants performed tasks that did and did not require such stimulus-task default overrides. The occipital region showing the greatest modulation of BOLD signal strength for the two stimulus types was used as the seed region for Granger causality mapping (GCM). Our GCM analysis revealed a region of rostromedial frontal cortex with a crossover interaction. When participants performed tasks that required overriding stimulus type defaults (i.e., parity judgments of number words and naming Arabic numerals) functional connectivity between the occipital region and rostromedial frontal cortex was present. Statistically significant functional connectivity was absent when the tasks were the default for the stimulus type (i.e., parity judgments of Arabic numerals and reading number words). This frontal region (BA 10) has previously been shown to be involved in goal-directed behavior and maintenance of a specific task set. We conclude that overriding stimulus-task defaults requires a modulation of connection strengths between cognitive modules and that the override mechanism predicted from cognitive theory is instantiated by frontal modulation of neural activity of brain regions specialized for sensory processing. PMID:25870571

  7. Globally conditioned Granger causality in brain-brain and brain-heart interactions: a combined heart rate variability/ultra-high-field (7 T) functional magnetic resonance imaging study.

    PubMed

    Duggento, Andrea; Bianciardi, Marta; Passamonti, Luca; Wald, Lawrence L; Guerrisi, Maria; Barbieri, Riccardo; Toschi, Nicola

    2016-05-13

    The causal, directed interactions between brain regions at rest (brain-brain networks) and between resting-state brain activity and autonomic nervous system (ANS) outflow (brain-heart links) have not been completely elucidated. We collected 7 T resting-state functional magnetic resonance imaging (fMRI) data with simultaneous respiration and heartbeat recordings in nine healthy volunteers to investigate (i) the causal interactions between cortical and subcortical brain regions at rest and (ii) the causal interactions between resting-state brain activity and the ANS as quantified through a probabilistic, point-process-based heartbeat model which generates dynamical estimates for sympathetic and parasympathetic activity as well as sympathovagal balance. Given the high amount of information shared between brain-derived signals, we compared the results of traditional bivariate Granger causality (GC) with a globally conditioned approach which evaluated the additional influence of each brain region on the causal target while factoring out effects concomitantly mediated by other brain regions. The bivariate approach resulted in a large number of possibly spurious causal brain-brain links, while, using the globally conditioned approach, we demonstrated the existence of significant selective causal links between cortical/subcortical brain regions and sympathetic and parasympathetic modulation as well as sympathovagal balance. In particular, we demonstrated a causal role of the amygdala, hypothalamus, brainstem and, among others, medial, middle and superior frontal gyri, superior temporal pole, paracentral lobule and cerebellar regions in modulating the so-called central autonomic network (CAN). In summary, we show that, provided proper conditioning is employed to eliminate spurious causalities, ultra-high-field functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain-brain and brain-heart interactions reflecting

  8. Wiener-Granger causality for effective connectivity in the hidden states: Indication from probabilistic causality. Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino and S.L. Bressler

    NASA Astrophysics Data System (ADS)

    Tang, Wei

    2015-12-01

    Statistics and probability theory have advanced our understanding of random processes widely observed in the physical world. There is a remarkable trend in studying the brain by looking into the stochastic information processing in large-scale brain networks [1,2]. As the review by Mannino and Bressler [3] points out, the probabilistic notion of causality, with its rooted philosophical foundations, represents a revolutionary view on how different parts of the brain interact and integrate to generate function. Specifically, Probabilistic Causality (PC) asserts that a cause should increase the probability of occurrence of its effect, and PC between two brain regions entails that the probability for the activity in one region to occur increases when conditioned on the activity of the other. This definition claims inherent randomness in the causal relationship.

  9. Granger causality from changes in level of atmospheric CO2 to global surface temperature and the El Niño-Southern Oscillation, and a candidate mechanism in global photosynthesis

    NASA Astrophysics Data System (ADS)

    Leggett, L. M. W.; Ball, D. A.

    2015-10-01

    A significant difference, now of some 16 years' duration, has been shown to exist between the observed global surface temperature trend and that expected from the majority of climate simulations. For its own sake, and to enable better climate prediction for policy use, the reasons behind this mismatch need to be better understood. While an increasing number of possible causes have been proposed, the candidate causes have not yet converged. With this background, this paper reinvestigates the relationship between change in the level of CO2 and two of the major climate variables, atmospheric temperature and the El Niño-Southern Oscillation (ENSO). Using time-series analysis in the form of dynamic regression modelling with autocorrelation correction, it is shown that first-difference CO2 leads temperature and that there is a highly statistically significant correlation between first-difference CO2 and temperature. Further, a correlation is found for second-difference CO2 with the Southern Oscillation Index, the atmospheric-pressure component of ENSO. This paper also shows that both these correlations display Granger causality. It is shown that the first-difference CO2 and temperature model shows no trend mismatch in recent years. These results may contribute to the prediction of future trends for global temperature and ENSO. Interannual variability in the growth rate of atmospheric CO2 is standardly attributed to variability in the carbon sink capacity of the terrestrial biosphere. The terrestrial biosphere carbon sink is created by the difference between photosynthesis and respiration (net primary productivity): a major way of measuring global terrestrial photosynthesis is by means of satellite measurements of vegetation reflectance, such as the Normalized Difference Vegetation Index (NDVI). In a preliminary analysis, this study finds a close correlation between an increasing NDVI and the increasing climate model/temperature mismatch (as quantified by the difference

  10. Causality

    NASA Astrophysics Data System (ADS)

    Pearl, Judea

    2000-03-01

    Written by one of the pre-eminent researchers in the field, this book provides a comprehensive exposition of modern analysis of causation. It shows how causality has grown from a nebulous concept into a mathematical theory with significant applications in the fields of statistics, artificial intelligence, philosophy, cognitive science, and the health and social sciences. Pearl presents a unified account of the probabilistic, manipulative, counterfactual and structural approaches to causation, and devises simple mathematical tools for analyzing the relationships between causal connections, statistical associations, actions and observations. The book will open the way for including causal analysis in the standard curriculum of statistics, artifical intelligence, business, epidemiology, social science and economics. Students in these areas will find natural models, simple identification procedures, and precise mathematical definitions of causal concepts that traditional texts have tended to evade or make unduly complicated. This book will be of interest to professionals and students in a wide variety of fields. Anyone who wishes to elucidate meaningful relationships from data, predict effects of actions and policies, assess explanations of reported events, or form theories of causal understanding and causal speech will find this book stimulating and invaluable.

  11. Causality networks from multivariate time series and application to epilepsy.

    PubMed

    Siggiridou, Elsa; Koutlis, Christos; Tsimpiris, Alkiviadis; Kimiskidis, Vasilios K; Kugiumtzis, Dimitris

    2015-08-01

    Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series. In this work, a large number of Granger causality measures used to form causality networks from multivariate time series are assessed. For this, realizations on high dimensional coupled dynamical systems are considered and the performance of the Granger causality measures is evaluated, seeking for the measures that form networks closest to the true network of the dynamical system. In particular, the comparison focuses on Granger causality measures that reduce the state space dimension when many variables are observed. Further, the linear and nonlinear Granger causality measures of dimension reduction are compared to a standard Granger causality measure on electroencephalographic (EEG) recordings containing episodes of epileptiform discharges.

  12. Parallel versus Serial Processing Dependencies in the Perisylvian Speech Network: A Granger Analysis of Intracranial EEG Data

    ERIC Educational Resources Information Center

    Gow, David W., Jr.; Keller, Corey J.; Eskandar, Emad; Meng, Nate; Cash, Sydney S.

    2009-01-01

    In this work, we apply Granger causality analysis to high spatiotemporal resolution intracranial EEG (iEEG) data to examine how different components of the left perisylvian language network interact during spoken language perception. The specific focus is on the characterization of serial versus parallel processing dependencies in the dominant…

  13. Detecting causality in complex ecosystems.

    PubMed

    Sugihara, George; May, Robert; Ye, Hao; Hsieh, Chih-hao; Deyle, Ethan; Fogarty, Michael; Munch, Stephan

    2012-10-26

    Identifying causal networks is important for effective policy and management recommendations on climate, epidemiology, financial regulation, and much else. We introduce a method, based on nonlinear state space reconstruction, that can distinguish causality from correlation. It extends to nonseparable weakly connected dynamic systems (cases not covered by the current Granger causality paradigm). The approach is illustrated both by simple models (where, in contrast to the real world, we know the underlying equations/relations and so can check the validity of our method) and by application to real ecological systems, including the controversial sardine-anchovy-temperature problem.

  14. Causal inference with multiple time series: principles and problems.

    PubMed

    Eichler, Michael

    2013-08-28

    I review the use of the concept of Granger causality for causal inference from time-series data. First, I give a theoretical justification by relating the concept to other theoretical causality measures. Second, I outline possible problems with spurious causality and approaches to tackle these problems. Finally, I sketch an identification algorithm that learns causal time-series structures in the presence of latent variables. The description of the algorithm is non-technical and thus accessible to applied scientists who are interested in adopting the method.

  15. Membership Finland

    ScienceCinema

    None

    2016-07-12

    Le DG C.Rubbia et la vice présidente du conseil du Cern souhaite la bienvenue à l'adhésion de la Finlande, comme 15me membre du Cern depuis le 1. janvier 1991 en présence du secrétaire generale et de l'ambassadeur

  16. Multiscale causal connectivity analysis by canonical correlation: theory and application to epileptic brain.

    PubMed

    Wu, Guo Rong; Chen, Fuyong; Kang, Dezhi; Zhang, Xiangyang; Marinazzo, Daniele; Chen, Huafu

    2011-11-01

    Multivariate Granger causality is a well-established approach for inferring information flow in complex systems, and it is being increasingly applied to map brain connectivity. Traditional Granger causality is based on vector autoregressive (AR) or mixed autoregressive moving average (ARMA) model, which are potentially affected by errors in parameter estimation and may be contaminated by zero-lag correlation, notably when modeling neuroimaging data. To overcome this issue, we present here an extended canonical correlation approach to measure multivariate Granger causal interactions among time series. The procedure includes a reduced rank step for calculating canonical correlation analysis (CCA), and extends the definition of causality including instantaneous effects, thus avoiding the potential estimation problems of AR (or ARMA) models. We tested this approach on simulated data and confirmed its practical utility by exploring local network connectivity at different scales in the epileptic brain analyzing scalp and depth-EEG data during an interictal period. PMID:21788178

  17. Algorithms for the inference of causality in dynamic processes: Application to cardiovascular and cerebrovascular variability.

    PubMed

    Faes, Luca; Porta, Alberto; Nollo, Giandomenico

    2015-08-01

    This study faces the problem of causal inference in multivariate dynamic processes, with specific regard to the detection of instantaneous and time-lagged directed interactions. We point out the limitations of the traditional Granger causality analysis, showing that it leads to false detection of causality when instantaneous and time-lagged effects coexist in the process structure. Then, we propose an improved algorithm for causal inference that combines the Granger framework with the approach proposed by Pearl for the study of causality among multiple random variables. This new approach is compared with the traditional one in theoretical and simulated examples of interacting processes, showing its ability to retrieve the correct structure of instantaneous and time-lagged interactions. These approaches for causal inference are then tested on the physiological variability series of heart period, arterial pressure and cerebral blood flow variability obtained in subjects with postural-related syncope during a tilt-test protocol.

  18. Confounding effects of phase delays on causality estimation.

    PubMed

    Vakorin, Vasily A; Mišić, Bratislav; Krakovska, Olga; Bezgin, Gleb; McIntosh, Anthony R

    2013-01-01

    Linear and non-linear techniques for inferring causal relations between the brain signals representing the underlying neuronal systems have become a powerful tool to extract the connectivity patterns in the brain. Typically these tools employ the idea of Granger causality, which is ultimately based on the temporal precedence between the signals. At the same time, phase synchronization between coupled neural ensembles is considered a mechanism implemented in the brain to integrate relevant neuronal ensembles to perform a cognitive or perceptual task. Phase synchronization can be studied by analyzing the effects of phase-locking between the brain signals. However, we should expect that there is no one-to-one mapping between the observed phase lag and the time precedence as specified by physically interacting systems. Specifically, phase lag observed between two signals may interfere with inferring causal relations. This could be of critical importance for the coupled non-linear oscillating systems, with possible time delays in coupling, when classical linear cross-spectrum strategies for solving phase ambiguity are not efficient. To demonstrate this, we used a prototypical model of coupled non-linear systems, and compared three typical pipelines of inferring Granger causality, as established in the literature. Specifically, we compared the performance of the spectral and information-theoretic Granger pipelines as well as standard Granger causality in their relations to the observed phase differences for frequencies at which the signals become synchronized to each other. We found that an information-theoretic approach, which takes into account different time lags between the past of one signal and the future of another signal, was the most robust to phase effects.

  19. A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression

    PubMed Central

    Nicolaou, Nicoletta; Constandinou, Timothy G.

    2016-01-01

    Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, CNPMR, Autoregressive modeling is replaced by Nonparametric Multiplicative Regression (NPMR). NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how a particular measure, the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply CNPMR on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). CNPMR correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. CNPMR is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonpametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications. PMID:27378901

  20. A Nonlinear Causality Estimator Based on Non-Parametric Multiplicative Regression.

    PubMed

    Nicolaou, Nicoletta; Constandinou, Timothy G

    2016-01-01

    Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, C NPMR , Autoregressive modeling is replaced by Nonparametric Multiplicative Regression (NPMR). NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how a particular measure, the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply C NPMR on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). C NPMR correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. C NPMR is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonpametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications.

  1. Causal and causally separable processes

    NASA Astrophysics Data System (ADS)

    Oreshkov, Ognyan; Giarmatzi, Christina

    2016-09-01

    The idea that events are equipped with a partial causal order is central to our understanding of physics in the tested regimes: given two pointlike events A and B, either A is in the causal past of B, B is in the causal past of A, or A and B are space-like separated. Operationally, the meaning of these order relations corresponds to constraints on the possible correlations between experiments performed in the vicinities of the respective events: if A is in the causal past of B, an experimenter at A could signal to an experimenter at B but not the other way around, while if A and B are space-like separated, no signaling is possible in either direction. In the context of a concrete physical theory, the correlations compatible with a given causal configuration may obey further constraints. For instance, space-like correlations in quantum mechanics arise from local measurements on joint quantum states, while time-like correlations are established via quantum channels. Similarly to other variables, however, the causal order of a set of events could be random, and little is understood about the constraints that causality implies in this case. A main difficulty concerns the fact that the order of events can now generally depend on the operations performed at the locations of these events, since, for instance, an operation at A could influence the order in which B and C occur in A’s future. So far, no formal theory of causality compatible with such dynamical causal order has been developed. Apart from being of fundamental interest in the context of inferring causal relations, such a theory is imperative for understanding recent suggestions that the causal order of events in quantum mechanics can be indefinite. Here, we develop such a theory in the general multipartite case. Starting from a background-independent definition of causality, we derive an iteratively formulated canonical decomposition of multipartite causal correlations. For a fixed number of settings and

  2. Effect of Causal Stories in Solving Mathematical Story Problems

    ERIC Educational Resources Information Center

    Smith, Glenn Gordon; Gerretson, Helen; Olkun, Sinan; Joutsenlahti, Jorma

    2010-01-01

    This study investigated whether infusing "causal" story elements into mathematical word problems improves student performance. In one experiment in the USA and a second in USA, Finland and Turkey, undergraduate elementary education majors worked word problems in three formats: 1) standard (minimal verbiage), 2) potential causation (causal and…

  3. Causal connectivity of evolved neural networks during behavior.

    PubMed

    Seth, Anil K

    2005-03-01

    To show how causal interactions in neural dynamics are modulated by behavior, it is valuable to analyze these interactions without perturbing or lesioning the neural mechanism. This paper proposes a method, based on a graph-theoretic extension of vector autoregressive modeling and 'Granger causality,' for characterizing causal interactions generated within intact neural mechanisms. This method, called 'causal connectivity analysis' is illustrated via model neural networks optimized for controlling target fixation in a simulated head-eye system, in which the structure of the environment can be experimentally varied. Causal connectivity analysis of this model yields novel insights into neural mechanisms underlying sensorimotor coordination. In contrast to networks supporting comparatively simple behavior, networks supporting rich adaptive behavior show a higher density of causal interactions, as well as a stronger causal flow from sensory inputs to motor outputs. They also show different arrangements of 'causal sources' and 'causal sinks': nodes that differentially affect, or are affected by, the remainder of the network. Finally, analysis of causal connectivity can predict the functional consequences of network lesions. These results suggest that causal connectivity analysis may have useful applications in the analysis of neural dynamics. PMID:16350433

  4. Higher Education and Unemployment: A Cointegration and Causality Analysis of the Case of Turkey

    ERIC Educational Resources Information Center

    Erdem, Ekrem; Tugcu, Can Tansel

    2012-01-01

    This article analyses the short and the long-term relations between higher education and unemployment in Turkey for the period 1960-2007. It chooses the recently developed ARDL cointegration and Granger causality of Dolado and Lutkepohl (1996) methods. While the proxy of unemployment is total unemployment rate, higher education graduates were…

  5. Education and Economic Growth in Pakistan: A Cointegration and Causality Analysis

    ERIC Educational Resources Information Center

    Afzal, Muhammad; Rehman, Hafeez Ur; Farooq, Muhammad Shahid; Sarwar, Kafeel

    2011-01-01

    This study explored the cointegration and causality between education and economic growth in Pakistan by using time series data on real gross domestic product (RGDP), labour force, physical capital and education from 1970-1971 to 2008-2009 were used. Autoregressive Distributed Lag (ARDL) Model of Cointegration and the Augmented Granger Causality…

  6. Algorithms of causal inference for the analysis of effective connectivity among brain regions

    PubMed Central

    Chicharro, Daniel; Panzeri, Stefano

    2014-01-01

    In recent years, powerful general algorithms of causal inference have been developed. In particular, in the framework of Pearl’s causality, algorithms of inductive causation (IC and IC*) provide a procedure to determine which causal connections among nodes in a network can be inferred from empirical observations even in the presence of latent variables, indicating the limits of what can be learned without active manipulation of the system. These algorithms can in principle become important complements to established techniques such as Granger causality and Dynamic Causal Modeling (DCM) to analyze causal influences (effective connectivity) among brain regions. However, their application to dynamic processes has not been yet examined. Here we study how to apply these algorithms to time-varying signals such as electrophysiological or neuroimaging signals. We propose a new algorithm which combines the basic principles of the previous algorithms with Granger causality to obtain a representation of the causal relations suited to dynamic processes. Furthermore, we use graphical criteria to predict dynamic statistical dependencies between the signals from the causal structure. We show how some problems for causal inference from neural signals (e.g., measurement noise, hemodynamic responses, and time aggregation) can be understood in a general graphical approach. Focusing on the effect of spatial aggregation, we show that when causal inference is performed at a coarser scale than the one at which the neural sources interact, results strongly depend on the degree of integration of the neural sources aggregated in the signals, and thus characterize more the intra-areal properties than the interactions among regions. We finally discuss how the explicit consideration of latent processes contributes to understand Granger causality and DCM as well as to distinguish functional and effective connectivity. PMID:25071541

  7. Algorithms of causal inference for the analysis of effective connectivity among brain regions.

    PubMed

    Chicharro, Daniel; Panzeri, Stefano

    2014-01-01

    In recent years, powerful general algorithms of causal inference have been developed. In particular, in the framework of Pearl's causality, algorithms of inductive causation (IC and IC(*)) provide a procedure to determine which causal connections among nodes in a network can be inferred from empirical observations even in the presence of latent variables, indicating the limits of what can be learned without active manipulation of the system. These algorithms can in principle become important complements to established techniques such as Granger causality and Dynamic Causal Modeling (DCM) to analyze causal influences (effective connectivity) among brain regions. However, their application to dynamic processes has not been yet examined. Here we study how to apply these algorithms to time-varying signals such as electrophysiological or neuroimaging signals. We propose a new algorithm which combines the basic principles of the previous algorithms with Granger causality to obtain a representation of the causal relations suited to dynamic processes. Furthermore, we use graphical criteria to predict dynamic statistical dependencies between the signals from the causal structure. We show how some problems for causal inference from neural signals (e.g., measurement noise, hemodynamic responses, and time aggregation) can be understood in a general graphical approach. Focusing on the effect of spatial aggregation, we show that when causal inference is performed at a coarser scale than the one at which the neural sources interact, results strongly depend on the degree of integration of the neural sources aggregated in the signals, and thus characterize more the intra-areal properties than the interactions among regions. We finally discuss how the explicit consideration of latent processes contributes to understand Granger causality and DCM as well as to distinguish functional and effective connectivity.

  8. Epidemiological causality.

    PubMed

    Morabia, Alfredo

    2005-01-01

    Epidemiological methods, which combine population thinking and group comparisons, can primarily identify causes of disease in populations. There is therefore a tension between our intuitive notion of a cause, which we want to be deterministic and invariant at the individual level, and the epidemiological notion of causes, which are invariant only at the population level. Epidemiologists have given heretofore a pragmatic solution to this tension. Causal inference in epidemiology consists in checking the logical coherence of a causality statement and determining whether what has been found grossly contradicts what we think we already know: how strong is the association? Is there a dose-response relationship? Does the cause precede the effect? Is the effect biologically plausible? Etc. This approach to causal inference can be traced back to the English philosophers David Hume and John Stuart Mill. On the other hand, the mode of establishing causality, devised by Jakob Henle and Robert Koch, which has been fruitful in bacteriology, requires that in every instance the effect invariably follows the cause (e.g., inoculation of Koch bacillus and tuberculosis). This is incompatible with epidemiological causality which has to deal with probabilistic effects (e.g., smoking and lung cancer), and is therefore invariant only for the population.

  9. Finland's energy choices

    SciTech Connect

    Jeffs, E.

    1994-01-01

    In Finland, a decision on a fifth nuclear power plant is stalled for at least two years. This leaves the country with a difficult choice for meeting anticipated electricity demand in the years ahead. This article examines the various energy alternatives of Finland and the political aspects of their energy development.

  10. New Levels of Language Processing Complexity and Organization Revealed by Granger Causation

    PubMed Central

    Gow, David W.; Caplan, David N.

    2012-01-01

    Granger causation analysis of high spatiotemporal resolution reconstructions of brain activation offers a new window on the dynamic interactions between brain areas that support language processing. Premised on the observation that causes both precede and uniquely predict their effects, this approach provides an intuitive, model-free means of identifying directed causal interactions in the brain. It requires the analysis of all non-redundant potentially interacting signals, and has shown that even “early” processes such as speech perception involve interactions of many areas in a strikingly large network that extends well beyond traditional left hemisphere perisylvian cortex that play out over hundreds of milliseconds. In this paper we describe this technique and review several general findings that reframe the way we think about language processing and brain function in general. These include the extent and complexity of language processing networks, the central role of interactive processing dynamics, the role of processing hubs where the input from many distinct brain regions are integrated, and the degree to which task requirements and stimulus properties influence processing dynamics and inform our understanding of “language-specific” localized processes. PMID:23293611

  11. Exploratory Causal Analysis in Bivariate Time Series Data

    NASA Astrophysics Data System (ADS)

    McCracken, James M.

    Many scientific disciplines rely on observational data of systems for which it is difficult (or impossible) to implement controlled experiments and data analysis techniques are required for identifying causal information and relationships directly from observational data. This need has lead to the development of many different time series causality approaches and tools including transfer entropy, convergent cross-mapping (CCM), and Granger causality statistics. In this thesis, the existing time series causality method of CCM is extended by introducing a new method called pairwise asymmetric inference (PAI). It is found that CCM may provide counter-intuitive causal inferences for simple dynamics with strong intuitive notions of causality, and the CCM causal inference can be a function of physical parameters that are seemingly unrelated to the existence of a driving relationship in the system. For example, a CCM causal inference might alternate between ''voltage drives current'' and ''current drives voltage'' as the frequency of the voltage signal is changed in a series circuit with a single resistor and inductor. PAI is introduced to address both of these limitations. Many of the current approaches in the times series causality literature are not computationally straightforward to apply, do not follow directly from assumptions of probabilistic causality, depend on assumed models for the time series generating process, or rely on embedding procedures. A new approach, called causal leaning, is introduced in this work to avoid these issues. The leaning is found to provide causal inferences that agree with intuition for both simple systems and more complicated empirical examples, including space weather data sets. The leaning may provide a clearer interpretation of the results than those from existing time series causality tools. A practicing analyst can explore the literature to find many proposals for identifying drivers and causal connections in times series data

  12. Causality and cointegration analysis between macroeconomic variables and the Bovespa.

    PubMed

    da Silva, Fabiano Mello; Coronel, Daniel Arruda; Vieira, Kelmara Mendes

    2014-01-01

    The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI), industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa). The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988) causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988) long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship) in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelfth month, followed by the country risk, with less than 5%.

  13. Causality and Cointegration Analysis between Macroeconomic Variables and the Bovespa

    PubMed Central

    da Silva, Fabiano Mello; Coronel, Daniel Arruda; Vieira, Kelmara Mendes

    2014-01-01

    The aim of this study is to analyze the causality relationship among a set of macroeconomic variables, represented by the exchange rate, interest rate, inflation (CPI), industrial production index as a proxy for gross domestic product in relation to the index of the São Paulo Stock Exchange (Bovespa). The period of analysis corresponded to the months from January 1995 to December 2010, making a total of 192 observations for each variable. Johansen tests, through the statistics of the trace and of the maximum eigenvalue, indicated the existence of at least one cointegration vector. In the analysis of Granger (1988) causality tests via error correction, it was found that a short-term causality existed between the CPI and the Bovespa. Regarding the Granger (1988) long-term causality, the results indicated a long-term behaviour among the macroeconomic variables with the BOVESPA. The results of the long-term normalized vector for the Bovespa variable showed that most signals of the cointegration equation parameters are in accordance with what is suggested by the economic theory. In other words, there was a positive behaviour of the GDP and a negative behaviour of the inflation and of the exchange rate (expected to be a positive relationship) in relation to the Bovespa, with the exception of the Selic rate, which was not significant with that index. The variance of the Bovespa was explained by itself in over 90% at the twelth month, followed by the country risk, with less than 5%. PMID:24587019

  14. Finland's Cleanup Campaign

    ERIC Educational Resources Information Center

    Environmental Science and Technology, 1975

    1975-01-01

    Finland has received a $20 million loan from the World Bank to attack its pollution problems, mainly water. Improved quality of life, as well as resource conservation are both motives and goals of that country's environmental programs. (BT)

  15. A review of causal inference for biomedical informatics

    PubMed Central

    Kleinberg, Samantha; Hripcsak, George

    2011-01-01

    Causality is an important concept throughout the health sciences and is particularly vital for informatics work such as finding adverse drug events or risk factors for disease using electronic health records. While philosophers and scientists working for centuries on formalizing what makes something a cause have not reached a consensus, new methods for inference show that we can make progress in this area in many practical cases. This article reviews core concepts in understanding and identifying causality and then reviews current computational methods for inference and explanation, focusing on inference from large-scale observational data. While the problem is not fully solved, we show that graphical models and Granger causality provide useful frameworks for inference and that a more recent approach based on temporal logic addresses some of the limitations of these methods. PMID:21782035

  16. Estimating the directed information to infer causal relationships in ensemble neural spike train recordings.

    PubMed

    Quinn, Christopher J; Coleman, Todd P; Kiyavash, Negar; Hatsopoulos, Nicholas G

    2011-02-01

    Advances in recording technologies have given neuroscience researchers access to large amounts of data, in particular, simultaneous, individual recordings of large groups of neurons in different parts of the brain. A variety of quantitative techniques have been utilized to analyze the spiking activities of the neurons to elucidate the functional connectivity of the recorded neurons. In the past, researchers have used correlative measures. More recently, to better capture the dynamic, complex relationships present in the data, neuroscientists have employed causal measures-most of which are variants of Granger causality-with limited success. This paper motivates the directed information, an information and control theoretic concept, as a modality-independent embodiment of Granger's original notion of causality. Key properties include: (a) it is nonzero if and only if one process causally influences another, and (b) its specific value can be interpreted as the strength of a causal relationship. We next describe how the causally conditioned directed information between two processes given knowledge of others provides a network version of causality: it is nonzero if and only if, in the presence of the present and past of other processes, one process causally influences another. This notion is shown to be able to differentiate between true direct causal influences, common inputs, and cascade effects in more two processes. We next describe a procedure to estimate the directed information on neural spike trains using point process generalized linear models, maximum likelihood estimation and information-theoretic model order selection. We demonstrate that on a simulated network of neurons, it (a) correctly identifies all pairwise causal relationships and (b) correctly identifies network causal relationships. This procedure is then used to analyze ensemble spike train recordings in primary motor cortex of an awake monkey while performing target reaching tasks, uncovering

  17. Causality in epidemiology.

    PubMed

    Kamangar, Farin

    2012-10-01

    This article provides an introduction to the meaning of causality in epidemiology and methods that epidemiologists use to distinguish causal associations from non-causal ones. Alternatives to causal association are discussed in detail. Hill's guidelines, set forth approximately 50 years ago, and more recent developments are reviewed. The role of religious and philosophic views in our understanding of causality is briefly discussed.

  18. Causal Predominance of Cognitions in Disturbed Affects among Finnish Primary School Teachers.

    ERIC Educational Resources Information Center

    Rajala, Raimo

    1990-01-01

    A putative causal relationship of cognitions to affects in different phases of teachers' stress cycles was studied for 414 elementary school teachers in Finland. Results provide only negligible support for the causal predominance of cognitions in disturbed affects; the opposite seemed to prevail. Implications for teacher satisfaction are…

  19. Circular causality.

    PubMed

    Thomas, R

    2006-07-01

    The problem of disentangling complex dynamic systems is addressed, especially with a view to identifying those variables that take part in the essential qualitative behaviour of systems. The author presents a series of reflections about the methods of formalisation together with the principles that govern the global operation of systems. In particular, a section on circuits, nuclei, and circular causality and a rather detailed description of the analytic use of the generalised asynchronous logical description, together with a brief description of its synthetic use (OreverseO logic). Some basic rules are recalled, such as the fact that a positive circuit is a necessary condition of multistationarity. Also, the interest of considering as a model, rather than a well-defined set of differential equations, a variety of systems that differ from each other only by the values of constant terms is emphasised. All these systems have a common Jacobian matrix and for all of them phase space has exactly the same structure. It means that all can be partitioned in the same way as regards the signs of the eigenvalues and thus as regards the precise nature of any steady states that might be present. Which steady states are actually present, depends on the values of terms of order zero in the ordinary differential equations (ODEs), and it is easy to find for which values of these terms a given point in phase space is steady. Models can be synthesised first at the level of the circuits involved in the Jacobian matrix (that determines which types and numbers of steady states are consistent with the model), then only at the level of terms of order zero in the ODE's (that determines which of the steady states actually exist), hence the title 'Circular casuality'.

  20. Quantum causal modelling

    NASA Astrophysics Data System (ADS)

    Costa, Fabio; Shrapnel, Sally

    2016-06-01

    Causal modelling provides a powerful set of tools for identifying causal structure from observed correlations. It is well known that such techniques fail for quantum systems, unless one introduces ‘spooky’ hidden mechanisms. Whether one can produce a genuinely quantum framework in order to discover causal structure remains an open question. Here we introduce a new framework for quantum causal modelling that allows for the discovery of causal structure. We define quantum analogues for core features of classical causal modelling techniques, including the causal Markov condition and faithfulness. Based on the process matrix formalism, this framework naturally extends to generalised structures with indefinite causal order.

  1. Outage management in Finland

    SciTech Connect

    Pernu, J.; Vuorenmaa, A.

    1987-01-01

    Of the electricity generated in Finland, approx. 40% is produced by nuclear power. There are two nuclear power stations in Finland: a boiling water reactor (BWR) station in Olkiluoto operated by TVO and a pressurized water reactor (PWR) station in Loviisa operated by IVO. The main technical information and the year that commercial operation of the nuclear units began are listed. Finland has long, cold, and dark winters. The summers are pleasant with several hours of daylight. The Finns like to have their holidays during July-August, which is why the major part of the energy-intensive base industries are shut down during this period. This means that the load on the Finnish grid may vary by a factor of 3 between a cold winter morning and a warm summer day. Because of these conditions, the utilities are trying to concentrate the annual reloading outages during late spring and summer. To be able to perform the outages of all four nuclear units in a short period of low marginal production cost, huge efforts had to be made to reduce the duration of outages. This reduction could not be done at the expense of availability during winter because the costs of replacement energy in winter are very high. Both utilities have succeeded in achieving their goals. The outage times have been reduced significantly and, at the same time, the average load factor in Finland has exceeded the 85% level.

  2. Sami Education in Finland

    ERIC Educational Resources Information Center

    Keskitalo, Pigga; Maatta, Kaarina; Uusiautti, Satu

    2012-01-01

    The purpose of this article is, first, to describe Sami children's education and its status in the Finnish education system and, secondly, to contemplate its development in Finland. The core of the article is intertwined with issues concerning the status, language, and culture of indigenous peoples. According to the article, the western school…

  3. Children's Books in Finland.

    ERIC Educational Resources Information Center

    Kuivasmaki, Riitta

    1984-01-01

    Discusses influence of Finnish Institute for Children's Literature (SNI) on writing, illustrating, translating, and marketing of children's books in Finland, and notes efforts of Finnish Section of International Board on Books for Young People to make children's literature and connected research better known to public. Publications of SNI are…

  4. Causal reasoning with forces

    PubMed Central

    Wolff, Phillip; Barbey, Aron K.

    2015-01-01

    Causal composition allows people to generate new causal relations by combining existing causal knowledge. We introduce a new computational model of such reasoning, the force theory, which holds that people compose causal relations by simulating the processes that join forces in the world, and compare this theory with the mental model theory (Khemlani et al., 2014) and the causal model theory (Sloman et al., 2009), which explain causal composition on the basis of mental models and structural equations, respectively. In one experiment, the force theory was uniquely able to account for people's ability to compose causal relationships from complex animations of real-world events. In three additional experiments, the force theory did as well as or better than the other two theories in explaining the causal compositions people generated from linguistically presented causal relations. Implications for causal learning and the hierarchical structure of causal knowledge are discussed. PMID:25653611

  5. Causally nonseparable processes admitting a causal model

    NASA Astrophysics Data System (ADS)

    Feix, Adrien; Araújo, Mateus; Brukner, Časlav

    2016-08-01

    A recent framework of quantum theory with no global causal order predicts the existence of ‘causally nonseparable’ processes. Some of these processes produce correlations incompatible with any causal order (they violate so-called ‘causal inequalities’ analogous to Bell inequalities) while others do not (they admit a ‘causal model’ analogous to a local model). Here we show for the first time that bipartite causally nonseparable processes with a causal model exist, and give evidence that they have no clear physical interpretation. We also provide an algorithm to generate processes of this kind and show that they have nonzero measure in the set of all processes. We demonstrate the existence of processes which stop violating causal inequalities but are still causally nonseparable when mixed with a certain amount of ‘white noise’. This is reminiscent of the behavior of Werner states in the context of entanglement and nonlocality. Finally, we provide numerical evidence for the existence of causally nonseparable processes which have a causal model even when extended with an entangled state shared among the parties.

  6. The causal relationship between subcortical local field potential oscillations and Parkinsonian resting tremor

    NASA Astrophysics Data System (ADS)

    Tass, Peter; Smirnov, Dmitry; Karavaev, Anatoly; Barnikol, Utako; Barnikol, Thomas; Adamchic, Ilya; Hauptmann, Christian; Pawelcyzk, Norbert; Maarouf, Mohammad; Sturm, Volker; Freund, Hans-Joachim; Bezruchko, Boris

    2010-02-01

    To study the dynamical mechanism which generates Parkinsonian resting tremor, we apply coupling directionality analysis to local field potentials (LFP) and accelerometer signals recorded in an ensemble of 48 tremor epochs in four Parkinsonian patients with depth electrodes implanted in the ventro-intermediate nucleus of the thalamus (VIM) or the subthalmic nucleus (STN). Apart from the traditional linear Granger causality method we use two nonlinear techniques: phase dynamics modelling and nonlinear Granger causality. We detect a bidirectional coupling between the subcortical (VIM or STN) oscillation and the tremor, in the theta range (around 5 Hz) as well as broadband (>2 Hz). In particular, we show that the theta band LFP oscillations definitely play an efferent role in tremor generation, while beta band LFP oscillations might additionally contribute. The brain→tremor driving is a complex, nonlinear mechanism, which is reliably detected with the two nonlinear techniques only. In contrast, the tremor→brain driving is detected with any of the techniques including the linear one, though the latter is less sensitive. The phase dynamics modelling (applied to theta band oscillations) consistently reveals a long delay in the order of 1-2 mean tremor periods for the brain→tremor driving and a small delay, compatible with the neural transmission time, for the proprioceptive feedback. Granger causality estimation (applied to broadband signals) does not provide reliable estimates of the delay times, but is even more sensitive to detect the brain→tremor influence than the phase dynamics modelling.

  7. Causality in physiological signals.

    PubMed

    Müller, Andreas; Kraemer, Jan F; Penzel, Thomas; Bonnemeier, Hendrik; Kurths, Jürgen; Wessel, Niels

    2016-05-01

    Health is one of the most important non-material assets and thus also has an enormous influence on material values, since treating and preventing diseases is expensive. The number one cause of death worldwide today originates in cardiovascular diseases. For these reasons the aim of understanding the functions and the interactions of the cardiovascular system is and has been a major research topic throughout various disciplines for more than a hundred years. The purpose of most of today's research is to get as much information as possible with the lowest possible effort and the least discomfort for the subject or patient, e.g. via non-invasive measurements. A family of tools whose importance has been growing during the last years is known under the headline of coupling measures. The rationale for this kind of analysis is to identify the structure of interactions in a system of multiple components. Important information lies for example in the coupling direction, the coupling strength, and occurring time lags. In this work, we will, after a brief general introduction covering the development of cardiovascular time series analysis, introduce, explain and review some of the most important coupling measures and classify them according to their origin and capabilities in the light of physiological analyses. We will begin with classical correlation measures, go via Granger-causality-based tools, entropy-based techniques (e.g. momentary information transfer), nonlinear prediction measures (e.g. mutual prediction) to symbolic dynamics (e.g. symbolic coupling traces). All these methods have contributed important insights into physiological interactions like cardiorespiratory coupling, neuro-cardio-coupling and many more. Furthermore, we will cover tools to detect and analyze synchronization and coordination (e.g. synchrogram and coordigram). As a last point we will address time dependent couplings as identified using a recent approach employing ensembles of time series. The

  8. Stochastic causality, criticality, and non-locality in brain networks. Comment on "Foundational perspectives on causality in large-scale brain networks" by M. Mannino and S.L. Bressler

    NASA Astrophysics Data System (ADS)

    Kozma, Robert; Hu, Sanqing

    2015-12-01

    For millennia, causality served as a powerful guiding principle to our understanding of natural processes, including the functioning of our body, mind, and brain. The target paper presents an impressive vista of the field of causality in brain networks, starting from philosophical issues, expanding on neuroscience effects, and addressing broad engineering and societal aspects as well. The authors conclude that the concept of stochastic causality is more suited to characterize the experimentally observed complex dynamical processes in large-scale brain networks, rather than the more traditional view of deterministic causality. We strongly support this conclusion and provide two additional examples that may enhance and complement this review: (i) a generalization of the Wiener-Granger Causality (WGC) to fit better the complexity of brain networks; (ii) employment of criticality as a key concept highly relevant to interpreting causality and non-locality in large-scale brain networks.

  9. Assessing Dynamic Spectral Causality by Lagged Adaptive Directed Transfer Function and Instantaneous Effect Factor

    PubMed Central

    Xu, Haojie; Lu, Yunfeng; Zhu, Shanan

    2014-01-01

    It is of significance to assess the dynamic spectral causality among physiological signals. Several practical estimators adapted from spectral Granger causality have been exploited to track dynamic causality based on the framework of time-varying multivariate autoregressive (tvMVAR) models. The non-zero covariance of the model’s residuals has been used to describe the instantaneous effect phenomenon in some causality estimators. However, for the situations with Gaussian residuals in some autoregressive models, it is challenging to distinguish the directed instantaneous causality if the sufficient prior information about the “causal ordering” is missing. Here, we propose a new algorithm to assess the time-varying causal ordering of tvMVAR model under the assumption that the signals follow the same acyclic causal ordering for all time lags and to estimate the instantaneous effect factor (IEF) value in order to track the dynamic directed instantaneous connectivity. The time-lagged adaptive directed transfer function (ADTF) is also estimated to assess the lagged causality after removing the instantaneous effect. In the present study, we firstly investigated the performance of the causal-ordering estimation algorithm and the accuracy of IEF value. Then, we presented the results of IEF and time-lagged ADTF method by comparing with the conventional ADTF method through simulations of various propagation models. Statistical analysis results suggest that the new algorithm could accurately estimate the causal ordering and give a good estimation of the IEF values in the Gaussian residual conditions. Meanwhile, the time-lagged ADTF approach is also more accurate in estimating the time-lagged dynamic interactions in a complex nervous system after extracting the instantaneous effect. In addition to the simulation studies, we applied the proposed method to estimate the dynamic spectral causality on real visual evoked potential (VEP) data in a human subject. Its usefulness in

  10. Causality analysis in business performance measurement system using system dynamics methodology

    NASA Astrophysics Data System (ADS)

    Yusof, Zainuridah; Yusoff, Wan Fadzilah Wan; Maarof, Faridah

    2014-07-01

    One of the main components of the Balanced Scorecard (BSC) that differentiates it from any other performance measurement system (PMS) is the Strategy Map with its unidirectional causality feature. Despite its apparent popularity, criticisms on the causality have been rigorously discussed by earlier researchers. In seeking empirical evidence of causality, propositions based on the service profit chain theory were developed and tested using the econometrics analysis, Granger causality test on the 45 data points. However, the insufficiency of well-established causality models was found as only 40% of the causal linkages were supported by the data. Expert knowledge was suggested to be used in the situations of insufficiency of historical data. The Delphi method was selected and conducted in obtaining the consensus of the causality existence among the 15 selected expert persons by utilizing 3 rounds of questionnaires. Study revealed that only 20% of the propositions were not supported. The existences of bidirectional causality which demonstrate significant dynamic environmental complexity through interaction among measures were obtained from both methods. With that, a computer modeling and simulation using System Dynamics (SD) methodology was develop as an experimental platform to identify how policies impacting the business performance in such environments. The reproduction, sensitivity and extreme condition tests were conducted onto developed SD model to ensure their capability in mimic the reality, robustness and validity for causality analysis platform. This study applied a theoretical service management model within the BSC domain to a practical situation using SD methodology where very limited work has been done.

  11. Tracking time-varying causality and directionality of information flow using an error reduction ratio test with applications to electroencephalography data.

    PubMed

    Zhao, Yifan; Billings, Steve A; Wei, Hualiang; Sarrigiannis, Ptolemaios G

    2012-11-01

    This paper introduces an error reduction ratio-causality (ERR-causality) test that can be used to detect and track causal relationships between two signals. In comparison to the traditional Granger method, one significant advantage of the new ERR-causality test is that it can effectively detect the time-varying direction of linear or nonlinear causality between two signals without fitting a complete model. Another important advantage is that the ERR-causality test can detect both the direction of interactions and estimate the relative time shift between the two signals. Numerical examples are provided to illustrate the effectiveness of the new method together with the determination of the causality between electroencephalograph signals from different cortical sites for patients during an epileptic seizure.

  12. Teachers as Leaders in Finland

    ERIC Educational Resources Information Center

    Sahlberg, Pasi

    2013-01-01

    During the last decade, thousands of visitors have flocked to Finland--now a leader in education rankings--to uncover this small Nordic country's secret to its education success. In this article, Finnish educator and scholar Pasi Sahlberg explains how Finland has managed such a feat. A rigorous graduate degree and at least five years of…

  13. Finland to Join ESO

    NASA Astrophysics Data System (ADS)

    2004-02-01

    Finland will become the eleventh member state of the European Southern Observatory (ESO) [1]. Today, during a ceremony at the ESO Headquarters in Garching (Germany), a corresponding Agreement was signed by the Finnish Minister of Education and Science, Ms. Tuula Haatainen and the ESO Director General, Dr. Catherine Cesarsky, in the presence of other high officials from Finland and the ESO member states (see Video Clip 02/04 below). Following subsequent ratification by the Finnish Parliament of the ESO Convention and the associated protocols [2], it is foreseen that Finland will formally join ESO on July 1, 2004. Uniting European Astronomy ESO PR Photo 03/04 ESO PR Photo 03/04 Caption : Signing of the Finland-ESO Agreement on February 9, 2004, at the ESO Headquarters in Garching (Germany). At the table, the ESO Director General, Dr. Catherine Cesarsky, and the Finnish Minister of Education and Science, Ms. Tuula Haatainen . [Preview - JPEG: 400 x 499 pix - 52k] [Normal - JPEG: 800 x 997 pix - 720k] [Full Res - JPEG: 2126 x 2649 pix - 2.9M] The Finnish Minister of Education and Science, Ms. Tuula Haatainen, began her speech with these words: "On behalf of Finland, I am happy and proud that we are now joining the European Southern Observatory, one of the most successful megaprojects of European science. ESO is an excellent example of the potential of European cooperation in science, and along with the ALMA project, more and more of global cooperation as well." She also mentioned that besides science ESO offers many technological challenges and opportunities. And she added: "In Finland we will try to promote also technological and industrial cooperation with ESO, and we hope that the ESO side will help us to create good working relations. I am confident that Finland's membership in ESO will be beneficial to both sides." Dr. Catherine Cesarsky, ESO Director General, warmly welcomed the Finnish intention to join ESO. "With the accession of their country to ESO, Finnish

  14. When two become one: the limits of causality analysis of brain dynamics.

    PubMed

    Chicharro, Daniel; Ledberg, Anders

    2012-01-01

    Biological systems often consist of multiple interacting subsystems, the brain being a prominent example. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. In this work we investigate the extent to which the cause-and-effect framework is applicable to such interacting subsystems. We base our work on a standard notion of causal effects and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We identify the constraints on the structure of the causal connections that determine the existence of natural causal effects. In particular, we show that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. Only when the causing subsystem is autonomous with respect to the rest can this interpretation be made. We note that subsystems in the brain are often bidirectionally connected, which means that interactions rarely should be quantified in terms of cause-and-effect. We furthermore introduce a framework for how natural causal effects can be characterized when they exist. Our work also has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM). Our results are generic and the concept of natural causal effects is relevant in all areas where the effects of interactions between subsystems are of interest.

  15. When Two Become One: The Limits of Causality Analysis of Brain Dynamics

    PubMed Central

    Chicharro, Daniel; Ledberg, Anders

    2012-01-01

    Biological systems often consist of multiple interacting subsystems, the brain being a prominent example. To understand the functions of such systems it is important to analyze if and how the subsystems interact and to describe the effect of these interactions. In this work we investigate the extent to which the cause-and-effect framework is applicable to such interacting subsystems. We base our work on a standard notion of causal effects and define a new concept called natural causal effect. This new concept takes into account that when studying interactions in biological systems, one is often not interested in the effect of perturbations that alter the dynamics. The interest is instead in how the causal connections participate in the generation of the observed natural dynamics. We identify the constraints on the structure of the causal connections that determine the existence of natural causal effects. In particular, we show that the influence of the causal connections on the natural dynamics of the system often cannot be analyzed in terms of the causal effect of one subsystem on another. Only when the causing subsystem is autonomous with respect to the rest can this interpretation be made. We note that subsystems in the brain are often bidirectionally connected, which means that interactions rarely should be quantified in terms of cause-and-effect. We furthermore introduce a framework for how natural causal effects can be characterized when they exist. Our work also has important consequences for the interpretation of other approaches commonly applied to study causality in the brain. Specifically, we discuss how the notion of natural causal effects can be combined with Granger causality and Dynamic Causal Modeling (DCM). Our results are generic and the concept of natural causal effects is relevant in all areas where the effects of interactions between subsystems are of interest. PMID:22438878

  16. Comparative performance evaluation of data-driven causality measures applied to brain networks.

    PubMed

    Fasoula, Angie; Attal, Yohan; Schwartz, Denis

    2013-05-15

    In this article, several well-known data-driven causality methods are revisited and comparatively evaluated. These are the Granger-Geweke Causality (GGC), the Partial Directed Coherence (PDC), the Directed Transfer Function (DTF) and the Direct Directed Transfer Function (dDTF). The robustness of the four causality measures against two degradation factors is quantitatively evaluated. These are: the presence of realistic biological/electronic noise at various SNR levels, as recorded on a MagnetoEncephalography (MEG) machine, and the presence of a weak node in the brain network where the causality analysis is applied. The causality measures are evaluated in terms of the relative estimation error and the compromise between true and fictitious causal density in the brain network. Both parametric and non-parametric causality analysis is performed. It is illustrated that the non-parametric method is a promising alternative to the more commonly applied MVAR-model based causality analysis. It is also demonstrated that, in the presence of both tested degradation factors, the DTF method is the most robust in terms of low estimation error, while the PDC in terms of low fictitious causal density. The dDTF provides lower fictitious causal density and higher spectral selectivity as compared to DTF, at high enough SNR. The GGC exhibits the worst compromise of performance. An application of the causality measures to a set of MEG resting-state experimental data is accordingly presented. It is demonstrated that significant contrast between the Eyes-Closed and Eyes-Open rest condition in the alpha frequency band allows to detect significant causality between the occipital cortex and the thalamus.

  17. Embeddings of Causal Sets

    SciTech Connect

    Reid, David D.

    2009-07-06

    A key postulate of the causal set program is that this discrete partial order offers a sufficiently rich structure to make it a viable model of spacetime for quantum gravity. If the deep structure of spacetime is that of a causal set, then the correspondence principle (with the spacetimes of general relativity) must be obeyed. Therefore, one of the requirements of this program is to establish that the causal set structure is in fact, not just in principle, fully consistent with our macroscopic notion of spacetime as a Lorentzian manifold. An important component of any such 'manifold test' is the ability to find embeddings of causal sets into Lorentzian manifolds.

  18. International report Finland

    SciTech Connect

    Not Available

    1982-04-01

    The Valentin Shashin, the world's first dynamically positioned ice-class drillship for Arctic conditions, has been completed by Rauma-Repola Oy's Mantyluoto Works in Pori, Finland and delivered to V/O Sudoimport, the Soviet Union. This drillship is the first of three such vessels ordered by the Soviet Union in 1979 for oil exploration in Russia's Arctic waters. All three drillships will be capable of operating in water depths to 300 m and of drilling to 20,000 ft in winds of 23 m/sec, in significant wave heights to 4.7 m and currents to 1 m/sec. Since the vessels are to be used in hostile Arctic conditions, the design incorporates a great deal of sophisticated equipment for operating in heavy seas and avoiding hazards, such as icebergs, that may exist in the drilling area. Included is a quick disconnect system that will allow the ship to detach from the drilling mode in approximately three minutes.

  19. Causal Learning Across Domains

    ERIC Educational Resources Information Center

    Schulz, Laura E.; Gopnik, Alison

    2004-01-01

    Five studies investigated (a) children's ability to use the dependent and independent probabilities of events to make causal inferences and (b) the interaction between such inferences and domain-specific knowledge. In Experiment 1, preschoolers used patterns of dependence and independence to make accurate causal inferences in the domains of…

  20. Causality in Classical Electrodynamics

    ERIC Educational Resources Information Center

    Savage, Craig

    2012-01-01

    Causality in electrodynamics is a subject of some confusion, especially regarding the application of Faraday's law and the Ampere-Maxwell law. This has led to the suggestion that we should not teach students that electric and magnetic fields can cause each other, but rather focus on charges and currents as the causal agents. In this paper I argue…

  1. Assessing Interactive Causal Influence

    ERIC Educational Resources Information Center

    Novick, Laura R.; Cheng, Patricia W.

    2004-01-01

    The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses…

  2. Repeated Causal Decision Making

    ERIC Educational Resources Information Center

    Hagmayer, York; Meder, Bjorn

    2013-01-01

    Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our world. We used a repeated decision-making paradigm to examine what kind of knowledge people acquire in…

  3. Population and growth causality in developing countries.

    PubMed

    Kapuria-foreman, V

    1995-07-01

    This study empirically tests the null hypotheses of no causality between population growth and economic growth and of no causality between economic growth and population growth in 15 developing countries. The model follows the Cheng Hsiao form with lag lengths to minimize Akaike's Final Prediction Error (FPE). Equations are run separately for each country. Lag lengths and Granger causality test were chosen according to three steps. 1) Each of the variables was regressed on its own lagged values with a maximum lag of five years. A lag length was chosen that minimized FPE, which was calculated for each regression. 2) Bivariate regressions were run with a fixed lag length for population growth and mixed lag lengths for the other variable, until the lag length which minimized FPE was determined. 3) The last step involved checking the lag length of population growth by keeping the lag fixed for economic growth. The economic growth measure was gross domestic product per capita. Findings indicate that in seven countries the null hypothesis of no causality between population growth and economic growth, either positive or negative, cannot be rejected (Ghana, Sri Lanka, Bolivia, Philippines, Syria, Thailand, and Argentina). In Nepal, India, China, Guatemala, Peru, Turkey, Chile, and Mexico lagged values of population growth improve predictions of economic growth. Higher economic growth has no significant effect on population growth rates in Nepal, Bolivia, Philippines, Guatemala, Peru, Thailand, Argentina, and Mexico. Interaction between economic growth and population growth was found in India, China, Turkey, and Chile. The direction of causation tests indicate that population growth has a significant positive impact on income growth in China, Guatemala, Turkey, Chile, and Mexico. India shows a negative impact of population growth on income. A significant negative impact of economic growth on population growth is evident only in Sri Lanka. There is weak evidence of a

  4. Agency, time, and causality

    PubMed Central

    Widlok, Thomas

    2014-01-01

    Cognitive Scientists interested in causal cognition increasingly search for evidence from non-Western Educational Industrial Rich Democratic people but find only very few cross-cultural studies that specifically target causal cognition. This article suggests how information about causality can be retrieved from ethnographic monographs, specifically from ethnographies that discuss agency and concepts of time. Many apparent cultural differences with regard to causal cognition dissolve when cultural extensions of agency and personhood to non-humans are taken into account. At the same time considerable variability remains when we include notions of time, linearity and sequence. The article focuses on ethnographic case studies from Africa but provides a more general perspective on the role of ethnography in research on the diversity and universality of causal cognition. PMID:25414683

  5. Causal Networks or Causal Islands? The Representation of Mechanisms and the Transitivity of Causal Judgment

    ERIC Educational Resources Information Center

    Johnson, Samuel G. B.; Ahn, Woo-kyoung

    2015-01-01

    Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge--an interconnected causal "network," where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms--causal "islands"--such that events in different…

  6. Warp drive and causality

    NASA Astrophysics Data System (ADS)

    Everett, Allen E.

    1996-06-01

    Alcubierre recently exhibited a spacetime which, within the framework of general relativity, allows travel at superluminal speeds if matter with a negative energy density can exist, and conjectured that it should be possible to use similar techniques to construct a theory containing closed causal loops and, thus, travel backwards in time. We verify this conjecture by exhibiting a simple modification of Alcubierre's model, requiring no additional assumptions, in which causal loops are possible. We also note that this mechanism for generating causal loops differs in essential ways from that discovered by Gott involving cosmic strings.

  7. Membership Contests: Encountering Immigrant Youth in Finland

    ERIC Educational Resources Information Center

    Harinen, Paivi; Suurpaa, Leena; Hoikkala, Tommi; Hautaniemi, Petri; Perho, Sini; Keskisalo, Anne-Mari; Kuure, Tapio; Kunnapuu, Krista

    2005-01-01

    This article discusses different aspects of social and societal membership, when minority groups of young immigrants living in Finland are under consideration. During its history, Finland has mainly been a country of emigration. In the 1990s the direction of moving turned to the contrary and the amount of immigrants in Finland increased relatively…

  8. Canadian Art Partnership Program in Finland

    ERIC Educational Resources Information Center

    Ketovuori, Mikko

    2011-01-01

    This article is about a multidisciplinary R&D project in which a Canadian Learning Through The Arts (LTTA) program was imported to Finland in 2003-2004. Cultural differences in arts education in Finland and Canada are discussed. While Finland has a national school curriculum with all the arts included. Canada relies more on partnerships to ensure…

  9. Causal conditionals and counterfactuals

    PubMed Central

    Frosch, Caren A.; Byrne, Ruth M.J.

    2012-01-01

    Causal counterfactuals e.g., ‘if the ignition key had been turned then the car would have started’ and causal conditionals e.g., ‘if the ignition key was turned then the car started’ are understood by thinking about multiple possibilities of different sorts, as shown in six experiments using converging evidence from three different types of measures. Experiments 1a and 1b showed that conditionals that comprise enabling causes, e.g., ‘if the ignition key was turned then the car started’ primed people to read quickly conjunctions referring to the possibility of the enabler occurring without the outcome, e.g., ‘the ignition key was turned and the car did not start’. Experiments 2a and 2b showed that people paraphrased causal conditionals by using causal or temporal connectives (because, when), whereas they paraphrased causal counterfactuals by using subjunctive constructions (had…would have). Experiments 3a and 3b showed that people made different inferences from counterfactuals presented with enabling conditions compared to none. The implications of the results for alternative theories of conditionals are discussed. PMID:22858874

  10. Causality discovery technology

    NASA Astrophysics Data System (ADS)

    Chen, M.; Ertl, T.; Jirotka, M.; Trefethen, A.; Schmidt, A.; Coecke, B.; Bañares-Alcántara, R.

    2012-11-01

    Causality is the fabric of our dynamic world. We all make frequent attempts to reason causation relationships of everyday events (e.g., what was the cause of my headache, or what has upset Alice?). We attempt to manage causality all the time through planning and scheduling. The greatest scientific discoveries are usually about causality (e.g., Newton found the cause for an apple to fall, and Darwin discovered natural selection). Meanwhile, we continue to seek a comprehensive understanding about the causes of numerous complex phenomena, such as social divisions, economic crisis, global warming, home-grown terrorism, etc. Humans analyse and reason causality based on observation, experimentation and acquired a priori knowledge. Today's technologies enable us to make observations and carry out experiments in an unprecedented scale that has created data mountains everywhere. Whereas there are exciting opportunities to discover new causation relationships, there are also unparalleled challenges to benefit from such data mountains. In this article, we present a case for developing a new piece of ICT, called Causality Discovery Technology. We reason about the necessity, feasibility and potential impact of such a technology.

  11. Causal networks or causal islands? The representation of mechanisms and the transitivity of causal judgment

    PubMed Central

    Johnson, Samuel G. B.; Ahn, Woo-kyoung

    2014-01-01

    Knowledge of mechanisms is critical for causal reasoning. We contrasted two possible organizations of causal knowledge—an interconnected causal network, where events are causally connected without any boundaries delineating discrete mechanisms; or a set of disparate mechanisms—causal islands—such that events in different mechanisms are not thought to be related even when they belong to the same causal chain. To distinguish these possibilities, we used causal transitivity—the inference given A causes B and B causes C that A causes C. Specifically, causal chains schematized as one chunk or mechanism in semantic memory (e.g., exercising, becoming thirsty, drinking water) led to transitive causal judgments. On the other hand, chains schematized as multiple chunks (e.g., having sex, becoming pregnant, becoming nauseous) led to intransitive judgments despite strong intermediate links (Experiments 1–3). Normative accounts of causal intransitivity could not explain these intransitive judgments (Experiments 4–5). PMID:25556901

  12. Causal premise semantics.

    PubMed

    Kaufmann, Stefan

    2013-08-01

    The rise of causality and the attendant graph-theoretic modeling tools in the study of counterfactual reasoning has had resounding effects in many areas of cognitive science, but it has thus far not permeated the mainstream in linguistic theory to a comparable degree. In this study I show that a version of the predominant framework for the formal semantic analysis of conditionals, Kratzer-style premise semantics, allows for a straightforward implementation of the crucial ideas and insights of Pearl-style causal networks. I spell out the details of such an implementation, focusing especially on the notions of intervention on a network and backtracking interpretations of counterfactuals.

  13. Systemic risk and causality dynamics of the world international shipping market

    NASA Astrophysics Data System (ADS)

    Zhang, Xin; Podobnik, Boris; Kenett, Dror Y.; Eugene Stanley, H.

    2014-12-01

    Various studies have reported that many economic systems have been exhibiting an increase in the correlation between different market sectors, a factor that exacerbates the level of systemic risk. We measure this systemic risk of three major world shipping markets, (i) the new ship market, (ii) the second-hand ship market, and (iii) the freight market, as well as the shipping stock market. Based on correlation networks during three time periods, that prior to the financial crisis, during the crisis, and after the crisis, minimal spanning trees (MSTs) and hierarchical trees (HTs) both exhibit complex dynamics, i.e., different market sectors tend to be more closely linked during financial crisis. Brownian distance correlation and Granger causality test both can be used to explore the directional interconnectedness of market sectors, while Brownian distance correlation captures more dependent relationships, which are not observed in the Granger causality test. These two measures can also identify and quantify market regression periods, implying that they contain predictive power for the current crisis.

  14. Causal relationship between CO₂ emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia.

    PubMed

    Farhani, Sahbi; Ozturk, Ilhan

    2015-10-01

    The aim of this paper is to examine the causal relationship between CO2 emissions, real GDP, energy consumption, financial development, trade openness, and urbanization in Tunisia over the period of 1971-2012. The long-run relationship is investigated by the auto-regressive distributed lag (ARDL) bounds testing approach to cointegration and error correction method (ECM). The results of the analysis reveal a positive sign for the coefficient of financial development, suggesting that the financial development in Tunisia has taken place at the expense of environmental pollution. The Tunisian case also shows a positive monotonic relationship between real GDP and CO2 emissions. This means that the results do not support the validity of environmental Kuznets curve (EKC) hypothesis. In addition, the paper explores causal relationship between the variables by using Granger causality models and it concludes that financial development plays a vital role in the Tunisian economy.

  15. Evaluating Causal Models.

    ERIC Educational Resources Information Center

    Watt, James H., Jr.

    Pointing out that linear causal models can organize the interrelationships of a large number of variables, this paper contends that such models are particularly useful to mass communication research, which must by necessity deal with complex systems of variables. The paper first outlines briefly the philosophical requirements for establishing a…

  16. Causal Responsibility and Counterfactuals

    ERIC Educational Resources Information Center

    Lagnado, David A.; Gerstenberg, Tobias; Zultan, Ro'i

    2013-01-01

    How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in…

  17. Causal Premise Semantics

    ERIC Educational Resources Information Center

    Kaufmann, Stefan

    2013-01-01

    The rise of causality and the attendant graph-theoretic modeling tools in the study of counterfactual reasoning has had resounding effects in many areas of cognitive science, but it has thus far not permeated the mainstream in linguistic theory to a comparable degree. In this study I show that a version of the predominant framework for the formal…

  18. Causal essentialism in kinds.

    PubMed

    Ahn, Woo-kyoung; Taylor, Eric G; Kato, Daniel; Marsh, Jessecae K; Bloom, Paul

    2013-06-01

    The current study examines causal essentialism, derived from psychological essentialism of concepts. We examine whether people believe that members of a category share some underlying essence that is both necessary and sufficient for category membership and that also causes surface features. The main claim is that causal essentialism is restricted to categories that correspond to our intuitive notions of existing kinds and hence is more attenuated for categories that are based on arbitrary criteria. Experiments 1 and 3 found that people overtly endorse causal essences in nonarbitrary kinds but are less likely to do so for arbitrary categories. Experiments 2 and 4 found that people were more willing to generalize a member's known causal relations (or lack thereof) when dealing with a kind than when dealing with an arbitrary category. These differences between kinds and arbitrary categories were found across various domains-not only for categories of living things, but also for artefacts. These findings have certain real-world implications, including how people make sense of mental disorders that are treated as real kinds. PMID:23098315

  19. The Causal Asymmetry

    ERIC Educational Resources Information Center

    White, Peter A.

    2006-01-01

    It is hypothesized that there is a pervasive and fundamental bias in humans' understanding of physical causation: Once the roles of cause and effect are assigned to objects in interactions, people tend to overestimate the strength and importance of the causal object and underestimate that of the effect object in bringing about the outcome. This…

  20. Causality: Physics and Philosophy

    ERIC Educational Resources Information Center

    Chatterjee, Atanu

    2013-01-01

    Nature is a complex causal network exhibiting diverse forms and species. These forms or rather systems are physically open, structurally complex and naturally adaptive. They interact with the surrounding media by operating a positive-feedback loop through which, they adapt, organize and self-organize themselves in response to the ever-changing…

  1. Causal Modes in the Low-frequency variability of Mediterranean and Middle-Eastern climates

    NASA Astrophysics Data System (ADS)

    Casagrande, Erik; Zampieri, Matteo; Artale, Vincenzo; Gualdi, Silvio; Molini, Annalisa

    2015-04-01

    In the last three decades, the Mediterranean and the Middle East experienced a phase of warming larger than the one that could be expected from global warming, and largely ascribable to natural (e.g. internal) climate variability. To better understand this process we explore here the presence of causal relationships among the diverse modes of variability of the climate system, focusing in particular on inter-annual and decadal scales of variability, influencing the climate of Mediterranean and Middle-Eastern regions. Causality measures used in this study include time and frequency-domain Granger causality (GC) and the phase slope index (Ψ), a directional coupling statistic developed by Nolte et. al. in 2007. GC metrics are applied to signals before and after the filtering of high frequency (inter-annual) components, while Ψ is designed to discern between low-frequency causal flow and higher frequency components. To assure the necessary sample size, the analysis is based on the preindustrial runs of the Fifth Coupled Model Intercomparison Project (CMIP5), which are free from external perturbation and last some hundred years. We selected the runs based on ENSO stationarity - to ensure that the simulations reached the equilibrium - and the consistent representation of the Atlantic Multidecadal Oscillation (AMO), which is considered one of the main drivers for the low-frequency (decadal) climate variability of the Mediterranean and the Middle East in summer. Finally, we discuss the potential of causality metrics for the predictability of future decadal variability in these regions.

  2. Productivity Analysis of Public and Private Airports: A Causal Investigation

    NASA Technical Reports Server (NTRS)

    Vasigh, Bijan; Gorjidooz, Javad

    2007-01-01

    Around the world, airports are being viewed as enterprises, rather than public services, which are expected to be managed efficiently and provide passengers with courteous customer services. Governments are, increasingly, turning to the private sectors for their efficiency in managing the operation, financing, and development, as well as providing security for airports. Operational and financial performance evaluation has become increasingly important to airport operators due to recent trends in airport privatization. Assessing performance allows the airport operators to plan for human resources and capital investment as efficiently as possible. Productivity measurements may be used as comparisons and guidelines in strategic planning, in the internal analysis of operational efficiency and effectiveness, and in assessing the competitive position of an airport in transportation industry. The primary purpose of this paper is to investigate the operational and financial efficiencies of 22 major airports in the United States and Europe. These airports are divided into three groups based on private ownership (7 British Airport Authority airports), public ownership (8 major United States airports), and a mix of private and public ownership (7 major European Union airports. The detail ownership structures of these airports are presented in Appendix A. Total factor productivity (TFP) model was utilized to measure airport performance in terms of financial and operational efficiencies and to develop a benchmarking tool to identify the areas of strength and weakness. A regression model was then employed to measure the relationship between TFP and ownership structure. Finally a Granger causality test was performed to determine whether ownership structure is a Granger cause of TFP. The results of the analysis presented in this paper demonstrate that there is not a significant relationship between airport TFP and ownership structure. Airport productivity and efficiency is

  3. Causal Discovery of Dynamic Systems

    ERIC Educational Resources Information Center

    Voortman, Mark

    2010-01-01

    Recently, several philosophical and computational approaches to causality have used an interventionist framework to clarify the concept of causality [Spirtes et al., 2000, Pearl, 2000, Woodward, 2005]. The characteristic feature of the interventionist approach is that causal models are potentially useful in predicting the effects of manipulations.…

  4. Context, causality, and appreciation.

    PubMed

    Ross, Stephanie

    2013-04-01

    I applaud and elaborate on the contextualism at the heart of Bullot & Reber's (B&R's) theory, challenge two aspects of the appreciative structure they posit (the causal reasoning that allegedly underlies the design stance and the segregation of the component stages), suggest that expert and novice appreciators operate differently, and question the degree to which B&R's final theory is open to empirical investigation. PMID:23507111

  5. Context, causality, and appreciation.

    PubMed

    Ross, Stephanie

    2013-04-01

    I applaud and elaborate on the contextualism at the heart of Bullot & Reber's (B&R's) theory, challenge two aspects of the appreciative structure they posit (the causal reasoning that allegedly underlies the design stance and the segregation of the component stages), suggest that expert and novice appreciators operate differently, and question the degree to which B&R's final theory is open to empirical investigation.

  6. Causal Responsibility and Counterfactuals

    PubMed Central

    Lagnado, David A; Gerstenberg, Tobias; Zultan, Ro'i

    2013-01-01

    How do people attribute responsibility in situations where the contributions of multiple agents combine to produce a joint outcome? The prevalence of over-determination in such cases makes this a difficult problem for counterfactual theories of causal responsibility. In this article, we explore a general framework for assigning responsibility in multiple agent contexts. We draw on the structural model account of actual causation (e.g., Halpern & Pearl, 2005) and its extension to responsibility judgments (Chockler & Halpern, 2004). We review the main theoretical and empirical issues that arise from this literature and propose a novel model of intuitive judgments of responsibility. This model is a function of both pivotality (whether an agent made a difference to the outcome) and criticality (how important the agent is perceived to be for the outcome, before any actions are taken). The model explains empirical results from previous studies and is supported by a new experiment that manipulates both pivotality and criticality. We also discuss possible extensions of this model to deal with a broader range of causal situations. Overall, our approach emphasizes the close interrelations between causality, counterfactuals, and responsibility attributions. PMID:23855451

  7. The New Member States: Austria, Finland, Sweden.

    ERIC Educational Resources Information Center

    Goetschy, Janine; And Others

    1995-01-01

    Includes "Difficult Metamorphosis of the Social 'Models' of the Nordic Countries" (Goetschy); "Swedish Training System" (Ottersten); "Features of Vocational Education in Finland" (Kyro); "Boom in Apprenticeship Training in Finland" (Vartiainen); "Vocational Training in Austria" (Riemer); "Reforms in the Vocational Education and Training Systems of…

  8. Abnormal causal connectivity by structural deficits in first-episode, drug-naive schizophrenia at rest.

    PubMed

    Guo, Wenbin; Liu, Feng; Liu, Jianrong; Yu, Liuyu; Zhang, Jian; Zhang, Zhikun; Xiao, Changqing; Zhai, Jinguo; Zhao, Jingping

    2015-01-01

    Anatomical deficits and resting-state functional connectivity (FC) alterations in prefrontal-thalamic-cerebellar circuit have been implicated in the neurobiology of schizophrenia. However, the effect of structural deficits in schizophrenia on causal connectivity of this circuit remains unclear. This study was conducted to examine the causal connectivity biased by structural deficits in first-episode, drug-naive schizophrenia patients. Structural and resting-state functional magnetic resonance imaging (fMRI) data were obtained from 49 first-episode, drug-naive schizophrenia patients and 50 healthy controls. Data were analyzed by voxel-based morphometry and Granger causality analysis. The causal connectivity of the integrated prefrontal-thalamic (limbic)-cerebellar (sensorimotor) circuit was partly affected by structural deficits in first-episode, drug-naive schizophrenia as follows: (1) unilateral prefrontal-sensorimotor connectivity abnormalities (increased driving effect from the left medial prefrontal cortex [MPFC] to the sensorimotor regions); (2) bilateral limbic-sensorimotor connectivity abnormalities (increased driving effect from the right anterior cingulate cortex [ACC] to the sensorimotor regions and decreased feedback from the sensorimotor regions to the right ACC); and (3) bilateral increased and decreased causal connectivities among the sensorimotor regions. Some correlations between the gray matter volume of the seeds, along with their causal effects and clinical variables (duration of untreated psychosis and symptom severity), were also observed in the patients. The findings indicated the partial effects of structural deficits in first-episode, drug-naive schizophrenia on the prefrontal-thalamic (limbic)-cerebellar (sensorimotor) circuit. Schizophrenia may reinforce the driving connectivities from the left MPFC or right ACC to the sensorimotor regions and may disrupt bilateral causal connectivities among the sensorimotor regions.

  9. Does sunspot numbers cause global temperatures? A reconsideration using non-parametric causality tests

    NASA Astrophysics Data System (ADS)

    Hassani, Hossein; Huang, Xu; Gupta, Rangan; Ghodsi, Mansi

    2016-10-01

    In a recent paper, Gupta et al., (2015), analyzed whether sunspot numbers cause global temperatures based on monthly data covering the period 1880:1-2013:9. The authors find that standard time domain Granger causality test fails to reject the null hypothesis that sunspot numbers do not cause global temperatures for both full and sub-samples, namely 1880:1-1936:2, ​1936:3-1986:11 and 1986:12-2013:9 (identified based on tests of structural breaks). However, frequency domain causality test detects predictability for the full-sample at short (2-2.6 months) cycle lengths, but not the sub-samples. But since, full-sample causality cannot be relied upon due to structural breaks, Gupta et al., (2015) conclude that the evidence of causality running from sunspot numbers to global temperatures is weak and inconclusive. Given the importance of the issue of global warming, our current paper aims to revisit this issue of whether sunspot numbers cause global temperatures, using the same data set and sub-samples used by Gupta et al., (2015), based on an nonparametric Singular Spectrum Analysis (SSA)-based causality test. Based on this test, we however, show that sunspot numbers have predictive ability for global temperatures for the three sub-samples, over and above the full-sample. Thus, generally speaking, our non-parametric SSA-based causality test outperformed both time domain and frequency domain causality tests and highlighted that sunspot numbers have always been important in predicting global temperatures.

  10. Disrupted causal connectivity anchored on the anterior cingulate cortex in first-episode medication-naive major depressive disorder.

    PubMed

    Feng, Zhan; Xu, Shunliang; Huang, Manli; Shi, Yushu; Xiong, Bing; Yang, Hong

    2016-01-01

    In recent years, major depressive disorder (MDD) has been demonstrated to be associated with abnormalities in neural networks, particularly the prefrontal-limbic network (PLN). However, there are few current studies that have examined information flow in the PLN. In this study, Granger causality analysis (GCA), based on signed regression coefficient, was used to explore changes in causal connectivity in resting-state PLNs of MDD patients. A total of 23 first-episode medication-naïve MDD patients and 20 normal control participants were subjected to resting-state functional magnetic resonance imaging (RS-fMRI) scans. Increased causal effects of the right insular cortex, right putamen and right caudate on the rostral anterior cingulate cortex (rACC) and reduced causal effects of bilateral dorsolateral prefrontal cortex (DLPFC) and left orbitofrontal cortex (OFC) on the rACC were found in MDD patients compared to normal controls. The extensive reduction in the causal effect of the prefrontal cortex (PFC) demonstrates impaired top-down cognitive control in MDD patients. Changes in the causal relationship between the right insula and rACC suggest problems in coordination of the default mode network by the right anterior insular cortex (rAI). These findings provide valuable insight into MDD-related neural network disorders reported in previous RS-fMRI studies and may potentially guide clinical treatment of MDD in the future. PMID:26234517

  11. Structure and Connectivity Analysis of Financial Complex System Based on G-Causality Network

    NASA Astrophysics Data System (ADS)

    Xu, Chuan-Ming; Yan, Yan; Zhu, Xiao-Wu; Li, Xiao-Teng; Chen, Xiao-Song

    2013-11-01

    The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.

  12. Causal Entropic Forces

    NASA Astrophysics Data System (ADS)

    Wissner-Gross, A. D.; Freer, C. E.

    2013-04-01

    Recent advances in fields ranging from cosmology to computer science have hinted at a possible deep connection between intelligence and entropy maximization, but no formal physical relationship between them has yet been established. Here, we explicitly propose a first step toward such a relationship in the form of a causal generalization of entropic forces that we find can cause two defining behaviors of the human “cognitive niche”—tool use and social cooperation—to spontaneously emerge in simple physical systems. Our results suggest a potentially general thermodynamic model of adaptive behavior as a nonequilibrium process in open systems.

  13. Quantum information causality.

    PubMed

    Pitalúa-García, Damián

    2013-05-24

    How much information can a transmitted physical system fundamentally communicate? We introduce the principle of quantum information causality, which states the maximum amount of quantum information that a quantum system can communicate as a function of its dimension, independently of any previously shared quantum physical resources. We present a new quantum information task, whose success probability is upper bounded by the new principle, and show that an optimal strategy to perform it combines the quantum teleportation and superdense coding protocols with a task that has classical inputs. PMID:23745844

  14. Fast causal multicast

    NASA Technical Reports Server (NTRS)

    Birman, Kenneth P.; Schiper, Andre; Stephenson, Pat

    1990-01-01

    A new protocol is presented that efficiently implements a reliable, causally ordered multicast primitive and is easily extended into a totally ordered one. Intended for use in the ISIS toolkit, it offers a way to bypass the most costly aspects of ISIS while benefiting from virtual synchrony. The facility scales with bounded overhead. Measured speedups of more than an order of magnitude were obtained when the protocol was implemented within ISIS. One conclusion is that systems such as ISIS can achieve performance competitive with the best existing multicast facilities - a finding contradicting the widespread concern that fault-tolerance may be unacceptably costly.

  15. Learning a theory of causality.

    PubMed

    Goodman, Noah D; Ullman, Tomer D; Tenenbaum, Joshua B

    2011-01-01

    The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be learned from co-occurrence of events. We begin by phrasing the causal Bayes nets theory of causality and a range of alternatives in a logical language for relational theories. This allows us to explore simultaneous inductive learning of an abstract theory of causality and a causal model for each of several causal systems. We find that the correct theory of causality can be learned relatively quickly, often becoming available before specific causal theories have been learned--an effect we term the blessing of abstraction. We then explore the effect of providing a variety of auxiliary evidence and find that a collection of simple perceptual input analyzers can help to bootstrap abstract knowledge. Together, these results suggest that the most efficient route to causal knowledge may be to build in not an abstract notion of causality but a powerful inductive learning mechanism and a variety of perceptual supports. While these results are purely computational, they have implications for cognitive development, which we explore in the conclusion.

  16. Photodetection and causality I

    NASA Astrophysics Data System (ADS)

    de Haan, M.

    1985-09-01

    We investigate in this paper the link between the measurement process in quantum mechanics and casuality associated to a finite velocity of field propagation. We use models of interaction of a bound state with a scalar field. We first show that in the existing approaches, time delays resulting from the finite velocity of propagation have been obtained only as a consequence of ad hoc approximations. We show that exact causality can be restored in the case of a single photodetection by a slight change of the observable associated to the measurement process. Moreover, this modification may be justified by the introduction of a simple model for the photodetector. We present qualitative arguments to show that this procedure cannot be extended to the case of multiple photodetections. The process of repeated photodetection clashes therefore with causality. This paradox is closely related to the Zeno paradox described by Misra and Sudarshan 1). Both may be traced back to the positive definite character of the hamiltonian which is the generator of motion in quantum mechanics.

  17. Experimental test of nonlocal causality

    PubMed Central

    Ringbauer, Martin; Giarmatzi, Christina; Chaves, Rafael; Costa, Fabio; White, Andrew G.; Fedrizzi, Alessandro

    2016-01-01

    Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell’s local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect. PMID:27532045

  18. Experimental test of nonlocal causality.

    PubMed

    Ringbauer, Martin; Giarmatzi, Christina; Chaves, Rafael; Costa, Fabio; White, Andrew G; Fedrizzi, Alessandro

    2016-08-01

    Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell's local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect. PMID:27532045

  19. Experimental test of nonlocal causality.

    PubMed

    Ringbauer, Martin; Giarmatzi, Christina; Chaves, Rafael; Costa, Fabio; White, Andrew G; Fedrizzi, Alessandro

    2016-08-01

    Explaining observations in terms of causes and effects is central to empirical science. However, correlations between entangled quantum particles seem to defy such an explanation. This implies that some of the fundamental assumptions of causal explanations have to give way. We consider a relaxation of one of these assumptions, Bell's local causality, by allowing outcome dependence: a direct causal influence between the outcomes of measurements of remote parties. We use interventional data from a photonic experiment to bound the strength of this causal influence in a two-party Bell scenario, and observational data from a Bell-type inequality test for the considered models. Our results demonstrate the incompatibility of quantum mechanics with a broad class of nonlocal causal models, which includes Bell-local models as a special case. Recovering a classical causal picture of quantum correlations thus requires an even more radical modification of our classical notion of cause and effect.

  20. Causal inference based on counterfactuals

    PubMed Central

    Höfler, M

    2005-01-01

    Background The counterfactual or potential outcome model has become increasingly standard for causal inference in epidemiological and medical studies. Discussion This paper provides an overview on the counterfactual and related approaches. A variety of conceptual as well as practical issues when estimating causal effects are reviewed. These include causal interactions, imperfect experiments, adjustment for confounding, time-varying exposures, competing risks and the probability of causation. It is argued that the counterfactual model of causal effects captures the main aspects of causality in health sciences and relates to many statistical procedures. Summary Counterfactuals are the basis of causal inference in medicine and epidemiology. Nevertheless, the estimation of counterfactual differences pose several difficulties, primarily in observational studies. These problems, however, reflect fundamental barriers only when learning from observations, and this does not invalidate the counterfactual concept. PMID:16159397

  1. Spatial-temporal causal modeling: a data centric approach to climate change attribution (Invited)

    NASA Astrophysics Data System (ADS)

    Lozano, A. C.

    2010-12-01

    Attribution of climate change has been predominantly based on simulations using physical climate models. These approaches rely heavily on the employed models and are thus subject to their shortcomings. Given the physical models’ limitations in describing the complex system of climate, we propose an alternative approach to climate change attribution that is data centric in the sense that it relies on actual measurements of climate variables and human and natural forcing factors. We present a novel class of methods to infer causality from spatial-temporal data, as well as a procedure to incorporate extreme value modeling into our methodology in order to address the attribution of extreme climate events. We develop a collection of causal modeling methods using spatio-temporal data that combine graphical modeling techniques with the notion of Granger causality. “Granger causality” is an operational definition of causality from econometrics, which is based on the premise that if a variable causally affects another, then the past values of the former should be helpful in predicting the future values of the latter. In its basic version, our methodology makes use of the spatial relationship between the various data points, but treats each location as being identically distributed and builds a unique causal graph that is common to all locations. A more flexible framework is then proposed that is less restrictive than having a single causal graph common to all locations, while avoiding the brittleness due to data scarcity that might arise if one were to independently learn a different graph for each location. The solution we propose can be viewed as finding a middle ground by partitioning the locations into subsets that share the same causal structures and pooling the observations from all the time series belonging to the same subset in order to learn more robust causal graphs. More precisely, we make use of relationships between locations (e.g. neighboring

  2. The Secret to Finland's Success: Educating Teachers. Research Brief

    ERIC Educational Resources Information Center

    Sahlberg, Pasi

    2010-01-01

    In the last decade, Finland has emerged as the leading OECD country in educational achievement. In examining the sources of Finland's dramatic rise to the top, research shows one key element that has impacted Finland's success above all others: excellent teachers. This policy brief details the key elements of Finland's successful system, examining…

  3. Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease.

    PubMed

    McBride, Joseph C; Zhao, Xiaopeng; Munro, Nancy B; Jicha, Gregory A; Schmitt, Frederick A; Kryscio, Richard J; Smith, Charles D; Jiang, Yang

    2015-01-01

    Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the detection of

  4. Structural Equations and Causal Explanations: Some Challenges for Causal SEM

    ERIC Educational Resources Information Center

    Markus, Keith A.

    2010-01-01

    One common application of structural equation modeling (SEM) involves expressing and empirically investigating causal explanations. Nonetheless, several aspects of causal explanation that have an impact on behavioral science methodology remain poorly understood. It remains unclear whether applications of SEM should attempt to provide complete…

  5. Causality violation and paradoxes

    NASA Astrophysics Data System (ADS)

    Krasnikov, S. V.

    1997-03-01

    Paradoxes that can supposedly occur if causality is violated are discussed. It is shown that the existence of ``trajectories of multiplicity zero'' (i.e., trajectories that describe, say, a ball hitting its younger self so that the latter cannot fall into the time machine) is not paradoxical by itself. This apparent paradox can be resolved (at least sometimes) without any harm to local physics or to the time machine. Also a simple model is adduced for which the absence of true paradoxes caused by self-interaction in an acausal world is proved. The conclusion is made that the paradoxes appear if and (within this model) only if the fact is neglected that no conditions fixed to the past of a time machine guarantee that a system remains isolated after it intersects the Cauchy horizon.

  6. Causal electromagnetic interaction equations

    SciTech Connect

    Zinoviev, Yury M.

    2011-02-15

    For the electromagnetic interaction of two particles the relativistic causal quantum mechanics equations are proposed. These equations are solved for the case when the second particle moves freely. The initial wave functions are supposed to be smooth and rapidly decreasing at the infinity. This condition is important for the convergence of the integrals similar to the integrals of quantum electrodynamics. We also consider the singular initial wave functions in the particular case when the second particle mass is equal to zero. The discrete energy spectrum of the first particle wave function is defined by the initial wave function of the free-moving second particle. Choosing the initial wave functions of the free-moving second particle it is possible to obtain a practically arbitrary discrete energy spectrum.

  7. A causality between fund performance and stock market

    NASA Astrophysics Data System (ADS)

    Kim, Ho-Yong; Kwon, Okyu; Oh, Gabjin

    2016-02-01

    We investigate whether the characteristic fund performance indicators (FPI), such as the fund return, the Net asset value (NAV) and the cash flow, are correlated with the asset price movement using information flows estimated by the Granger causality test. First, we find that the information flow of FPI is most sensitive to extreme events of the Korean stock market, which include negative events such as the sub-prime crisis and the impact of QE (quantitative easing) by the US subprime and Europe financial crisis as well as the positive events of the golden period of Korean Composite Stock Price Index (KOSPI), except for the fund cash flow. Second, both the fund return and the NAV exhibit significant correlations with the KOSPI, whereas the cash flow is not correlated with the stock market. This result suggests that the information resulting from the ability of the fund manager should influence stock market. Finally, during market crisis period, information flows between FPI and the Korean stock market are significantly positively correlated with the market volatility.

  8. University Mergers in Finland: Mediating Global Competition

    ERIC Educational Resources Information Center

    Välimaa, Jussi; Aittola, Helena; Ursin, Jani

    2014-01-01

    University mergers have become a common strategy for increasing global competitiveness. In this chapter, the authors analyze the implementation of mergers in Finnish universities from the perspective of social justice as conceived within Finland and other Nordic countries.

  9. Globally conditioned causality in estimating directed brain-heart interactions through joint MRI and RR series analysis.

    PubMed

    Duggento, Andrea; Bianciardi, Marta; Wald, Lawrence L; Passamonti, Luca; Guerrisi, Maria; Barbieri, Riccardo; Toschi, Nicola

    2015-08-01

    We used 7T fMRI with simultaneous physiological signals acquisitions to investigate the causal interactions from resting state brain activity to autonomic nervous system (ANS) outflow as quantified through a probabilistic heartbeat model. Given the highly redundant nature of brain-derived signals, we compare the results of traditional bivariate Granger Causality (GC) to a globally conditioned approach which evaluates the additional influence of each brain region on ANS activity while factoring out effects concomitantly mediated by other brain regions. The bivariate approach results in an unrealistically large number of spurious causal brain-heart links. In contrast, using the globally conditioned approac, we demonstrate the existence of significant selective causal links between cortical/subcortical brain regions and ANS outflow for sympathetic and parasympathetic modulation as well as sympathovagal balance, with a prominent involvement of frontal, parietal, and cerebellar regions and Sensory Motor, Default Mode, Left and Right executive networks. Provided proper conditioning is employed to eliminate spurious causalities, 7T functional imaging coupled with physiological signal acquisition and GC analysis is able to quantify directed brain-heart interactions reflecting central modulation of ANS outflow.

  10. Expert Causal Reasoning and Explanation.

    ERIC Educational Resources Information Center

    Kuipers, Benjamin

    The relationship between cognitive psychologists and researchers in artificial intelligence carries substantial benefits for both. An ongoing investigation in causal reasoning in medical problem solving systems illustrates this interaction. This paper traces a dialectic of sorts in which three different types of causal resaoning for medical…

  11. Theory-Based Causal Induction

    ERIC Educational Resources Information Center

    Griffiths, Thomas L.; Tenenbaum, Joshua B.

    2009-01-01

    Inducing causal relationships from observations is a classic problem in scientific inference, statistics, and machine learning. It is also a central part of human learning, and a task that people perform remarkably well given its notorious difficulties. People can learn causal structure in various settings, from diverse forms of data: observations…

  12. Causal Inference in Retrospective Studies.

    ERIC Educational Resources Information Center

    Holland, Paul W.; Rubin, Donald B.

    1988-01-01

    The problem of drawing causal inferences from retrospective case-controlled studies is considered. A model for causal inference in prospective studies is applied to retrospective studies. Limitations of case-controlled studies are formulated concerning relevant parameters that can be estimated in such studies. A coffee-drinking/myocardial…

  13. Causal Inference and Developmental Psychology

    ERIC Educational Resources Information Center

    Foster, E. Michael

    2010-01-01

    Causal inference is of central importance to developmental psychology. Many key questions in the field revolve around improving the lives of children and their families. These include identifying risk factors that if manipulated in some way would foster child development. Such a task inherently involves causal inference: One wants to know whether…

  14. The Development of Causal Categorization

    ERIC Educational Resources Information Center

    Hayes, Brett K.; Rehder, Bob

    2012-01-01

    Two experiments examined the impact of causal relations between features on categorization in 5- to 6-year-old children and adults. Participants learned artificial categories containing instances with causally related features and noncausal features. They then selected the most likely category member from a series of novel test pairs.…

  15. Model-free causality analysis of cardiovascular variability detects the amelioration of autonomic control in Parkinson's disease patients undergoing mechanical stimulation.

    PubMed

    Bassani, Tito; Bari, Vlasta; Marchi, Andrea; Tassin, Stefano; Dalla Vecchia, Laura; Canesi, Margherita; Barbic, Franca; Furlan, Raffaello; Porta, Alberto

    2014-07-01

    We tested the hypothesis that causality analysis, applied to the spontaneous beat-to-beat variability of heart period (HP) and systolic arterial pressure (SAP), can identify the improvement of autonomic control linked to plantar mechanical stimulation in patients with Parkinson's disease (PD). A causality index, measuring the strength of the association from SAP to HP variability, and derived according to the Granger paradigm (i.e. SAP causes HP if the inclusion of SAP into the set of signals utilized to describe cardiovascular interactions improves the prediction of HP series), was calculated using both linear model-based (MB) and nonlinear model-free (MF) approaches. Univariate HP and SAP variability indices in time and frequency domains, and bivariate descriptors of the HP-SAP variability interactions were computed as well. We studied ten PD patients (age range: 57-78 years; Hoehn-Yahr scale: 2-3; six males, four females) without orthostatic hypotension or symptoms of orthostatic intolerance and 'on-time' according to their habitual pharmacological treatment. PD patients underwent recordings at rest in a supine position and during a head-up tilt before, and 24 h after, mechanical stimulation was applied to the plantar surface of both feet. The MF causality analysis indicated a greater involvement of baroreflex in regulating HP-SAP variability interactions after mechanical stimulation. Remarkably, MB causality and more traditional univariate or bivariate techniques could not detect changes in cardiovascular regulation after mechanical stimulation, thus stressing the importance of accounting for nonlinear dynamics in PD patients. Due to the higher statistical power of MF causality we suggest its exploitation to monitor the baroreflex control improvement in PD patients, and we encourage the clinical application of the Granger causality approach to evaluate the modification of the autonomic control in relation to the application of a pharmacological treatment, a

  16. On causality of extreme events

    PubMed Central

    2016-01-01

    Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available. PMID:27330866

  17. [Causality: risk factors and interventions].

    PubMed

    Dekkers, Olaf M; Vandenbroucke, Jan P

    2013-01-01

    A risk factor has a causal effect on a disease when the disease would not have occurred in the absence of the risk factor. Analogous reasoning applies to the effect of a particular therapy. Thinking in terms of contrasts is fundamental to causal reasoning in medicine. The contrast determines the content of the causal claim; the most important assumption here is that the prognosis between groups is comparable. Causal effects of risk factors are not always the same as the causal effect of an intervention: removal of a risk factor (e.g. smoking) for a disease does not necessarily mean that the risk will subsequently normalize. A second problem is that risk factors cannot always easily be translated into interventions. This applies to factors that cannot be changed (e.g. gender) or that can have multiple causes themselves (e.g. obesity).

  18. On causality of extreme events.

    PubMed

    Zanin, Massimiliano

    2016-01-01

    Multiple metrics have been developed to detect causality relations between data describing the elements constituting complex systems, all of them considering their evolution through time. Here we propose a metric able to detect causality within static data sets, by analysing how extreme events in one element correspond to the appearance of extreme events in a second one. The metric is able to detect non-linear causalities; to analyse both cross-sectional and longitudinal data sets; and to discriminate between real causalities and correlations caused by confounding factors. We validate the metric through synthetic data, dynamical and chaotic systems, and data representing the human brain activity in a cognitive task. We further show how the proposed metric is able to outperform classical causality metrics, provided non-linear relationships are present and large enough data sets are available.

  19. Apparent causality affects perceived simultaneity.

    PubMed

    Kohlrausch, Armin; van Eijk, Rob; Juola, James F; Brandt, Inge; van de Par, Steven

    2013-10-01

    The present research addresses the question of how visual predictive information and implied causality affect audio-visual synchrony perception. Previous research has shown a systematic shift in the likelihood of observers to accept audio-leading stimulus pairs as being apparently simultaneous in variants of audio-visual stimulus pairs that differ in (1) the amount of visual predictive information available and (2) the apparent causal relation between the auditory and visual components. An experiment was designed to separate the predictability and causality explanations, and the results indicated that shifts in subjective simultaneity were explained completely by changes in the implied causal relations in the stimuli and that predictability had no added value. Together with earlier findings, these results further indicate that the observed shifts in subjective simultaneity due to causal relations among auditory and visual events do not reflect a mere change in response strategy, but rather result from early multimodal integration processes in event perception.

  20. Differences in hemispherical thalamo-cortical causality analysis during resting-state fMRI.

    PubMed

    Anwar, Abdul Rauf; Muthalib, Makii; Perrey, Stephane; Wolff, Stephan; Deuschl, Guunther; Heute, Ulrich; Muthuraman, Muthuraman

    2014-01-01

    Thalamus is a very important part of the human brain. It has been reported to act as a relay for the messaging taking place between the cortical and sub-cortical regions of the brain. In the present study, we analyze the functional network between both hemispheres of the brain with the focus on thalamus. We used conditional Granger causality (CGC) and time-resolved partial directed coherence (tPDC) to investigate the functional connectivity. Results of CGC analysis revealed the asymmetry between connection strengths of the bilateral thalamus. Upon testing the functional connectivity of the default-mode network (DMN) at low-frequency fluctuations (LFF) and comparing coherence vectors using Spearman's rank correlation, we found that thalamus is a better source for the signals directed towards the contralateral regions of the brain, however, when thalamus acts as sink, it is a better sink for signals generated from ipsilateral regions of the brain.

  1. CausalTrail: Testing hypothesis using causal Bayesian networks

    PubMed Central

    Trampert, Patrick; Lenhof, Hans-Peter

    2015-01-01

    Summary Causal Bayesian Networks are a special class of Bayesian networks in which the hierarchy directly encodes the causal relationships between the variables. This allows to compute the effect of interventions, which are external changes to the system, caused by e.g. gene knockouts or an administered drug. Whereas numerous packages for constructing causal Bayesian networks are available, hardly any program targeted at downstream analysis exists. In this paper we present CausalTrail, a tool for performing reasoning on causal Bayesian networks using the do-calculus. CausalTrail's features include multiple data import methods, a flexible query language for formulating hypotheses, as well as an intuitive graphical user interface. The program is able to account for missing data and thus can be readily applied in multi-omics settings where it is common that not all measurements are performed for all samples. Availability and Implementation CausalTrail is implemented in C++ using the Boost and Qt5 libraries. It can be obtained from https://github.com/dstoeckel/causaltrail PMID:26913195

  2. The Visual Causality Analyst: An Interactive Interface for Causal Reasoning.

    PubMed

    Wang, Jun; Mueller, Klaus

    2016-01-01

    Uncovering the causal relations that exist among variables in multivariate datasets is one of the ultimate goals in data analytics. Causation is related to correlation but correlation does not imply causation. While a number of casual discovery algorithms have been devised that eliminate spurious correlations from a network, there are no guarantees that all of the inferred causations are indeed true. Hence, bringing a domain expert into the casual reasoning loop can be of great benefit in identifying erroneous casual relationships suggested by the discovery algorithm. To address this need we present the Visual Causal Analyst-a novel visual causal reasoning framework that allows users to apply their expertise, verify and edit causal links, and collaborate with the causal discovery algorithm to identify a valid causal network. Its interface consists of both an interactive 2D graph view and a numerical presentation of salient statistical parameters, such as regression coefficients, p-values, and others. Both help users in gaining a good understanding of the landscape of causal structures particularly when the number of variables is large. Our framework is also novel in that it can handle both numerical and categorical variables within one unified model and return plausible results. We demonstrate its use via a set of case studies using multiple practical datasets.

  3. Causal Rasch models

    PubMed Central

    Stenner, A. Jackson; Fisher, William P.; Stone, Mark H.; Burdick, Donald S.

    2013-01-01

    Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained. PMID:23986726

  4. History, causality, and sexology.

    PubMed

    Money, John

    2003-08-01

    In 1896, Krafft-Ebing published Psychopathia Sexualis. Popularly defined as hereditary weakness or taintedness in the family pedigree, degeneracy was called upon as a causal explanation for perversions of the sexual instinct. Although Krafft-Ebing accepted Karl Ulrichs proposal that homosexuality could be innate and probably located in the brain, he paid little attention to neuropathological sexology. Alfred Binet challenged Krafft-Ebing's orthodoxy by explaining fetishism in terms of associative learning, to which Krafft-Ebing's response was that only those with a hereditary taint would be vulnerable. Thus did the venerable nature-nurture antithesis maintain its rhetoric, even to the present day. Krafft-Ebing died too soon to meet the Freudian challenge of endopsychic determinism, and too soon also to encounter the idea of a developmental multivariate outcome of what I have termed the lovemap. Like other brain maps, for example the languagemap, the lovemap requires an intact human brain in which to develop. The personalized content of the lovemap has access to the brain by way of the special senses.

  5. Asbestos and cancer in Finland.

    PubMed

    Huuskonen, M S; Karjalainen, A; Tossavainen, A; Rantanen, J

    1995-01-01

    Primary prevention carried out today can reduce the disease incidence in the future decades. The present disease panorama is the consequence of past asbestos exposure mainly before the 1970s. The peak incidence of asbestos-induced diseases will be reached around 2010 in Finland. The number of asbestos-related premature deaths is at present annually about 150 which exceeds the figure of fatal work accidents. Asbestos-related cancer will increase still for 15-20 years and reach its maximum, about 300 cases, in 2010, and will start to decrease after that. More than 20,000 asbestos-exposed workers have participated in the medical screening and follow-up. The termination of exposure, antismoking campaigns, improved diagnostics and careful attention to compensation issues, as well as other potentials for prevention, were the central issue of the Asbestos Program of the Finnish Institute of Occupational Health. An important objective of research work is to improve early diagnostics, and thereby treatment prospects, in case of asbestos-induced cancers.

  6. Human causal discovery from observational data.

    PubMed Central

    Hashem, A. I.; Cooper, G. F.

    1996-01-01

    Utilizing Bayesian belief networks as a model of causality, we examined medical students' ability to discover causal relationships from observational data. Nine sets of patient cases were generated from relatively simple causal belief networks by stochastic simulation. Twenty participants examined the data sets and attempted to discover the underlying causal relationships. Performance was poor in general, except at discovering the absence of a causal relationship. This work supports the potential for combining human and computer methods for causal discovery. PMID:8947621

  7. Local Activity and Causal Connectivity in Children with Benign Epilepsy with Centrotemporal Spikes

    PubMed Central

    Zang, Yu-Feng; Liao, Wei; Jin, Zhen; Liu, Ya-Li; Li, Ke; Zeng, Ya-Wei; Fang, Fang

    2015-01-01

    The aim of the current study was to localize the epileptic focus and characterize its causal relation with other brain regions, to understand the cognitive deficits in children with benign childhood epilepsy with centrotemporal spikes (BECTS). Resting-state functional magnetic resonance imaging (fMRI) was performed in 37 children with BECTS and 25 children matched for age, sex and educational achievement. We identified the potential epileptogenic zone (EZ) by comparing the amplitude of low frequency fluctuation (ALFF) of spontaneous blood oxygenation level dependent fMRI signals between the groups. Granger causality analysis was applied to explore the causal effect between EZ and the whole brain. Compared with controls, children with BECTS had significantly increased ALFF in the right postcentral gyrus and bilateral calcarine, and decreased ALFF in the left anterior cingulate cortex, bilateral putaman/caudate, and left cerebellum. ALFF values in the putaman/caudate were positively correlated with verbal IQ scores in patients. The ALFF values in cerebellum and performance IQ scores were negatively correlated in patients. These results suggest that ALFF disturbances in the putaman/caudate and cerebellum play an important role in BECTS cognitive dysfunction. Compared with controls, the patients showed increased driving effect from the EZ to the right medial frontal cortex and posterior cingulate cortex and decreased causal effects from the EZ to left inferior frontal gyrus. The causal effect of the left inferior frontal gyrus negatively correlated with disease duration, which suggests a relation between the epileptiform activity and language impairment. All together, these findings provide additional insight into the neurophysiological mechanisms of epilepitogenisis and cognitive dysfunction associated with BECTS. PMID:26225427

  8. Causal learning with local computations.

    PubMed

    Fernbach, Philip M; Sloman, Steven A

    2009-05-01

    The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require relatively small amounts of data, and need not respect normative prescriptions as inferences that are principled locally may violate those principles when combined. Over a series of 3 experiments, the authors found (a) systematic inferences from small amounts of data; (b) systematic inference of extraneous causal links; (c) influence of data presentation order on inferences; and (d) error reduction through pretraining. Without pretraining, a model based on local computations fitted data better than a Bayesian structural inference model. The data suggest that local computations serve as a heuristic for learning causal structure.

  9. Causal Inference in Public Health

    PubMed Central

    Glass, Thomas A.; Goodman, Steven N.; Hernán, Miguel A.; Samet, Jonathan M.

    2014-01-01

    Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. We argue that in public health this framework is more suitable, providing an estimate of an action’s consequences rather than the less precise notion of a risk factor’s causal effect. A variety of modern statistical methods adopt this approach. When an intervention cannot be specified, causal relations can still exist, but how to intervene to change the outcome will be unclear. In application, the often-complex structure of causal processes needs to be acknowledged and appropriate data collected to study them. These newer approaches need to be brought to bear on the increasingly complex public health challenges of our globalized world. PMID:23297653

  10. On the causal links between health indicator, output, combustible renewables and waste consumption, rail transport, and CO2 emissions: the case of Tunisia.

    PubMed

    Ben Jebli, Mehdi

    2016-08-01

    This study employs the autoregressive distributed lag (ARDL) approach and Granger causality test to investigate the short- and long-run relationships between health indicator, real GDP, combustible renewables and waste consumption, rail transport, and carbon dioxide (CO2) emissions for the case of Tunisia, spanning the period of 1990-2011. The empirical findings suggest that the Fisher statistic of the Wald test confirm the existence of a long-run relationship between the variables. Moreover, the long-run estimated elasticities of the ARDL model provide that output and combustible renewables and waste consumption have a positive and statistically significant impact on health situation, while CO2 emissions and rail transport both contribute to the decrease of health indicator. Granger causality results affirm that, in the short-run, there is a unidirectional causality running from real GDP to health, a unidirectional causality from health to combustible renewables and waste consumption, and a unidirectional causality from all variables to CO2 emissions. In the long-run, all the computed error correction terms are significant and confirm the existence of long-run association among the variables. Our recommendations for the Tunisian policymakers are as follows: (i) exploiting wastes and renewable fuels can be a good strategy to eliminate pollution caused by emissions and subsequently improve health quality, (ii) the use of renewable energy as a main source for national rail transport is an effective strategy for public health, (iii) renewable energy investment projects are beneficial plans for the country as this contributes to the growth of its own economy and reduce energy dependence, and (iii) more renewable energy consumption leads not only to decrease pollution but also to stimulate health situation because of the increase of doctors and nurses numbers.

  11. On the causal links between health indicator, output, combustible renewables and waste consumption, rail transport, and CO2 emissions: the case of Tunisia.

    PubMed

    Ben Jebli, Mehdi

    2016-08-01

    This study employs the autoregressive distributed lag (ARDL) approach and Granger causality test to investigate the short- and long-run relationships between health indicator, real GDP, combustible renewables and waste consumption, rail transport, and carbon dioxide (CO2) emissions for the case of Tunisia, spanning the period of 1990-2011. The empirical findings suggest that the Fisher statistic of the Wald test confirm the existence of a long-run relationship between the variables. Moreover, the long-run estimated elasticities of the ARDL model provide that output and combustible renewables and waste consumption have a positive and statistically significant impact on health situation, while CO2 emissions and rail transport both contribute to the decrease of health indicator. Granger causality results affirm that, in the short-run, there is a unidirectional causality running from real GDP to health, a unidirectional causality from health to combustible renewables and waste consumption, and a unidirectional causality from all variables to CO2 emissions. In the long-run, all the computed error correction terms are significant and confirm the existence of long-run association among the variables. Our recommendations for the Tunisian policymakers are as follows: (i) exploiting wastes and renewable fuels can be a good strategy to eliminate pollution caused by emissions and subsequently improve health quality, (ii) the use of renewable energy as a main source for national rail transport is an effective strategy for public health, (iii) renewable energy investment projects are beneficial plans for the country as this contributes to the growth of its own economy and reduce energy dependence, and (iii) more renewable energy consumption leads not only to decrease pollution but also to stimulate health situation because of the increase of doctors and nurses numbers. PMID:27180840

  12. Temperature, Not Fine Particulate Matter (PM2.5), is Causally Associated with Short-Term Acute Daily Mortality Rates: Results from One Hundred United States Cities

    PubMed Central

    Cox, Tony; Popken, Douglas; Ricci, Paolo F

    2013-01-01

    Exposures to fine particulate matter (PM2.5) in air (C) have been suspected of contributing causally to increased acute (e.g., same-day or next-day) human mortality rates (R). We tested this causal hypothesis in 100 United States cities using the publicly available NMMAPS database. Although a significant, approximately linear, statistical C-R association exists in simple statistical models, closer analysis suggests that it is not causal. Surprisingly, conditioning on other variables that have been extensively considered in previous analyses (usually using splines or other smoothers to approximate their effects), such as month of the year and mean daily temperature, suggests that they create strong, nonlinear confounding that explains the statistical association between PM2.5 and mortality rates in this data set. As this finding disagrees with conventional wisdom, we apply several different techniques to examine it. Conditional independence tests for potential causation, non-parametric classification tree analysis, Bayesian Model Averaging (BMA), and Granger-Sims causality testing, show no evidence that PM2.5 concentrations have any causal impact on increasing mortality rates. This apparent absence of a causal C-R relation, despite their statistical association, has potentially important implications for managing and communicating the uncertain health risks associated with, but not necessarily caused by, PM2.5 exposures. PMID:23983662

  13. New records of the genus Iporhogas Granger (Hymenoptera, Braconidae, Rogadinae) from Vietnam, with description of four new species

    PubMed Central

    Long, Khuat Dang

    2014-01-01

    Abstract The genus Iporhogas Granger, 1949 (Braconidae: Rogadinae) is recorded for the first time for Vietnam. Four new species of the genus Iporhogas, viz. Iporhogas albilateralis sp. n., I. contrastus sp. n., I. simulatus sp. n. and I. tricoloratus sp. n., from Vietnam are described and illustrated, and additionally, one species, Iporhogas guangxiensis Chen & He, 1997, is newly recorded for Vietnam’s fauna of the family Braconidae. A key to the five Vietnamese species of the genus Iporhogas and a checklist with distributions of the ten species are provided. PMID:25161368

  14. Finland Becomes Eleventh ESO Member State

    NASA Astrophysics Data System (ADS)

    2004-07-01

    Finland has become the eleventh member state of the European Southern Observatory (ESO) [1]. The formal accession procedure was carried through as planned and has now been completed. Following the signing of the corresponding Agreement earlier this year (ESO PR 02/04), acceptance by the Finnish Parliament and ratification by the Finnish President of the Agreement as well as the ESO Convention and the associated protocols in June [2] and the deposit of the instruments of accession today, Finland has now officially joined ESO. ESO warmly welcomes the new member country and its scientific community that is renowned for their expertise in many frontline areas. The related opportunities will contribute to strenghtening of pioneering research with the powerful facilities at ESO's observatories, to the benefit of Astronomy and Astrophysics as well as European science in general. ESO also looks forward to collaboration with the Finnish high-tech industry. For Finland, the membership in ESO is motivated by scientific and technological objectives as well as by the objective of improving the public understanding of science. The Finnish Government is committed to increasing the public research funding in order to improve the quality, impact and internationalisation of research. Membership in ESO offers unique facilities for astronomical research which would not otherwise be available for Finnish astronomers. Finland is also very interested in taking part in technological development projects in fields like ICT, optics and instrumentation. For young scientists and engineers, ESO is a challenging, international working and learning environment. Finland has already taken part in the educational programmes of ESO, and as a member this activity will be broadened and intensified. In Finland there are also several science journalists and a large community of amateur astronomers who will be very happy to take part in ESO's outreach activities.

  15. [Endemic cryptosporidiosis--underdiagnosed disease in Finland].

    PubMed

    Autio, Tiina; Karhukorpi, Jari; Mäkelä, Mauno; Meri, Taru; Savolainen, Sami; Rimhanen-Finne, Ruska

    2012-01-01

    Acute diarrhea caused by Cryptosporidium-protozoan is rarely diagnosed in Finland. The infection is usually self-limited and does not require antimicrobial treatment. Cryptosporidiosis, like other intestinal parasite infections, is mostly associated with travelling, but may also cause large waterborne epidemics. Contact with infected calves may be a source of cryptosporidiosis also in Finland. Cryptosporidiosis should be considered in patients suffering from severe or long-lasting watery diarrhea. We describe three cases of cryptosporidiosis, originating from infected calves. These cases show that verification of the etiology of human cryptosporidiosis associated with calves may be difficult and demands collaboration of clinicians, laboratories and veterinarians.

  16. Update on women in physics in Finland

    NASA Astrophysics Data System (ADS)

    Miikkulainen, Kukka; Vapaavuori, Jaana

    2015-12-01

    Despite Finland's role as a forerunner in many areas of gender equality, in the field of physics, the advancement of females to reach the full gender equality has been stagnated for the past decade, and no significant improvements since 2011 can be reported. However, a few interesting PhD theses have focused on gaining a better understanding of the phenomena, and a few seminars on the topic have been organized. However, good, systematically collected statistics on the numbers and salaries of female researches in Finland are still lacking.

  17. Different Kinds of Causality in Event Cognition

    ERIC Educational Resources Information Center

    Radvansky, Gabriel A.; Tamplin, Andrea K.; Armendarez, Joseph; Thompson, Alexis N.

    2014-01-01

    Narrative memory is better for information that is more causally connected and occurs at event boundaries, such as a causal break. However, it is unclear whether there are common or distinct influences of causality. For the event boundaries that arise as a result of causal breaks, the events that follow may subsequently become more causally…

  18. Causal reasoning with mental models

    PubMed Central

    Khemlani, Sangeet S.; Barbey, Aron K.; Johnson-Laird, Philip N.

    2014-01-01

    This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations. It reviews evidence both to corroborate the theory and to account for phenomena sometimes taken to be incompatible with it. Finally, it reviews neuroscience evidence indicating that mental models for causal inference are implemented within lateral prefrontal cortex. PMID:25389398

  19. Causal inference from observational data.

    PubMed

    Listl, Stefan; Jürges, Hendrik; Watt, Richard G

    2016-10-01

    Randomized controlled trials have long been considered the 'gold standard' for causal inference in clinical research. In the absence of randomized experiments, identification of reliable intervention points to improve oral health is often perceived as a challenge. But other fields of science, such as social science, have always been challenged by ethical constraints to conducting randomized controlled trials. Methods have been established to make causal inference using observational data, and these methods are becoming increasingly relevant in clinical medicine, health policy and public health research. This study provides an overview of state-of-the-art methods specifically designed for causal inference in observational data, including difference-in-differences (DiD) analyses, instrumental variables (IV), regression discontinuity designs (RDD) and fixed-effects panel data analysis. The described methods may be particularly useful in dental research, not least because of the increasing availability of routinely collected administrative data and electronic health records ('big data'). PMID:27111146

  20. Fluctuations in relativistic causal hydrodynamics

    NASA Astrophysics Data System (ADS)

    Kumar, Avdhesh; Bhatt, Jitesh R.; Mishra, Ananta P.

    2014-05-01

    Formalism to calculate the hydrodynamic fluctuations by applying the Onsager theory to the relativistic Navier-Stokes equation is already known. In this work, we calculate hydrodynamic fluctuations within the framework of the second order hydrodynamics of Müller, Israel and Stewart and its generalization to the third order. We have also calculated the fluctuations for several other causal hydrodynamical equations. We show that the form for the Onsager-coefficients and form of the correlation functions remain the same as those obtained by the relativistic Navier-Stokes equation and do not depend on any specific model of hydrodynamics. Further we numerically investigate evolution of the correlation function using the one dimensional boost-invariant (Bjorken) flow. We compare the correlation functions obtained using the causal hydrodynamics with the correlation function for the relativistic Navier-Stokes equation. We find that the qualitative behavior of the correlation functions remains the same for all the models of the causal hydrodynamics.

  1. Equality and Cooperation: Finland's Path to Excellence

    ERIC Educational Resources Information Center

    Sarjala, Jukka

    2013-01-01

    For the past decade, Finland has been lauded for consistently being a top performer on international assessments of student achievement. Having spent 25 years in the Ministry of Education, and then another 8 as director general of the National Board of Education, the author was heartened by these accomplishment--but he is also concerned about how…

  2. Mathematics Lessons from Finland and Sweden

    ERIC Educational Resources Information Center

    Seaberg, Rebecca L.

    2015-01-01

    In many ways, mathematics classrooms in Finland and Sweden are very similar to what would be considered traditional classrooms in the United States. Classes begin with checking homework and questions, followed by the teacher giving instruction in the new material, and end with students working on their new assignment. There are also interesting…

  3. The Professional Educator: Lessons from Finland

    ERIC Educational Resources Information Center

    Sahlberg, Pasi

    2011-01-01

    Since Finland emerged in 2000 as the top-scoring Organisation for Economic Co-operation and Development (OECD) nation on the Programme for International Student Assessment (PISA), researchers have been pouring into the country to study the so-called "Finnish miracle." How did a country with an undistinguished education system in the 1980s surge to…

  4. How Finland Serves Gifted and Talented Pupils

    ERIC Educational Resources Information Center

    Tirri, Kirsi; Kuusisto, Elina

    2013-01-01

    The purpose of this article is to provide an overview of the ways gifted and talented pupils are served in Finland. The trend toward individualism and freedom of choice as well as national policy affecting gifted education are discussed. Empirical research on Finnish teachers' attitudes toward gifted education with respect to the national…

  5. Deep drilling for geothermal energy in Finland

    NASA Astrophysics Data System (ADS)

    Kukkonen, Ilmo

    2016-04-01

    There is a societal request to find renewable CO2-free energy resources. One of the biggest such resources is provided by geothermal energy. In addition to shallow ground heat already extensively used in Finland, deep geothermal energy provides an alternative so far not exploited. Temperatures are high at depth, but the challenge is, how to mine the heat? In this presentation, the geological and geophysical conditions for deep geothermal energy production in Finland are discussed as well as challenges for drilling and conditions at depth for geothermal energy production. Finland is located on ancient bedrock with much lower temperatures than geologically younger volcanically and tectonically active areas. In order to reach sufficiently high temperatures drilling to depths of several kilometres are needed. Further, mining of the heat with, e.g., the principle of Enhanced Geothermal System (EGS) requires high hydraulic conductivity for efficient circulation of fluid in natural or artificial fractures of the rock. There are many issues that must be solved and/or improved: Drilling technology, the EGS concept, rock stress and hydraulic fracturing, scale formation, induced seismicity and ground movements, possible microbial activity, etc. An industry-funded pilot project currently in progress in southern Finland is shortly introduced.

  6. From Finland to Kyrgyzstan: A Changing Landscape

    ERIC Educational Resources Information Center

    Schleicher, Andreas K. R.

    2009-01-01

    In the most recent Programme for International Student Assessment of science learning, the equivalent of six school years separate the achievement of 15-year-olds in Finland, the best-performing country, from their counterparts in Kyrgyzstan, a former Soviet republic. Still more than a school year lies between the neighboring countries Canada,…

  7. Reasoning about Causal Relationships: Inferences on Causal Networks

    PubMed Central

    Rottman, Benjamin Margolin; Hastie, Reid

    2013-01-01

    Over the last decade, a normative framework for making causal inferences, Bayesian Probabilistic Causal Networks, has come to dominate psychological studies of inference based on causal relationships. The following causal networks—[X→Y→Z, X←Y→Z, X→Y←Z]—supply answers for questions like, “Suppose both X and Y occur, what is the probability Z occurs?” or “Suppose you intervene and make Y occur, what is the probability Z occurs?” In this review, we provide a tutorial for how normatively to calculate these inferences. Then, we systematically detail the results of behavioral studies comparing human qualitative and quantitative judgments to the normative calculations for many network structures and for several types of inferences on those networks. Overall, when the normative calculations imply that an inference should increase, judgments usually go up; when calculations imply a decrease, judgments usually go down. However, two systematic deviations appear. First, people’s inferences violate the Markov assumption. For example, when inferring Z from the structure X→Y→Z, people think that X is relevant even when Y completely mediates the relationship between X and Z. Second, even when people’s inferences are directionally consistent with the normative calculations, they are often not as sensitive to the parameters and the structure of the network as they should be. We conclude with a discussion of productive directions for future research. PMID:23544658

  8. Inferring functional relationships and causal network structure from gene expression profiles.

    PubMed

    Nagarajan, Radhakrishnan; Upreti, Meenakshi

    2011-01-01

    Inferring functional relationships and network structure from the observed gene expression profiles can provide a novel insight into the working of the genes as a system or network as opposed to independent entities. Such networks may also represent possible causal relationships between a given set of genes, hence can prove to be a convenient abstraction of the underlying signaling mechanism. The discovery of functional relationships from the observed gene expression profiles does not rely on prior literature, hence useful in identifying undocumented relationships between a given set of genes. Several techniques have been proposed in the literature. The present study investigates the choice Granger causality (GC) and its extensions in modeling the network structure between a given pair of genes from their expression profiles. The impact of noise variance on GC relationships is investigated. VAR parameter estimation is proposed to obtain a finer insight into the functional relationships inferred using GC tests. The results are presented on synthetic networks generated from known vector-autoregressive (VAR) models and those from cell-cycle gene expression profiles that can be modeled as a first-order bivariate VAR.

  9. Increased Causal Connectivity Related to Anatomical Alterations as Potential Endophenotypes for Schizophrenia

    PubMed Central

    Guo, Wenbin; Liu, Feng; Xiao, Changqing; Yu, Miaoyu; Zhang, Zhikun; Liu, Jianrong; Zhang, Jian; Zhao, Jingping

    2015-01-01

    Abstract Anatomical and functional abnormalities in the cortico-cerebellar-thalamo-cortical circuit have been observed in schizophrenia patients and their unaffected siblings. However, it remains unclear to the relationship between anatomical and functional abnormalities within this circuit in schizophrenia patients and their unaffected siblings, which may serve as potential endophenotypes for schizophrenia. Anatomical and resting-state functional magnetic resonance imaging data were acquired from 49 first-episode, drug-naive schizophrenia patients, 46 unaffected siblings, and 46 healthy controls. Data were analyzed by using voxel-based morphometry and Granger causality analysis. The patients and the siblings shared anatomical deficits in the left middle temporal gyrus (MTG) and increased left MTG–left angular gyrus (AG) connectivity. Moreover, the left MTG–left AG connectivity negatively correlates to the duration of untreated psychosis in the patients. The findings indicate that anatomical deficits in the left MTG and its increased causal connectivity with the left AG may serve as potential endophenotypes for schizophrenia with clinical implications. PMID:26496253

  10. Learning a Theory of Causality

    ERIC Educational Resources Information Center

    Goodman, Noah D.; Ullman, Tomer D.; Tenenbaum, Joshua B.

    2011-01-01

    The very early appearance of abstract knowledge is often taken as evidence for innateness. We explore the relative learning speeds of abstract and specific knowledge within a Bayesian framework and the role for innate structure. We focus on knowledge about causality, seen as a domain-general intuitive theory, and ask whether this knowledge can be…

  11. Identity, causality, and pronoun ambiguity.

    PubMed

    Sagi, Eyal; Rips, Lance J

    2014-10-01

    This article looks at the way people determine the antecedent of a pronoun in sentence pairs, such as: Albert invited Ron to dinner. He spent hours cleaning the house. The experiment reported here is motivated by the idea that such judgments depend on reasoning about identity (e.g., the identity of the he who cleaned the house). Because the identity of an individual over time depends on the causal-historical path connecting the stages of the individual, the correct antecedent will also depend on causal connections. The experiment varied how likely it is that the event of the first sentence (e.g., the invitation) would cause the event of the second (the house cleaning) for each of the two individuals (the likelihood that if Albert invited Ron to dinner, this would cause Albert to clean the house, versus cause Ron to clean the house). Decisions about the antecedent followed causal likelihood. A mathematical model of causal identity accounted for most of the key aspects of the data from the individual sentence pairs.

  12. Causal Inference for a Population of Causally Connected Units

    PubMed Central

    van der Laan, Mark J.

    2015-01-01

    Suppose that we observe a population of causally connected units. On each unit at each time-point on a grid we observe a set of other units the unit is potentially connected with, and a unit-specific longitudinal data structure consisting of baseline and time-dependent covariates, a time-dependent treatment, and a final outcome of interest. The target quantity of interest is defined as the mean outcome for this group of units if the exposures of the units would be probabilistically assigned according to a known specified mechanism, where the latter is called a stochastic intervention. Causal effects of interest are defined as contrasts of the mean of the unit-specific outcomes under different stochastic interventions one wishes to evaluate. This covers a large range of estimation problems from independent units, independent clusters of units, and a single cluster of units in which each unit has a limited number of connections to other units. The allowed dependence includes treatment allocation in response to data on multiple units and so called causal interference as special cases. We present a few motivating classes of examples, propose a structural causal model, define the desired causal quantities, address the identification of these quantities from the observed data, and define maximum likelihood based estimators based on cross-validation. In particular, we present maximum likelihood based super-learning for this network data. Nonetheless, such smoothed/regularized maximum likelihood estimators are not targeted and will thereby be overly bias w.r.t. the target parameter, and, as a consequence, generally not result in asymptotically normally distributed estimators of the statistical target parameter. To formally develop estimation theory, we focus on the simpler case in which the longitudinal data structure is a point-treatment data structure. We formulate a novel targeted maximum likelihood estimator of this estimand and show that the double robustness of the

  13. Entanglement, holography and causal diamonds

    NASA Astrophysics Data System (ADS)

    de Boer, Jan; Haehl, Felix M.; Heller, Michal P.; Myers, Robert C.

    2016-08-01

    We argue that the degrees of freedom in a d-dimensional CFT can be reorganized in an insightful way by studying observables on the moduli space of causal diamonds (or equivalently, the space of pairs of timelike separated points). This 2 d-dimensional space naturally captures some of the fundamental nonlocality and causal structure inherent in the entanglement of CFT states. For any primary CFT operator, we construct an observable on this space, which is defined by smearing the associated one-point function over causal diamonds. Known examples of such quantities are the entanglement entropy of vacuum excitations and its higher spin generalizations. We show that in holographic CFTs, these observables are given by suitably defined integrals of dual bulk fields over the corresponding Ryu-Takayanagi minimal surfaces. Furthermore, we explain connections to the operator product expansion and the first law of entanglemententropy from this unifying point of view. We demonstrate that for small perturbations of the vacuum, our observables obey linear two-derivative equations of motion on the space of causal diamonds. In two dimensions, the latter is given by a product of two copies of a two-dimensional de Sitter space. For a class of universal states, we show that the entanglement entropy and its spin-three generalization obey nonlinear equations of motion with local interactions on this moduli space, which can be identified with Liouville and Toda equations, respectively. This suggests the possibility of extending the definition of our new observables beyond the linear level more generally and in such a way that they give rise to new dynamically interacting theories on the moduli space of causal diamonds. Various challenges one has to face in order to implement this idea are discussed.

  14. Atmospheric inorganic trace contaminants in Finland, especially in the Gulf of Finland area

    NASA Astrophysics Data System (ADS)

    Jalkanen, Liisa Maria

    Atmospheric aerosol samples were collected at Utö and Virolahti in the Gulf of Finland area and Ähtäri in Central Finland using a filter pack. The samples were analysed by instrumental neutron activation analysis (INAA) and inductively coupled plasma mass-spectrometry (ICP-MS) for 34 elements including halogens and heavy metals. A very simple and quantitative acid digestion method was developed for the dissolution of the aerosol samples for ICP-MS analysis. Analysis of the elemental data is given using trajectories, principal component analysis and long-range transport modelling. The average total (fine + coarse) atmospheric concentrations range at Utö from 0.083 ng m -3 for Cd to 730 ng m-3 for Na. The sea areas (Utö, Virolahti, Hailuoto) have most of the heavy metal air pollution in Finland, as witnessed by the aerosol concentration and wet deposition data. There is a clear decreasing gradient in the deposition of As, Cd, Cr, Pb, and V from South to North in Finland. In general, the trace element concentrations and deposition are lower in Finland than in Central Europe. The effect of large particulate emission sources in Estonia can be seen in the elemental concentrations of atmospheric particles and in the deposition around the eastern Gulf of Finland region. There has been a remarkable decrease in heavy metal emissions in Finland during the 1990s. However, due to long-range transport, the decrease in deposition as witnessed by analysis of these concentrations in precipitation and moss is much less than would be expected.

  15. Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971-2013.

    PubMed

    Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

    2016-07-01

    In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen's multivariate co-integration and performed a variance decomposition analysis using Cholesky's technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana's energy mix can help mitigate climate change and its impact in the future.

  16. Carbon dioxide emissions, GDP, energy use, and population growth: a multivariate and causality analysis for Ghana, 1971-2013.

    PubMed

    Asumadu-Sarkodie, Samuel; Owusu, Phebe Asantewaa

    2016-07-01

    In this study, the relationship between carbon dioxide emissions, GDP, energy use, and population growth in Ghana was investigated from 1971 to 2013 by comparing the vector error correction model (VECM) and the autoregressive distributed lag (ARDL). Prior to testing for Granger causality based on VECM, the study tested for unit roots, Johansen's multivariate co-integration and performed a variance decomposition analysis using Cholesky's technique. Evidence from the variance decomposition shows that 21 % of future shocks in carbon dioxide emissions are due to fluctuations in energy use, 8 % of future shocks are due to fluctuations in GDP, and 6 % of future shocks are due to fluctuations in population. There was evidence of bidirectional causality running from energy use to GDP and a unidirectional causality running from carbon dioxide emissions to energy use, carbon dioxide emissions to GDP, carbon dioxide emissions to population, and population to energy use. Evidence from the long-run elasticities shows that a 1 % increase in population in Ghana will increase carbon dioxide emissions by 1.72 %. There was evidence of short-run equilibrium relationship running from energy use to carbon dioxide emissions and GDP to carbon dioxide emissions. As a policy implication, the addition of renewable energy and clean energy technologies into Ghana's energy mix can help mitigate climate change and its impact in the future. PMID:27030236

  17. Constraints on Children's Judgments of Magical Causality

    ERIC Educational Resources Information Center

    Woolley, Jacqueline D.; Browne, Cheryl A.; Boerger, Elizabeth A.

    2006-01-01

    In 3 studies we addressed the operation of constraints on children's causal judgments. Our primary focus was whether children's beliefs about magical causality, specifically wishing, are constrained by features that govern the attribution of ordinary causality. In Experiment 1, children witnessed situations in which a confederate's wish appeared…

  18. Expectations and interpretations during causal learning.

    PubMed

    Luhmann, Christian C; Ahn, Woo-Kyoung

    2011-05-01

    In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A-1C, participants' interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings.

  19. Expectations and Interpretations during Causal Learning

    ERIC Educational Resources Information Center

    Luhmann, Christian C.; Ahn, Woo-kyoung

    2011-01-01

    In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to…

  20. Expectations and Interpretations During Causal Learning

    PubMed Central

    Luhmann, Christian C.; Ahn, Woo-kyoung

    2012-01-01

    In existing models of causal induction, 4 types of covariation information (i.e., presence/absence of an event followed by presence/absence of another event) always exert identical influences on causal strength judgments (e.g., joint presence of events always suggests a generative causal relationship). In contrast, we suggest that, due to expectations developed during causal learning, learners give varied interpretations to covariation information as it is encountered and that these interpretations influence the resulting causal beliefs. In Experiments 1A–1C, participants’ interpretations of observations during a causal learning task were dynamic, expectation based, and, furthermore, strongly tied to subsequent causal judgments. Experiment 2 demonstrated that adding trials of joint absence or joint presence of events, whose roles have been traditionally interpreted as increasing causal strengths, could result in decreased overall causal judgments and that adding trials where one event occurs in the absence of another, whose roles have been traditionally interpreted as decreasing causal strengths, could result in increased overall causal judgments. We discuss implications for traditional models of causal learning and how a more top-down approach (e.g., Bayesian) would be more compatible with the current findings. PMID:21534705

  1. Designing Effective Supports for Causal Reasoning

    ERIC Educational Resources Information Center

    Jonassen, David H.; Ionas, Ioan Gelu

    2008-01-01

    Causal reasoning represents one of the most basic and important cognitive processes that underpin all higher-order activities, such as conceptual understanding and problem solving. Hume called causality the "cement of the universe" [Hume (1739/2000). Causal reasoning is required for making predictions, drawing implications and inferences, and…

  2. Exploring Individual Differences in Preschoolers' Causal Stance

    ERIC Educational Resources Information Center

    Alvarez, Aubry; Booth, Amy E.

    2016-01-01

    Preschoolers, as a group, are highly attuned to causality, and this attunement is known to facilitate memory, learning, and problem solving. However, recent work reveals substantial individual variability in the strength of children's "causal stance," as demonstrated by their curiosity about and preference for new causal information. In…

  3. Representing Personal Determinants in Causal Structures.

    ERIC Educational Resources Information Center

    Bandura, Albert

    1984-01-01

    Responds to Staddon's critique of the author's earlier article and addresses issues raised by Staddon's (1984) alternative models of causality. The author argues that it is not the formalizability of causal processes that is the issue but whether cognitive determinants of behavior are reducible to past stimulus inputs in causal structures.…

  4. Causal inference in economics and marketing.

    PubMed

    Varian, Hal R

    2016-07-01

    This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference.

  5. Considerations on causality in pharmacovigilance.

    PubMed

    Edwards, I Ralph

    2012-01-01

    Causality has been a topic of debate by philosophers, scientists, lawyers and for centuries. It is essential to define as precisely as possible all steps in the logical chain of events, since each may strengthen or confound an argument. Almost always there are issues of missing and conflicting data that need to be addressed specifically. In pharmacovigilance, as in many other situations, there is not just one possible causation for an effect but several. Each must be evaluated in the given context for probability. There is also likely to be a causal chain of events to the adverse effect under consideration, and each must be considered. In an individual patient diagnosis the components of patient history, clinical findings and various laboratory test findings are combined to point to the probability of the patho-physiological diagnosis, which in turn is related to possible causes with a strength determined by the constellation of findings. The established Bradford-Hill criteria are valuable in considering all the possible causal factors. Pharmacoepidemiology allows for population incidences of causes for particular effects and therefore provides an a priori probability listing for competing possible causes, or at least of one possible cause against the background of all others in a control group. Since adverse effects of medicines are generally rare, it is not possible to exclude drug causation in an individual by reliance on epidemiological evidence alone, only to argue that the incidence is below a level determined by statistical power, of the study or studies combined. Other areas of society are concerned with the process of causal inference, and this is especially true in legal cases in which judgements are made on possible personal injury by drugs.

  6. Modeling of causality with metamaterials

    NASA Astrophysics Data System (ADS)

    Smolyaninov, Igor I.

    2013-02-01

    Hyperbolic metamaterials may be used to model a 2 + 1-dimensional Minkowski space-time in which the role of time is played by one of the spatial coordinates. When a metamaterial is built and illuminated with a coherent extraordinary laser beam, the stationary pattern of light propagation inside the metamaterial may be treated as a collection of particle world lines, which represents a complete ‘history’ of this 2 + 1-dimensional space-time. While this model may be used to build interesting space-time analogs, such as metamaterial ‘black holes’ and a metamaterial ‘big bang’, it lacks causality: since light inside the metamaterial may propagate back and forth along the ‘timelike’ spatial coordinate, events in the ‘future’ may affect events in the ‘past’. Here we demonstrate that a more sophisticated metamaterial model may fix this deficiency via breaking the mirror and temporal (PT) symmetries of the original model and producing one-way propagation along the ‘timelike’ spatial coordinate. The resulting 2 + 1-dimensional Minkowski space-time appears to be causal. This scenario may be considered as a metamaterial model of the Wheeler-Feynman absorber theory of causality.

  7. Velocity Requirements for Causality Violation

    NASA Astrophysics Data System (ADS)

    Modanese, Giovanni

    We re-examine the "Regge-Tolman paradox" with reference to some recent experimental results. It is straightforward to find a formula for the velocity v of the moving system required to produce causality violation. This formula typically yields a velocity very close to the speed of light (for instance, v/c > 0.97 for X-shaped microwaves), which raises some doubts about the real physical observability of the violations. We then compute the velocity requirement introducing a delay between the reception of the primary signal and the emission of the secondary. It turns out that in principle for any delay it is possible to find moving observers able to produce active causal violation. This is mathematically due to the singularity of the Lorentz transformations for β →1. For a realistic delay due to the propagation of a luminal precursor, we find that causality violations in the reported experiments are still more unlikely (v/c > 0.989), and even in the hypothesis that the superluminal propagation velocity goes to infinity, the velocity requirement is bounded by v/c > 0.62. We also prove that if two oscopic bodies exchange energy and momentum through superluminal signals, then the swap of signal source and target is incompatible with the Lorentz transformations; therefore it is not possible to distinguish between source and target, even with reference to a definite reference frame.

  8. Inference of directed climate networks: role of instability of causality estimation methods

    NASA Astrophysics Data System (ADS)

    Hlinka, Jaroslav; Hartman, David; Vejmelka, Martin; Paluš, Milan

    2013-04-01

    Climate data are increasingly analyzed by complex network analysis methods, including graph-theoretical approaches [1]. For such analysis, links between localized nodes of climate network are typically quantified by some statistical measures of dependence (connectivity) between measured variables of interest. To obtain information on the directionality of the interactions in the networks, a wide range of methods exists. These can be broadly divided into linear and nonlinear methods, with some of the latter having the theoretical advantage of being model-free, and principally a generalization of the former [2]. However, as a trade-off, this generality comes together with lower accuracy - in particular if the system was close to linear. In an overall stationary system, this may potentially lead to higher variability in the nonlinear network estimates. Therefore, with the same control of false alarms, this may lead to lower sensitivity for detection of real changes in the network structure. These problems are discussed on the example of daily SAT and SLP data from the NCEP/NCAR reanalysis dataset. We first reduce the dimensionality of data using PCA with VARIMAX rotation to detect several dozens of components that together explain most of the data variability. We further construct directed climate networks applying a selection of most widely used methods - variants of linear Granger causality and conditional mutual information. Finally, we assess the stability of the detected directed climate networks by computing them in sliding time windows. To understand the origin of the observed instabilities and their range, we also apply the same procedure to two types of surrogate data: either with non-stationarity in network structure removed, or imposed in a controlled way. In general, the linear methods show stable results in terms of overall similarity of directed climate networks inferred. For instance, for different decades of SAT data, the Spearman correlation of edge

  9. Volatile organic compound sources for Southern Finland

    NASA Astrophysics Data System (ADS)

    Patokoski, Johanna; Ruuskanen, Taina M.; Kajos, Maija K.; Taipale, Risto; Rantala, Pekka; Aalto, Juho; Ryyppö, Timo; Hakola, Hannele; Rinne, Janne

    2014-05-01

    Volatile organic compounds (VOCs) have several sources, both biogenic and anthropogenic. Emissions of biogenic VOCs in a global scale are estimated to be an order of magnitude higher than anthropogenic ones. However, in densely populated areas and during winter time the anthropogenic VOC emissions dominate over the biogenic ones. The aim of this study was to clarify potential local sources and source areas of VOCs in different seasons. Diurnal behaviour in winter and spring were also compared at two different sites in Finland: SMEAR II and III (Station for Measuring Ecosystem - Atmosphere Relations). SMEAR II is a rural site located in Hyytiälä in Southern Finland 220 km North-West from Helsinki whereas SMEAR III is background urban site located 5 km from the downtown of Helsinki. The volume mixing ratios of VOCs were measured with a proton-transfer-reaction mass spectrometer (PTR-MS, Ionicon Analytik GmbH, Austria) during years 2006-2011. Other trace gases such as CO, NOXand SO2 were also measured in both sites and used for source analysis. Source areas for long term VOC measurements were investigated with trajectory analysis and sources for local and regional concentrations were determined by Unmix multivariate receptor model. Forest fires affect air quality and the biggest smoke plumes can be seen in satellite images and even hinder visibility in the plume areas. They provide temporally and spatially well-defined sources that can be used to verify source area estimates. During the measurement periods two different forest fire episodes with several hotspots, happened in Russia. Forest fires which showed up in these measurements were in 2006 near the border of Finland in Vyborg area and 2010 in Moscow area. Forest fire episodes were clearly observed in trajectory analysis for benzene, toluene and methanol and also CO and NOX. In addition to event sources continuous source areas were determined. Anthropogenic local sources seemed to be dominant during winter in

  10. Time-Varying Causal Inference From Phosphoproteomic Measurements in Macrophage Cells

    PubMed Central

    Masnadi-Shirazi, Maryam; Maurya, Mano Ram; Subramaniam, Shankar

    2015-01-01

    Cellular signaling circuitry in eukaryotes can be studied by analyzing the regulation of protein phosphorylation and its impact on downstream mechanisms leading to a pheno-type. A primary role of phosphorylation is to act as a switch to turn “on” or “off” a protein activity or a cellular pathway. Specifically, protein phosphorylation is a major leit motif for transducing molecular signals inside the cell. Errors in transferring cellular information can alter the normal function and may lead to diseases such as cancer; an accurate reconstruction of the “true” signaling network is essential for understanding the molecular machinery involved in normal and pathological function. In this study, we have developed a novel framework for time-dependent reconstruction of signaling networks involved in the activation of macrophage cells leading to an inflammatory response. Several signaling pathways have been identified in macrophage cells, but the time-varying causal relationship that can produce a dynamic directed graph of these molecules has not been explored in detail. Here, we use the notion of Granger causality, and apply a vector autoregressive model to phosphoprotein time-course data in RAW 264.7 macrophage cells. Through the reconstruction of the phosphoprotein network, we were able to estimate the directionality and the dynamics of information flow. Significant interactions were selected through statistical hypothesis testing (t-test) of the coefficients of a linear model and were used to reconstruct the phosphoprotein signaling network. Our approach results in a three-stage phosphoprotein network that represents the evolution of the causal interactions in the intracellular signaling pathways. PMID:24681921

  11. Improving causality induction with category learning.

    PubMed

    Guo, Yi; Wang, Zhihong; Shao, Zhiqing

    2014-01-01

    Causal relations are of fundamental importance for human perception and reasoning. According to the nature of causality, causality has explicit and implicit forms. In the case of explicit form, causal-effect relations exist at either clausal or discourse levels. The implicit causal-effect relations heavily rely on empirical analysis and evidence accumulation. This paper proposes a comprehensive causality extraction system (CL-CIS) integrated with the means of category-learning. CL-CIS considers cause-effect relations in both explicit and implicit forms and especially practices the relation between category and causality in computation. In elaborately designed experiments, CL-CIS is evaluated together with general causality analysis system (GCAS) and general causality analysis system with learning (GCAS-L), and it testified to its own capability and performance in construction of cause-effect relations. This paper confirms the expectation that the precision and coverage of causality induction can be remarkably improved by means of causal and category learning.

  12. Improving Causality Induction with Category Learning

    PubMed Central

    Wang, Zhihong; Shao, Zhiqing

    2014-01-01

    Causal relations are of fundamental importance for human perception and reasoning. According to the nature of causality, causality has explicit and implicit forms. In the case of explicit form, causal-effect relations exist at either clausal or discourse levels. The implicit causal-effect relations heavily rely on empirical analysis and evidence accumulation. This paper proposes a comprehensive causality extraction system (CL-CIS) integrated with the means of category-learning. CL-CIS considers cause-effect relations in both explicit and implicit forms and especially practices the relation between category and causality in computation. In elaborately designed experiments, CL-CIS is evaluated together with general causality analysis system (GCAS) and general causality analysis system with learning (GCAS-L), and it testified to its own capability and performance in construction of cause-effect relations. This paper confirms the expectation that the precision and coverage of causality induction can be remarkably improved by means of causal and category learning. PMID:24883419

  13. Voluntary action and causality in temporal binding.

    PubMed

    Cravo, Andre M; Claessens, Peter M E; Baldo, Marcus V C

    2009-10-01

    Previous studies have documented temporal attraction in perceived times of actions and their effects. While some authors argue that voluntary action is a necessary condition for this phenomenon, others claim that the causal relationship between action and effect is the crucial ingredient. In the present study, we investigate voluntary action and causality as the necessary and sufficient conditions for temporal binding. We used a variation of the launching effect proposed by Michotte, in which participants controlled the launch stimulus in some blocks. Volunteers reported causality ratings and estimated the interval between the two events. Our results show dissociations between causality ratings and temporal estimation. While causality ratings are not affected by voluntary action, temporal bindings were only found in the presence of both voluntary action and high causality. Our results indicate that voluntary action and causality are both necessary for the emergence of temporal binding.

  14. Economic reduction of acidifying deposition in Finland by decreasing emissions in Finland, Estonia and Russia.

    PubMed

    Tähtinen, M; Lehtilä, A; Pipatti, R; Wistbacka, M; Savolainen, I

    1997-09-26

    Here we consider cost-effective solutions of emission control measures in Finland and the nearby areas of Estonia, St. Petersburg region, Karelia and Kola, in order to limit the acidifying deposition in Finland. In the study, the emission control costs of SO2, NOx and NH3 are assessed for the areas studied and an optimisation model developed for calculation of cost-optimal deposition control policies. The input data of the model consist of the cost functions describing the emission control costs to achieve lower emission levels for the gases and areas considered and of dispersion coefficients which describe the deposition due to an emission source in the deposition receptor grid squares. In addition, the model includes a description to calculate the acidifying load. The optimisation is based on linear programming. When the acidifying load of Southern Finland is reduced by minimising the total control costs, approx, three quarters of the total control costs are due to measures in the nearby areas, Estonia, St. Peterburg region, Karelia and Kola, and approx. one quarter due to measures in Finland. The distribution of costs in the cost-optimised cases depends relatively little on the level to which the acidifying load due the source areas considered are required to be reduced. If the load reduction target is moderate, the emission control measures should mainly be allocated to sulphur emissions and to some extent to ammonia emissions and, if the load reduction target is stricter, also to the emissions of nitrogen oxides.

  15. Scalar Fields via Causal Tapestries

    NASA Astrophysics Data System (ADS)

    Sulis, William

    2012-02-01

    Causal tapestries provide a framework for implementing an explicit Process Theory approach to quantum foundations which models information flow within a physical system. We consider event-transition tapestry pairs. An event tapestry O is a 4-tuple (L, K, M, Ip ) where K is an index set of cardinality κ, M = M x F(M) x D x P(M') a mathematical structure with M a causal space, F(M) a function space, D a descriptor space, P(M') either a Lie algebra or tangent space on a manifold M', Ip an event tapestry. L consists of elements of the form [n]<α>G, n in K, α in M and G an acyclic directed graph whose vertex set is a subset of Lp Likewise, a transition tapestry π is a 4-tuple (L', K', M', I'p ) where M' = M' x F(M') x D' x P'(M). The dynamic generates a consistent succession of O-π pairs by means of a game based on the technique of forcing used in logic to generate models. This dynamic has previously been shown to be compatible with Lorentz invariance. An application of this approach to model scalar fields is presented in which each informon is associated with a function of the form f(πk1 /σ1 ,,πkN /σN )sin ( σ1 t1 --πk1 )/ ( σ1 t1 --πk1 ) .sin ( σN tN --πkN )/ ( σN tN --πkN ) and the WSK interpolation theorem is used to generate the resulting scalar field on the causal manifold.

  16. Sterilization in Finland: from eugenics to contraception.

    PubMed

    Hemminki, E; Rasimus, A; Forssas, E

    1997-12-01

    The purpose of this paper was to describe the transition of sterilization in Finland from an eugenic tool to a contraceptive. Historical data were drawn from earlier reports in Finnish. Numbers of and reasons for sterilizations since 1950 were collected from nationwide sterilization statistics. Prevalence, characteristics of sterilized women, and women's satisfaction with sterilizations were studied from a 1994 nationwide survey (74% response rate). Logistic regression was used for adjustments. In the first half of the 20th century, eugenic ideology had influence in Finland as in other parts of Europe, and the 1935 and 1950 sterilization laws had an eugenic spirit. Regardless of this, the numbers of eugenic sterilizations remained low, and in practice, family planning was the main reason for sterilization. Nonetheless, prior to 1970 not all sterilizations were freely chosen, because sterilizations were sometimes used as a precondition for abortion. Female sterilizations showed remarkable fluctuation over time. Male sterilizations have been rare. The reasons stipulated by the law did not explain the numbers of sterilizations. In a 1994 survey, 9% of Finnish women reported they were using sterilization as their current contraceptive method (n = 189). Compared to women using other contraceptive methods, sterilized women were older, had had more births and pregnancies, and came from lower social classes. Sterilized women were satisfied with their sterilization, but there were women (8.5%) who regretted it. In conclusion, sterilizations have been and are likely to continue to be an important family planning method in Finland. The extreme gender ratio suggests a need for promoting male sterilizations, and women's expressed regrets suggest consideration of a higher age limit.

  17. Silica, silicosis and cancer in Finland.

    PubMed

    Partanen, T; Jaakkola, J; Tossavainen, A

    1995-01-01

    Approximately 100 000 Finnish workers are currently employed in jobs and tasks that may involve exposure to airborne silica dust. The major industries involved are mining and quarrying; production of glass, ceramics, bricks and other building materials; metal industry, particularly iron and steel founding; and construction. Over 1500 cases of silicosis have occurred in Finland since 1935. Tuberculosis has been a frequent complication of silicosis. Results of studies from several countries strongly suggest that silica dust also causes lung cancer. The results of the relevant Finnish epidemiologic and industrial hygiene studies addressing cancer risk and exposure to quartz dust are summarized. PMID:8929699

  18. Silica, silicosis and cancer in Finland.

    PubMed

    Partanen, T; Jaakkola, J; Tossavainen, A

    1995-01-01

    Approximately 100 000 Finnish workers are currently employed in jobs and tasks that may involve exposure to airborne silica dust. The major industries involved are mining and quarrying; production of glass, ceramics, bricks and other building materials; metal industry, particularly iron and steel founding; and construction. Over 1500 cases of silicosis have occurred in Finland since 1935. Tuberculosis has been a frequent complication of silicosis. Results of studies from several countries strongly suggest that silica dust also causes lung cancer. The results of the relevant Finnish epidemiologic and industrial hygiene studies addressing cancer risk and exposure to quartz dust are summarized.

  19. Radon Policy in Finland, Achievements and Challenges

    SciTech Connect

    Arvela, Hannu; Maekelaeinen, Ilona; Reisbacka, Heikki

    2008-08-07

    Finland is a country of high indoor radon concentrations. Since 1980 the authority regulations, guidance, radon mapping and research work supporting decision making have been developed continuously. Clear regulations directed to citizens and authorities form the basis for radon policy. Active mapping work and measurement ordered by private home owners has resulted in 100.000 houses measured. National indoor radon data base forms a good basis for decision making, communication and research. The number of new houses provided with radon preventive constructions has increased remarkably. New radon campaigns has increased measurement and mitigation activity. Furher increasing of public awareness is the key challenge.

  20. [The viper--Finland's only poisonous snake].

    PubMed

    Vuori, Arno

    2011-01-01

    The viper (Vipera berus) is the most common poisonous snake in Europe, and the only one in Finland. In viper bites, highly varying amounts of venom end up into the victim, whereby prediction of the progression of symptoms of poisoning is very difficult. A severe clinical picture must always be anticipated. The size of the victim has also an effect on the outcome. Adequate monitoring and when necessary, massive fluid therapy are essential in the treatment. Due to possible kidney damage, anti-inflammatory drugs are not recommended. Severe or rapidly progressing symptoms require the use of an antidote.

  1. How prescriptive norms influence causal inferences.

    PubMed

    Samland, Jana; Waldmann, Michael R

    2016-11-01

    Recent experimental findings suggest that prescriptive norms influence causal inferences. The cognitive mechanism underlying this finding is still under debate. We compare three competing theories: The culpable control model of blame argues that reasoners tend to exaggerate the causal influence of norm-violating agents, which should lead to relatively higher causal strength estimates for these agents. By contrast, the counterfactual reasoning account of causal selection assumes that norms do not alter the representation of the causal model, but rather later causal selection stages. According to this view, reasoners tend to preferentially consider counterfactual states of abnormal rather than normal factors, which leads to the choice of the abnormal factor in a causal selection task. A third view, the accountability hypothesis, claims that the effects of prescriptive norms are generated by the ambiguity of the causal test question. Asking whether an agent is a cause can be understood as a request to assess her causal contribution but also her moral accountability. According to this theory norm effects on causal selection are mediated by accountability judgments that are not only sensitive to the abnormality of behavior but also to mitigating factors, such as intentionality and knowledge of norms. Five experiments are presented that favor the accountability account over the two alternative theories. PMID:27591550

  2. Space and time in perceptual causality.

    PubMed

    Straube, Benjamin; Chatterjee, Anjan

    2010-01-01

    Inferring causality is a fundamental feature of human cognition that allows us to theorize about and predict future states of the world. Michotte suggested that humans automatically perceive causality based on certain perceptual features of events. However, individual differences in judgments of perceptual causality cast doubt on Michotte's view. To gain insights in the neural basis of individual difference in the perception of causality, our participants judged causal relationships in animations of a blue ball colliding with a red ball (a launching event) while fMRI-data were acquired. Spatial continuity and temporal contiguity were varied parametrically in these stimuli. We did not find consistent brain activation differences between trials judged as caused and those judged as non-caused, making it unlikely that humans have universal instantiation of perceptual causality in the brain. However, participants were slower to respond to and showed greater neural activity for violations of causality, suggesting that humans are biased to expect causal relationships when moving objects appear to interact. Our participants demonstrated considerable individual differences in their sensitivity to spatial and temporal characteristics in perceiving causality. These qualitative differences in sensitivity to time or space in perceiving causality were instantiated in individual differences in activation of the left basal ganglia or right parietal lobe, respectively. Thus, the perception that the movement of one object causes the movement of another is triggered by elemental spatial and temporal sensitivities, which themselves are instantiated in specific distinct neural networks. PMID:20463866

  3. Youth Suicide Trends in Finland, 1969-2008

    ERIC Educational Resources Information Center

    Lahti, Anniina; Rasanen, Pirkko; Riala, Kaisa; Keranen, Sirpa; Hakko, Helina

    2011-01-01

    Background: There are only a few recent studies on secular trends in child and adolescent suicides. We examine here trends in rates and methods of suicide among young people in Finland, where suicide rates at these ages are among the highest in the world. Methods: The data, obtained from Statistics Finland, consisted of all suicides (n = 901)…

  4. ADHD in Finland and Types of Scandinavian Cooperation.

    ERIC Educational Resources Information Center

    Tapper, Marie-Louise; Michelsson, Katarina

    This paper reviews the history and current status of services to children with attention deficit hyperactivity disorder in Finland. It notes the availability of free or almost free health services in Finland and the resulting very low infant mortality rate. The history of attention deficit hyperactivity disorders (ADHD), termed "minimal brain…

  5. The Quality and Effectiveness of Confirmation Classes in Finland

    ERIC Educational Resources Information Center

    Niemela, Kati

    2006-01-01

    Every year some 90% of 15-year-olds in Finland attend confirmation classes in the Evangelical Lutheran Church of Finland, which is greater than the percentage of that age group belonging to the Church. What is behind the popularity of Finnish confirmation classes? This article scrutinizes the quality and effectiveness of confirmation classes.…

  6. The Discourse on Multicultural Education in Finland: Education for Whom?

    ERIC Educational Resources Information Center

    Holm, Gunilla; Londen, Monica

    2010-01-01

    Finland is experiencing increased immigration and therefore increased cultural diversity in its schools. This paper examines the multicultural education discourse in Finland by analysing the national and municipal curricula for the comprehensive school, educational policy documents and teacher education curricula. The focus is on how multicultural…

  7. Causal diagrams in systems epidemiology

    PubMed Central

    2012-01-01

    Methods of diagrammatic modelling have been greatly developed in the past two decades. Outside the context of infectious diseases, systematic use of diagrams in epidemiology has been mainly confined to the analysis of a single link: that between a disease outcome and its proximal determinant(s). Transmitted causes ("causes of causes") tend not to be systematically analysed. The infectious disease epidemiology modelling tradition models the human population in its environment, typically with the exposure-health relationship and the determinants of exposure being considered at individual and group/ecological levels, respectively. Some properties of the resulting systems are quite general, and are seen in unrelated contexts such as biochemical pathways. Confining analysis to a single link misses the opportunity to discover such properties. The structure of a causal diagram is derived from knowledge about how the world works, as well as from statistical evidence. A single diagram can be used to characterise a whole research area, not just a single analysis - although this depends on the degree of consistency of the causal relationships between different populations - and can therefore be used to integrate multiple datasets. Additional advantages of system-wide models include: the use of instrumental variables - now emerging as an important technique in epidemiology in the context of mendelian randomisation, but under-used in the exploitation of "natural experiments"; the explicit use of change models, which have advantages with respect to inferring causation; and in the detection and elucidation of feedback. PMID:22429606

  8. Self-esteem and causal attributions.

    PubMed

    Chandler, T A; Lee, M S; Pengilly, J W

    1997-11-01

    The relationship between self-esteem and causal attributions of success and failure in achievement-related behavior was examined among undergraduate students. An integration of a self-consistency model of causal attribution and self-enhancement theory was attempted. Self-esteem and performance outcome conditions of success and failure served as independent variables. Success and failure conditions were created via feedback regarding the participants' performance on an anagram task. The participants' attributions of six causal elements (ability, effort, immediate effort, task difficulty, luck, and mood) were categorized and combined with three causal dimensions (internal-external locus, stability, and controllability), which served as dependent variables. Participants' expectations regarding performance also served as a dependent variable. The relationship between self-esteem, expectancies of success and failure, performance, and stable causality were reported. In terms of causal dimensions, internal, stable, and controllable dimensions were explained by self-enhancement.

  9. Assessment of atmospheric mercury emissions in Finland

    PubMed

    Mukherjee; Melanen; Ekqvist; Verta

    2000-10-01

    This paper is part of the study of atmospheric emissions of heavy metals conducted by the Finnish Environment Institute in collaboration with the Technical Research Centre of Finland (VTT) under the umbrella of the Finnish Ministry of the Environment. The scope of our study is limited solely to anthropogenic mercury that is emitted directly to the atmosphere. This article addresses emission factors and trends of atmospheric mercury emissions during the 1990s and is based mainly on the database of the Finnish Environmental Administration. In addition, data based on the measurements taken by the VTT regarding emission factors have been used to estimate emissions of mercury from the incineration of waste. The study indicates that the total emission of mercury has decreased from 1140 kg in 1990 to 620 kg in 1997, while industrial and energy production have been on the increase simultaneously. The 45% emission reduction is due to improved gas cleaning equipment, process changes, automation, the installation of flue gas desulfurization process in coal-fired power plants and strict pollution control laws. In the past, some authors have estimated a higher mercury emission in Finland. In this study, it is also observed that there are no big changes in the quality of raw materials. Estimated emission factors can be of great help to management for estimating mercury emissions and also its risk assessment.

  10. Risk Assessment in Finland: Theory and Practice

    PubMed Central

    Pääkkönen, Rauno

    2010-01-01

    The Finnish risk assessment practice is based on the Occupational Safety and Health (OSH) Act aiming to improve working conditions in order maintain the employees' work ability, and to prevent occupational accidents and diseases. In practice there are hundreds of risk assessment methods in use. A simple method is used in small and medium sized enterprises and more complex risk evaluation methods in larger work places. Does the risk management function in the work places in Finland? According to our experience something more is needed. That is, understanding of common and company related benefits of risk management. The wider conclusion is that commitment for risk assessment in Finland is high enough. However, in those enterprises where OSH management was at an acceptable level or above it, there were also more varied and more successfully accomplished actions to remove or reduce the risks than in enterprises, where OSH management was in lower level. In risk assessment it is important to process active technical prevention and exact communication, increase work place attraction and increase job satisfaction and motivation. Investments in OSH are also good business. Low absenteeism due to illness or accidents increases directly the production results by improved quality and quantity of the product. In general Finnish studies have consistently shown that the return of an invested euro is three to seven-old. In national level, according to our calculations the savings could be even 20% of our gross national product. PMID:22953157

  11. The effects of tobacco sales promotion on initiation of smoking--experiences from Finland and Norway.

    PubMed

    Rimpelä, M K; Aarø, L E; Rimpelä, A H

    1993-01-01

    Norway and Finland were among the first countries to adopt a total ban on tobacco sales promotion. Such legislation came into force in Norway and Finland in 1975 and 1978 respectively. These two countries are sometimes referred to as illustrations that such legislation has been successfully used as a means to reduce tobacco consumption. Tobacco industry spokesmen seem to interpret available evidence in the opposite way and maintain that the prohibition has not contributed to reducing the use of tobacco. Among the publications referred to and misused by tobacco industry spokesmen are publications from the authors of the present report. The effects of a ban on advertising can only be properly examined after describing a reasonable conceptual model. Such a model has to take into account (i) other social and cultural predictors of smoking, (ii) tobacco sales promotion in the contexts of all other mass communication, (iii) control measures other than a ban, and (iv) the degree of success in implementing the ban on advertising. Like any other kind of mass communication tobacco advertising influences the individual in a rather complex way. Behaviour change may be regarded as the outcome of an interpersonal and intrapersonal process. Social science research on tobacco advertising and the effects of banning such advertising has a short history, most studies having been carried out in the late 1980s. After examining available evidence related to the effects of tobacco advertising on the smoking habits of adolescents we conclude as follows: the few scientifically valid reports available today give both theoretical and empirical evidence for a causal relationship. Tobacco sales promotion seems both to promote and to reinforce smoking among young people. The dynamic tobacco market represented by children and adolescents is probably the main target of tobacco sales promotion. In Finland, there have been few studies explicitly addressing the causal links between tobacco sales

  12. Multiple Causality: Consequences for Medical Practice

    PubMed Central

    Nydegger, Corinne N.

    1983-01-01

    When a scientifically trained health professional is called upon to deal with patients holding differing causal views of illness, the resulting lack of communication is frustrating to both. This discussion traces some implications for medical practice of significant cultural differences in two aspects of causal paradigms of illness: (1) terms accepted and (2) dimension or level of causality typically sought. The second is the more pervasive and intractable problem, having distinctive consequences for the role of curer, symptomatology, diagnosis and treatment. PMID:6858133

  13. Nonparametric causal inference for bivariate time series.

    PubMed

    McCracken, James M; Weigel, Robert S

    2016-02-01

    We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities.

  14. Physical integration: a causal account for consciousness.

    PubMed

    Manzotti, Riccardo; Chella, Antonio

    2014-06-01

    The issue of integration in neural networks is intimately connected with that of consciousness. In this paper, integration as an effective level of physical organization is contrasted with a methodological integrative approach. Understanding how consciousness arises out of neural processes requires a model of integration in just causal physical terms. Based on a set of feasible criteria (physical grounding, causal efficacy, no circularity and scaling), a causal account of physical integration for consciousness centered on joint causation is outlined.

  15. Causal inference in economics and marketing

    PubMed Central

    Varian, Hal R.

    2016-01-01

    This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual—a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference. PMID:27382144

  16. Causal inference in economics and marketing.

    PubMed

    Varian, Hal R

    2016-07-01

    This is an elementary introduction to causal inference in economics written for readers familiar with machine learning methods. The critical step in any causal analysis is estimating the counterfactual-a prediction of what would have happened in the absence of the treatment. The powerful techniques used in machine learning may be useful for developing better estimates of the counterfactual, potentially improving causal inference. PMID:27382144

  17. Nonparametric causal inference for bivariate time series

    NASA Astrophysics Data System (ADS)

    McCracken, James M.; Weigel, Robert S.

    2016-02-01

    We introduce new quantities for exploratory causal inference between bivariate time series. The quantities, called penchants and leanings, are computationally straightforward to apply, follow directly from assumptions of probabilistic causality, do not depend on any assumed models for the time series generating process, and do not rely on any embedding procedures; these features may provide a clearer interpretation of the results than those from existing time series causality tools. The penchant and leaning are computed based on a structured method for computing probabilities.

  18. Causal supports for early word learning.

    PubMed

    Booth, Amy E

    2009-01-01

    What factors determine whether a young child will learn a new word? Although there are surely numerous contributors, the current investigation highlights the role of causal information. Three-year-old children (N = 36) were taught 6 new words for unfamiliar objects or animals. Items were described in terms of their causal or noncausal properties. When tested only minutes after training, no significant differences between the conditions were evident. However, when tested several days after training, children performed better on words trained in the causal condition. These results demonstrate that the well-documented effect of causal information on learning and categorization extends to word learning in young children. PMID:19630905

  19. Assessing causality in multivariate accident models.

    PubMed

    Elvik, Rune

    2011-01-01

    This paper discusses the application of operational criteria of causality to multivariate statistical models developed to identify sources of systematic variation in accident counts, in particular the effects of variables representing safety treatments. Nine criteria of causality serving as the basis for the discussion have been developed. The criteria resemble criteria that have been widely used in epidemiology. To assess whether the coefficients estimated in a multivariate accident prediction model represent causal relationships or are non-causal statistical associations, all criteria of causality are relevant, but the most important criterion is how well a model controls for potentially confounding factors. Examples are given to show how the criteria of causality can be applied to multivariate accident prediction models in order to assess the relationships included in these models. It will often be the case that some of the relationships included in a model can reasonably be treated as causal, whereas for others such an interpretation is less supported. The criteria of causality are indicative only and cannot provide a basis for stringent logical proof of causality.

  20. Causal compensated perturbations in cosmology

    NASA Technical Reports Server (NTRS)

    Veeraraghavan, Shoba; Stebbins, Albert

    1990-01-01

    A theoretical framework is developed to calculate linear perturbations in the gravitational and matter fields which arise causally in response to the presence of stiff matter sources in a FRW cosmology. It is shown that, in order to satisfy energy and momentum conservation, the gravitational fields of the source must be compensated by perturbations in the matter and gravitational fields, and the role of such compensation in containing the initial inhomogeneities in their subsequent evolution is discussed. A complete formal solution is derived in terms of Green functions for the perturbations produced by an arbitrary source in a flat universe containing cold dark matter. Approximate Green function solutions are derived for the late-time density perturbations and late-time gravitational waves in a universe containing a radiation fluid. A cosmological energy-momentum pseudotensor is defined to clarify the nature of energy and momentum conservation in the expanding universe.

  1. Individual differences in causal uncertainty.

    PubMed

    Weary, G; Edwards, J A

    1994-08-01

    This article presents a scale that measures chronic individual differences in people's uncertainty about their ability to understand and detect cause-and-effect relationships in the social world: the Causal Uncertainty Scale (CUS). The results of Study 1 indicated that the scale has good internal and adequate test-retest reliability. Additionally, the results of a factor analysis suggested that the scale appears to be tapping a single construct. Study 2 examined the convergent and discriminant validity of the scale, and Studies 3 and 4 examined the predictive and incremental validity of the scale. The importance of the CUS to work on depressives' social information processing and for basic research and theory on human social judgment processes is discussed.

  2. Causal Systems Categories: Differences in Novice and Expert Categorization of Causal Phenomena

    ERIC Educational Resources Information Center

    Rottman, Benjamin M.; Gentner, Dedre; Goldwater, Micah B.

    2012-01-01

    We investigated the understanding of causal systems categories--categories defined by common causal structure rather than by common domain content--among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative…

  3. The Power of Causal Beliefs and Conflicting Evidence on Causal Judgments and Decision Making

    ERIC Educational Resources Information Center

    Garcia-Retamero, Rocio; Muller, Stephanie M.; Catena, Andres; Maldonado, Antonio

    2009-01-01

    In two experiments, we investigated the relative impact of causal beliefs and empirical evidence on both decision making and causal judgments, and whether this relative impact could be altered by previous experience. 2. Selected groups of participants in both experiments received pre-training with either causal or neutral cues, or no pre-training…

  4. [Mental health in Chile and Finland: Challenges and lessons].

    PubMed

    Retamal C, Pedro; Markkula, Niina; Peña, Sebastián

    2016-07-01

    This article analyses and compares the epidemiology of mental disorders and relevant public policies in Chile and Finland. In Chile, a specific mental health law is still lacking. While both countries highlight the role of primary care, Finland places more emphasis on participation and recovery of service users. Comprehensive mental health policies from Finland, such as a successful suicide prevention program, are presented. Both countries have similar prevalence of mental disorders, high alcohol consumption and high suicide rates. In Chile, the percentage of total disease burden due to psychiatric disorders is 13% and in Finland 14%. However, the resources to address these issues are very different. Finland spends 4.5% of its health budget on mental health, while in Chile the percentage is 2.2%. This results in differences in human resources and service provision. Finland has five times more psychiatric outpatient visits, four times more psychiatrists, triple antidepressant use and twice more clinical guidelines for different psychiatric conditions. In conclusion, both countries have similar challenges but differing realities. This may help to identify gaps and potential solutions for public health challenges in Chile. Finland’s experience demonstrates the importance of political will and long-term vision in the construction of mental health policies.

  5. [Mental health in Chile and Finland: Challenges and lessons].

    PubMed

    Retamal C, Pedro; Markkula, Niina; Peña, Sebastián

    2016-07-01

    This article analyses and compares the epidemiology of mental disorders and relevant public policies in Chile and Finland. In Chile, a specific mental health law is still lacking. While both countries highlight the role of primary care, Finland places more emphasis on participation and recovery of service users. Comprehensive mental health policies from Finland, such as a successful suicide prevention program, are presented. Both countries have similar prevalence of mental disorders, high alcohol consumption and high suicide rates. In Chile, the percentage of total disease burden due to psychiatric disorders is 13% and in Finland 14%. However, the resources to address these issues are very different. Finland spends 4.5% of its health budget on mental health, while in Chile the percentage is 2.2%. This results in differences in human resources and service provision. Finland has five times more psychiatric outpatient visits, four times more psychiatrists, triple antidepressant use and twice more clinical guidelines for different psychiatric conditions. In conclusion, both countries have similar challenges but differing realities. This may help to identify gaps and potential solutions for public health challenges in Chile. Finland’s experience demonstrates the importance of political will and long-term vision in the construction of mental health policies. PMID:27661557

  6. Inference and Action in Early Causal Reasoning.

    ERIC Educational Resources Information Center

    Frye, Douglas; And Others

    1996-01-01

    Two marble-and-ramp experiments investigated whether a simple-to-embedded-rules account can explain changes in children's causal reasoning. Results indicated that the same difference between three- and four-year olds in the prediction experiment appeared in the action experiment, suggesting that the same rules may underlie causal action as well as…

  7. Spin foam models as energetic causal sets

    NASA Astrophysics Data System (ADS)

    Cortês, Marina; Smolin, Lee

    2016-04-01

    Energetic causal sets are causal sets endowed by a flow of energy-momentum between causally related events. These incorporate a novel mechanism for the emergence of space-time from causal relations [M. Cortês and L. Smolin, Phys. Rev. D 90, 084007 (2014); Phys. Rev. D 90, 044035 (2014)]. Here we construct a spin foam model which is also an energetic causal set model. This model is closely related to the model introduced in parallel by Wolfgang Wieland in [Classical Quantum Gravity 32, 015016 (2015)]. What makes a spin foam model also an energetic causal set is Wieland's identification of new degrees of freedom analogous to momenta, conserved at events (or four-simplices), whose norms are not mass, but the volume of tetrahedra. This realizes the torsion constraints, which are missing in previous spin foam models, and are needed to relate the connection dynamics to those of the metric, as in general relativity. This identification makes it possible to apply the new mechanism for the emergence of space-time to a spin foam model. Our formulation also makes use of Markopoulou's causal formulation of spin foams [arXiv:gr-qc/9704013]. These are generated by evolving spin networks with dual Pachner moves. This endows the spin foam history with causal structure given by a partial ordering of the events which are dual to four-simplices.

  8. Updating during Reading Comprehension: Why Causality Matters

    ERIC Educational Resources Information Center

    Kendeou, Panayiota; Smith, Emily R.; O'Brien, Edward J.

    2013-01-01

    The present set of 7 experiments systematically examined the effectiveness of adding causal explanations to simple refutations in reducing or eliminating the impact of outdated information on subsequent comprehension. The addition of a single causal-explanation sentence to a refutation was sufficient to eliminate any measurable disruption in…

  9. Preschoolers' Understanding of Temporal and Causal Relations.

    ERIC Educational Resources Information Center

    Sharp, Kay Colby

    1982-01-01

    Two experiments investigated psychological factors determining preschoolers' success or failure on a sequence-completion task involving temporal and causal ordering of events. Overall findings demonstrate that while preschoolers do understand temporal/causal relationships, their abilities are dependent on process variables demanded by the task…

  10. Causal Inferences in the Campbellian Validity System

    ERIC Educational Resources Information Center

    Lund, Thorleif

    2010-01-01

    The purpose of the present paper is to critically examine causal inferences and internal validity as defined by Campbell and co-workers. Several arguments are given against their counterfactual effect definition, and this effect definition should be considered inadequate for causal research in general. Moreover, their defined independence between…

  11. Campbell's and Rubin's Perspectives on Causal Inference

    ERIC Educational Resources Information Center

    West, Stephen G.; Thoemmes, Felix

    2010-01-01

    Donald Campbell's approach to causal inference (D. T. Campbell, 1957; W. R. Shadish, T. D. Cook, & D. T. Campbell, 2002) is widely used in psychology and education, whereas Donald Rubin's causal model (P. W. Holland, 1986; D. B. Rubin, 1974, 2005) is widely used in economics, statistics, medicine, and public health. Campbell's approach focuses on…

  12. Causal Indicators Can Help to Interpret Factors

    ERIC Educational Resources Information Center

    Bentler, Peter M.

    2016-01-01

    The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…

  13. Quasi-Experimental Designs for Causal Inference

    ERIC Educational Resources Information Center

    Kim, Yongnam; Steiner, Peter

    2016-01-01

    When randomized experiments are infeasible, quasi-experimental designs can be exploited to evaluate causal treatment effects. The strongest quasi-experimental designs for causal inference are regression discontinuity designs, instrumental variable designs, matching and propensity score designs, and comparative interrupted time series designs. This…

  14. Causal Indicator Models: Identification, Estimation, and Testing

    ERIC Educational Resources Information Center

    Bollen, Kenneth A.; Davis, Walter R.

    2009-01-01

    We discuss the identification, estimation, and testing of structural equation models that have causal indicators. We first provide 2 rules of identification that are particularly helpful in models with causal indicators--the 2C emitted paths rule and the exogenous X rule. We demonstrate how these rules can help us distinguish identified from…

  15. Compact Representations of Extended Causal Models

    ERIC Educational Resources Information Center

    Halpern, Joseph Y.; Hitchcock, Christopher

    2013-01-01

    Judea Pearl (2000) was the first to propose a definition of actual causation using causal models. A number of authors have suggested that an adequate account of actual causation must appeal not only to causal structure but also to considerations of "normality." In Halpern and Hitchcock (2011), we offer a definition of actual causation…

  16. A Causal Model of Faculty Research Productivity.

    ERIC Educational Resources Information Center

    Bean, John P.

    A causal model of faculty research productivity was developed through a survey of the literature. Models of organizational behavior, organizational effectiveness, and motivation were synthesized into a causal model of productivity. Two general types of variables were assumed to affect individual research productivity: institutional variables and…

  17. Causal Moderation Analysis Using Propensity Score Methods

    ERIC Educational Resources Information Center

    Dong, Nianbo

    2012-01-01

    This paper is based on previous studies in applying propensity score methods to study multiple treatment variables to examine the causal moderator effect. The propensity score methods will be demonstrated in a case study to examine the causal moderator effect, where the moderators are categorical and continuous variables. Moderation analysis is an…

  18. Controlling for causally relevant third variables.

    PubMed

    Goodie, Adam S; Williams, Cristina C; Crooks, C L

    2003-10-01

    In 3 experiments, the authors tested the conditions under which 3rd variables are controlled for in making causal judgments. The authors hypothesized that 3rd variables are controlled for when the 3rd variables are themselves perceived as causal. In Experiment 1, the participants predicted test performance after seeing information about wearing a lucky garment, taking a test-preparation course, and staying up late. The course (perceived as more causally relevant) was controlled for more than was the garment (perceived as less causally relevant) in assessing the effectiveness of staying up late. In Experiments 2 and 3, to obviate the many alternative accounts that arise from the realistic cover story of Experiment 1, participants predicted flowers' blooming after the presentation or nonpresentation of liquids. When one liquid was trained as causal, it was controlled for more in judging another liquid than when it was trained as neutral. Overall, stimuli perceived as causal were controlled for more when judging other stimuli. The authors concluded that the effect of perceived causal relevance on causal conditionalizing is real and normatively reasonable. PMID:14672103

  19. Causal Mediation Analysis: Warning! Assumptions Ahead

    ERIC Educational Resources Information Center

    Keele, Luke

    2015-01-01

    In policy evaluations, interest may focus on why a particular treatment works. One tool for understanding why treatments work is causal mediation analysis. In this essay, I focus on the assumptions needed to estimate mediation effects. I show that there is no "gold standard" method for the identification of causal mediation effects. In…

  20. Essays on Causal Inference for Public Policy

    ERIC Educational Resources Information Center

    Zajonc, Tristan

    2012-01-01

    Effective policymaking requires understanding the causal effects of competing proposals. Relevant causal quantities include proposals' expected effect on different groups of recipients, the impact of policies over time, the potential trade-offs between competing objectives, and, ultimately, the optimal policy. This dissertation studies causal…

  1. Chernobyl fallout and cancer incidence in Finland.

    PubMed

    Auvinen, Anssi; Seppä, Karri; Pasanen, Kari; Kurttio, Päivi; Patama, Toni; Pukkala, Eero; Heinävaara, Sirpa; Arvela, Hannu; Verkasalo, Pia; Hakulinen, Timo

    2014-05-01

    Twenty-five years have passed since the Chernobyl accident, but its health consequences remain to be well established. Finland was one of the most heavily affected countries by the radioactive fallout outside the former Soviet Union. We analyzed the relation of the estimated external radiation exposure from the fallout to cancer incidence in Finland in 1988-2007. The study cohort comprised all ∼ 3.8 million Finns who had lived in the same dwelling for 12 months following the accident (May 1986-April 1987). Radiation exposure was estimated using data from an extensive mobile dose rate survey. Cancer incidence data were obtained for the cohort divided into four exposure categories (the lowest with the first-year committed dose <0.1 mSv and the highest ≥ 0.5 mSv) allowing for a latency of 5 years for leukemia and thyroid cancer, and 10 years for other cancers. Of the eight predefined cancer sites regarded as radiation-related from earlier studies, only colon cancer among women showed an association with exposure from fallout [excess rate ratio per increment in exposure category 0.06, 95% confidence interval (CI) 0.02-0.11]. No such effect was observed for men, or other cancer sites. Our analysis of a large cohort over two decades did not reveal an increase in cancer incidence following the Chernobyl accident, with the possible exception of colon cancer among women. The largely null findings are consistent with extrapolation from previous studies suggesting that the effect is likely to remain too small to be empirically detectable and of little public health impact.

  2. Information sources in science and technology in Finland

    NASA Technical Reports Server (NTRS)

    Haarala, Arja-Riitta

    1994-01-01

    Finland poses some problems to be overcome in the field of scientific and technical information: a small user community which makes domestic systems costly; great distances within the country between users and suppliers of information; great distances to international data systems and large libraries abroad; and inadequate collections of scientific and technical information. The national bibliography Fennica includes all books and journals published in Finland. Data base services available in Finland include: reference data bases in science and technology; data banks for decision making such as statistical time series or legal proceedings; national bibliographies; and library catalogs.

  3. Causal systems categories: differences in novice and expert categorization of causal phenomena.

    PubMed

    Rottman, Benjamin M; Gentner, Dedre; Goldwater, Micah B

    2012-07-01

    We investigated the understanding of causal systems categories--categories defined by common causal structure rather than by common domain content--among college students. We asked students who were either novices or experts in the physical sciences to sort descriptions of real-world phenomena that varied in their causal structure (e.g., negative feedback vs. causal chain) and in their content domain (e.g., economics vs. biology). Our hypothesis was that there would be a shift from domain-based sorting to causal sorting with increasing expertise in the relevant domains. This prediction was borne out: the novice groups sorted primarily by domain and the expert group sorted by causal category. These results suggest that science training facilitates insight about causal structures.

  4. Spread of entanglement and causality

    NASA Astrophysics Data System (ADS)

    Casini, Horacio; Liu, Hong; Mezei, Márk

    2016-07-01

    We investigate causality constraints on the time evolution of entanglement entropy after a global quench in relativistic theories. We first provide a general proof that the so-called tsunami velocity is bounded by the speed of light. We then generalize the free particle streaming model of [1] to general dimensions and to an arbitrary entanglement pattern of the initial state. In more than two spacetime dimensions the spread of entanglement in these models is highly sensitive to the initial entanglement pattern, but we are able to prove an upper bound on the normalized rate of growth of entanglement entropy, and hence the tsunami velocity. The bound is smaller than what one gets for quenches in holographic theories, which highlights the importance of interactions in the spread of entanglement in many-body systems. We propose an interacting model which we believe provides an upper bound on the spread of entanglement for interacting relativistic theories. In two spacetime dimensions with multiple intervals, this model and its variations are able to reproduce intricate results exhibited by holographic theories for a significant part of the parameter space. For higher dimensions, the model bounds the tsunami velocity at the speed of light. Finally, we construct a geometric model for entanglement propagation based on a tensor network construction for global quenches.

  5. Attitudes toward Others Depend upon Self and Other Causal Uncertainty

    PubMed Central

    Tobin, Stephanie J.; Osika, Matylda M.; McLanders, Mia

    2014-01-01

    People who are high in causal uncertainty doubt their own ability to understand the causes of social events. In three studies, we examined the effects of target and perceiver causal uncertainty on attitudes toward the target. Target causal uncertainty was manipulated via responses on a causal uncertainty scale in Studies 1 and 2, and with a scenario in Study 3. In Studies 1 and 2, we found that participants liked the low causal uncertainty target more than the high causal uncertainty target. This preference was stronger for low relative to high causal uncertainty participants because high causal uncertainty participants held more uncertain ideals. In Study 3, we examined the value individuals place upon causal understanding (causal importance) as an additional moderator. We found that regardless of their own causal uncertainty level, participants who were high in causal importance liked the low causal uncertainty target more than the high causal uncertainty target. However, when participants were low in causal importance, low causal uncertainty perceivers showed no preference and high causal uncertainty perceivers preferred the high causal uncertainty target. These findings reveal that goal importance and ideals can influence how perceivers respond to causal uncertainty in others. PMID:24504048

  6. Attitudes toward others depend upon self and other causal uncertainty.

    PubMed

    Tobin, Stephanie J; Osika, Matylda M; McLanders, Mia

    2014-01-01

    People who are high in causal uncertainty doubt their own ability to understand the causes of social events. In three studies, we examined the effects of target and perceiver causal uncertainty on attitudes toward the target. Target causal uncertainty was manipulated via responses on a causal uncertainty scale in Studies 1 and 2, and with a scenario in Study 3. In Studies 1 and 2, we found that participants liked the low causal uncertainty target more than the high causal uncertainty target. This preference was stronger for low relative to high causal uncertainty participants because high causal uncertainty participants held more uncertain ideals. In Study 3, we examined the value individuals place upon causal understanding (causal importance) as an additional moderator. We found that regardless of their own causal uncertainty level, participants who were high in causal importance liked the low causal uncertainty target more than the high causal uncertainty target. However, when participants were low in causal importance, low causal uncertainty perceivers showed no preference and high causal uncertainty perceivers preferred the high causal uncertainty target. These findings reveal that goal importance and ideals can influence how perceivers respond to causal uncertainty in others.

  7. Benchmark Study of Industrial Needs for Additive Manufacturing in Finland

    NASA Astrophysics Data System (ADS)

    Lindqvist, Markku; Piili, Heidi; Salminen, Antti

    Additive manufacturing (AM) is a modern way to produce parts for industrial use. Even though the technical knowledge and research of AM processes are strong in Finland, there are only few industrial applications. Aim of this study is to collect practical knowledge of companies who are interested in industrial use of AM, especially in South-Eastern Finland. Goal of this study is also to investigate demands and requirements of applications for industrial use of AM in this area of Finland. It was concluded, that two of the reasons prohibiting wider industrial use of AM in Finland, are wrong expectations against this technology as well as lack of basic knowledge of possibilities of the technology. Especially, it was noticed that strong 3D-hype is even causing misunderstandings. Nevertheless, the high-level industrial know-how in the area, built around Finnish lumber industry is a strong foundation for the additive manufacturing technology.

  8. Causal localizations in relativistic quantum mechanics

    SciTech Connect

    Castrigiano, Domenico P. L. Leiseifer, Andreas D.

    2015-07-15

    Causal localizations describe the position of quantum systems moving not faster than light. They are constructed for the systems with finite spinor dimension. At the center of interest are the massive relativistic systems. For every positive mass, there is the sequence of Dirac tensor-localizations, which provides a complete set of inequivalent irreducible causal localizations. They obey the principle of special relativity and are fully Poincaré covariant. The boosters are determined by the causal position operator and the other Poincaré generators. The localization with minimal spinor dimension is the Dirac localization. Thus, the Dirac equation is derived here as a mere consequence of the principle of causality. Moreover, the higher tensor-localizations, not known so far, follow from Dirac’s localization by a simple construction. The probability of localization for positive energy states results to be described by causal positive operator valued (PO-) localizations, which are the traces of the causal localizations on the subspaces of positive energy. These causal Poincaré covariant PO-localizations for every irreducible massive relativistic system were, all the more, not known before. They are shown to be separated. Hence, the positive energy systems can be localized within every open region by a suitable preparation as accurately as desired. Finally, the attempt is made to provide an interpretation of the PO-localization operators within the frame of conventional quantum mechanics attributing an important role to the negative energy states.

  9. Quantum correlations with no causal order

    PubMed Central

    Oreshkov, Ognyan; Costa, Fabio; Brukner, Časlav

    2012-01-01

    The idea that events obey a definite causal order is deeply rooted in our understanding of the world and at the basis of the very notion of time. But where does causal order come from, and is it a necessary property of nature? Here, we address these questions from the standpoint of quantum mechanics in a new framework for multipartite correlations that does not assume a pre-defined global causal structure but only the validity of quantum mechanics locally. All known situations that respect causal order, including space-like and time-like separated experiments, are captured by this framework in a unified way. Surprisingly, we find correlations that cannot be understood in terms of definite causal order. These correlations violate a 'causal inequality' that is satisfied by all space-like and time-like correlations. We further show that in a classical limit causal order always arises, which suggests that space-time may emerge from a more fundamental structure in a quantum-to-classical transition. PMID:23033068

  10. An autochthonous case of cystic echinococcosis in Finland, 2015.

    PubMed

    Hämäläinen, Sari; Kantele, Anu; Arvonen, Miika; Hakala, Tapio; Karhukorpi, Jari; Heikkinen, Jukka; Berg, Ensio; Vanamo, Kari; Tyrväinen, Erja; Heiskanen-Kosma, Tarja; Oksanen, Antti; Lavikainen, Antti

    2015-01-01

    We report a case of pulmonary cystic echinococcosis in a child from eastern Finland with no history of travelling abroad. The cyst was surgically removed and the organism molecularly identified as Echinococcus canadensis genotype G10. This parasite is maintained in eastern Finland in a sylvatic life cycle involving wolves and moose; in the present case, the infection was presumably transmitted by hunting dogs. PMID:26538367

  11. Perception of causality in schizophrenia spectrum disorder.

    PubMed

    Tschacher, Wolfgang; Kupper, Zeno

    2006-10-01

    Patients with schizophrenia spectrum disorders often maintain deviating views on cause-effect relationships, especially when positive and disorganization symptoms are manifest. Altered perceived causality is prominent in delusional ideation, in ideas of reference, and in the mentalizing ability (theory of mind [ToM]) of patients. Perceiving causal relationships may be understood either as higher order cognitive reasoning or as low-level information processing. In the present study, perception of causality was investigated as a low-level, preattentional capability similar to gestalt-like perceptual organization. Thirty-one patients (24 men and 7 women with mean age 27.7 years) and the same number of healthy control subjects matched to patients with respect to age and sex were tested. A visual paradigm was used in which 2 identical discs move, from opposite sides of a monitor, steadily toward and then past one another. Their coincidence generates an ambiguous, bistable percept (discs either "stream through" or "bounce off" one another). The bouncing perception, ie, perceived causality, is enhanced when auditory stimuli are presented at the time of coincidence. Psychopathology was measured using the Positive and Negative Syndrome Scale. It was found that positive symptoms were strongly associated with increased perceived causality and disorganization with attenuated perceived causality. Patients in general were not significantly different from controls, but symptom subgroups showed specifically altered perceived causality. Perceived causality as a basic preattentional process may contribute to higher order cognitive alterations and ToM deficiencies. It is suggested that cognitive remediation therapy should address both increased and reduced perception of causality. PMID:16896057

  12. On the origin of Hill's causal criteria.

    PubMed

    Morabia, A

    1991-09-01

    The rules to assess causation formulated by the eighteenth century Scottish philosopher David Hume are compared to Sir Austin Bradford Hill's causal criteria. The strength of the analogy between Hume's rules and Hill's causal criteria suggests that, irrespective of whether Hume's work was known to Hill or Hill's predecessors, Hume's thinking expresses a point of view still widely shared by contemporary epidemiologists. The lack of systematic experimental proof to causal inferences in epidemiology may explain the analogy of Hume's and Hill's, as opposed to Popper's, logic.

  13. Causality and methodology. Notes on thanatochronological estimations.

    PubMed

    Boniolo, Giovanni; Libero, Mirella; Aprile, Anna

    2005-01-01

    The authors propose some methodological considerations on thanatochronological estimations. They first consider the problem of the definition of death, and then they deal with the issue of the estimations of death time, that is, with the Post-Mortem Interval (PMI). As regards the first question, they note that it does not concern only the definition of death, but also the choice of a particular kind of definition of 'definition'. With reference to the second question, the authors suggest a causal model showing that the presence of many causal chains must be taken into consideration. Finally they discuss what 'most convenient and reliable causal chain' means for a thanatochronologist.

  14. Short-term dynamics of causal information transfer in thalamocortical networks during natural inputs and microstimulation for somatosensory neuroprosthesis.

    PubMed

    Semework, Mulugeta; DiStasio, Marcello

    2014-01-01

    Recording the activity of large populations of neurons requires new methods to analyze and use the large volumes of time series data thus created. Fast and clear methods for finding functional connectivity are an important step toward the goal of understanding neural processing. This problem presents itself readily in somatosensory neuroprosthesis (SSNP) research, which uses microstimulation (MiSt) to activate neural tissue to mimic natural stimuli, and has the capacity to potentiate, depotentiate, or even destroy functional connections. As the aim of SSNP engineering is artificially creating neural responses that resemble those observed during natural inputs, a central goal is describing the influence of MiSt on activity structure among groups of neurons, and how this structure may be altered to affect perception or behavior. In this paper, we demonstrate the concept of Granger causality, combined with maximum likelihood methods, applied to neural signals recorded before, during, and after natural and electrical stimulation. We show how these analyses can be used to evaluate the changing interactions in the thalamocortical somatosensory system in response to repeated perturbation. Using LFPs recorded from the ventral posterolateral thalamus (VPL) and somatosensory cortex (S1) in anesthetized rats, we estimated pair-wise functional interactions between functional microdomains. The preliminary results demonstrate input-dependent modulations in the direction and strength of information flow during and after application of MiSt. Cortico-cortical interactions during cortical MiSt and baseline conditions showed the largest causal influence differences, while there was no statistically significant difference between pre- and post-stimulation baseline causal activities. These functional connectivity changes agree with physiologically accepted communication patterns through the network, and their particular parameters have implications for both rehabilitation and brain

  15. The gravity dual of boundary causality

    NASA Astrophysics Data System (ADS)

    Engelhardt, Netta; Fischetti, Sebastian

    2016-09-01

    In gauge/gravity duality, points which are not causally related on the boundary cannot be causally related through the bulk; this is the statement of boundary causality. By the Gao-Wald theorem, the averaged null energy condition in the bulk is sufficient to ensure this property. Here we proceed in the converse direction: we derive a necessary as well as sufficient condition for the preservation of boundary causality under perturbative (quantum or stringy) corrections to the bulk. The condition that we find is a (background-dependent) constraint on the amount by which light cones can ‘open’ over all null bulk geodesics. We show that this constraint is weaker than the averaged null energy condition.

  16. Risk and causality in newspaper reporting.

    PubMed

    Boholm, Max

    2009-11-01

    The study addresses the textual representation of risk and causality in news media reporting. The analytical framework combines two theoretical perspectives: media frame analysis and the philosophy of causality. Empirical data derive from selected newspaper articles on risks in the Göta älv river valley in southwest Sweden from 1994 to 2007. News media content was coded and analyzed with respect to causal explanations of risk issues. At the level of individual articles, this study finds that the media provide simple causal explanations of risks such as water pollution, landslides, and flooding. Furthermore, these explanations are constructed, or framed, in various ways, the same risk being attributed to different causes in different articles. However, the study demonstrates that a fairly complex picture of risks in the media emerges when extensive material is analyzed systematically.

  17. Rate-Agnostic (Causal) Structure Learning

    PubMed Central

    Plis, Sergey; Danks, David; Freeman, Cynthia; Calhoun, Vince

    2016-01-01

    Causal structure learning from time series data is a major scientific challenge. Extant algorithms assume that measurements occur sufficiently quickly; more precisely, they assume approximately equal system and measurement timescales. In many domains, however, measurements occur at a significantly slower rate than the underlying system changes, but the size of the timescale mismatch is often unknown. This paper develops three causal structure learning algorithms, each of which discovers all dynamic causal graphs that explain the observed measurement data, perhaps given undersampling. That is, these algorithms all learn causal structure in a “rate-agnostic” manner: they do not assume any particular relation between the measurement and system timescales. We apply these algorithms to data from simulations to gain insight into the challenge of undersampling. PMID:27182188

  18. Quantum probability assignment limited by relativistic causality

    PubMed Central

    Han, Yeong Deok; Choi, Taeseung

    2016-01-01

    Quantum theory has nonlocal correlations, which bothered Einstein, but found to satisfy relativistic causality. Correlation for a shared quantum state manifests itself, in the standard quantum framework, by joint probability distributions that can be obtained by applying state reduction and probability assignment that is called Born rule. Quantum correlations, which show nonlocality when the shared state has an entanglement, can be changed if we apply different probability assignment rule. As a result, the amount of nonlocality in quantum correlation will be changed. The issue is whether the change of the rule of quantum probability assignment breaks relativistic causality. We have shown that Born rule on quantum measurement is derived by requiring relativistic causality condition. This shows how the relativistic causality limits the upper bound of quantum nonlocality through quantum probability assignment. PMID:26971717

  19. The gravity dual of boundary causality

    NASA Astrophysics Data System (ADS)

    Engelhardt, Netta; Fischetti, Sebastian

    2016-09-01

    In gauge/gravity duality, points which are not causally related on the boundary cannot be causally related through the bulk; this is the statement of boundary causality. By the Gao–Wald theorem, the averaged null energy condition in the bulk is sufficient to ensure this property. Here we proceed in the converse direction: we derive a necessary as well as sufficient condition for the preservation of boundary causality under perturbative (quantum or stringy) corrections to the bulk. The condition that we find is a (background-dependent) constraint on the amount by which light cones can ‘open’ over all null bulk geodesics. We show that this constraint is weaker than the averaged null energy condition.

  20. Singular clues to causality and their use in human causal judgment.

    PubMed

    White, Peter A

    2014-01-01

    It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as heuristics for generating causal judgments under uncertainty and are a pervasive source of bias in causal judgment. More sophisticated clues such as mechanism clues and repeated interventions are derived from the 14. Research on the use of empirical information and conditional probabilities to identify causes has used scenarios in which several of the clues are present, and the use of empirical association information for causal judgment depends on the presence of singular clues. It is the singular clues and their origin that are basic to causal understanding, not multiple instance clues such as empirical association, contingency, and conditional probabilities. PMID:23957568

  1. [The plague in Finland in 1710].

    PubMed

    Engström, N G

    1994-01-01

    In the autumn of 1710 Helsinki was struck by the so-called oriental plague during four months. The infection was transferred by black rats which harboured fleas. The flea-bites caused boils. It was believed that the plague was air-borne, and the air was very humid that autumn. Big fires were lit in order to reduce the humidity, the purpose being to make it easier for the infected to breathe. Attempts were also made to dissect the boils. The carriers of the contamination came as refugees from Estland over the Gulf of Finland. The infection had spread from Turkey to Poland and Balticum after the defeat of the Finnish-Swedish army in the summer of 1709 at Poltava in Ucraine. Helsingfors (Helsinki) was struck extremely hard. About two-thirds of the inhabitants died of the pestilence. Some escaped by fleeing to the countryside. The plague spread through the country as far north as to Uleåborg (Oulu) and Cajana (Kajaani). Marketplaces became important centres of infection. With the advent of the frost in December the plague dwindled. At that time Helsinki was practically a dead town. PMID:11640321

  2. Phenology cameras observing boreal ecosystems of Finland

    NASA Astrophysics Data System (ADS)

    Peltoniemi, Mikko; Böttcher, Kristin; Aurela, Mika; Kolari, Pasi; Tanis, Cemal Melih; Linkosalmi, Maiju; Loehr, John; Metsämäki, Sari; Nadir Arslan, Ali

    2016-04-01

    Cameras have become useful tools for monitoring seasonality of ecosystems. Low-cost cameras facilitate validation of other measurements and allow extracting some key ecological features and moments from image time series. We installed a network of phenology cameras at selected ecosystem research sites in Finland. Cameras were installed above, on the level, or/and below the canopies. Current network hosts cameras taking time lapse images in coniferous and deciduous forests as well as at open wetlands offering thus possibilities to monitor various phenological and time-associated events and elements. In this poster, we present our camera network and give examples of image series use for research. We will show results about the stability of camera derived color signals, and based on that discuss about the applicability of cameras in monitoring time-dependent phenomena. We will also present results from comparisons between camera-derived color signal time series and daily satellite-derived time series (NVDI, NDWI, and fractional snow cover) from the Moderate Resolution Imaging Spectrometer (MODIS) at selected spruce and pine forests and in a wetland. We will discuss the applicability of cameras in supporting phenological observations derived from satellites, by considering the possibility of cameras to monitor both above and below canopy phenology and snow.

  3. Kant on causal laws and powers.

    PubMed

    Henschen, Tobias

    2014-12-01

    The aim of the paper is threefold. Its first aim is to defend Eric Watkins's claim that for Kant, a cause is not an event but a causal power: a power that is borne by a substance, and that, when active, brings about its effect, i.e. a change of the states of another substance, by generating a continuous flow of intermediate states of that substance. The second aim of the paper is to argue against Watkins that the Kantian concept of causal power is not the pre-critical concept of real ground but the category of causality, and that Kant holds with Hume that causal laws cannot be inferred non-inductively (that he accordingly has no intention to show in the Second analogy or elsewhere that events fall under causal laws). The third aim of the paper is to compare the Kantian position on causality with central tenets of contemporary powers ontology: it argues that unlike the variants endorsed by contemporary powers theorists, the Kantian variants of these tenets are resistant to objections that neo-Humeans raise to these tenets. PMID:25571742

  4. Causal Inference for Vaccine Effects on Infectiousness

    PubMed Central

    Halloran, M. Elizabeth; Hudgens, Michael G.

    2012-01-01

    If a vaccine does not protect individuals completely against infection, it could still reduce infectiousness of infected vaccinated individuals to others. Typically, vaccine efficacy for infectiousness is estimated based on contrasts between the transmission risk to susceptible individuals from infected vaccinated individuals compared with that from infected unvaccinated individuals. Such estimates are problematic, however, because they are subject to selection bias and do not have a causal interpretation. Here, we develop causal estimands for vaccine efficacy for infectiousness for four different scenarios of populations of transmission units of size two. These causal estimands incorporate both principal stratification, based on the joint potential infection outcomes under vaccine and control, and interference between individuals within transmission units. In the most general scenario, both individuals can be exposed to infection outside the transmission unit and both can be assigned either vaccine or control. The three other scenarios are special cases of the general scenario where only one individual is exposed outside the transmission unit or can be assigned vaccine. The causal estimands for vaccine efficacy for infectiousness are well defined only within certain principal strata and, in general, are identifiable only with strong unverifiable assumptions. Nonetheless, the observed data do provide some information, and we derive large sample bounds on the causal vaccine efficacy for infectiousness estimands. An example of the type of data observed in a study to estimate vaccine efficacy for infectiousness is analyzed in the causal inference framework we developed. PMID:22499732

  5. Causality violation, gravitational shockwaves and UV completion

    NASA Astrophysics Data System (ADS)

    Hollowood, Timothy J.; Shore, Graham M.

    2016-03-01

    The effective actions describing the low-energy dynamics of QFTs involving gravity generically exhibit causality violations. These may take the form of superluminal propagation or Shapiro time advances and allow the construction of "time machines", i.e. spacetimes admitting closed non-spacelike curves. Here, we discuss critically whether such causality violations may be used as a criterion to identify unphysical effective actions or whether, and how, causality problems may be resolved by embedding the action in a fundamental, UV complete QFT. We study in detail the case of photon scattering in an Aichelburg-Sexl gravitational shockwave background and calculate the phase shifts in QED for all energies, demonstrating their smooth interpolation from the causality-violating effective action values at low-energy to their manifestly causal high-energy limits. At low energies, these phase shifts may be interpreted as backwards-in-time coordinate jumps as the photon encounters the shock wavefront, and we illustrate how the resulting causality problems emerge and are resolved in a two-shockwave time machine scenario. The implications of our results for ultra-high (Planck) energy scattering, in which graviton exchange is modelled by the shockwave background, are highlighted.

  6. The role of causal criteria in causal inferences: Bradford Hill's "aspects of association"

    PubMed Central

    Ward, Andrew C

    2009-01-01

    As noted by Wesley Salmon and many others, causal concepts are ubiquitous in every branch of theoretical science, in the practical disciplines and in everyday life. In the theoretical and practical sciences especially, people often base claims about causal relations on applications of statistical methods to data. However, the source and type of data place important constraints on the choice of statistical methods as well as on the warrant attributed to the causal claims based on the use of such methods. For example, much of the data used by people interested in making causal claims come from non-experimental, observational studies in which random allocations to treatment and control groups are not present. Thus, one of the most important problems in the social and health sciences concerns making justified causal inferences using non-experimental, observational data. In this paper, I examine one method of justifying such inferences that is especially widespread in epidemiology and the health sciences generally – the use of causal criteria. I argue that while the use of causal criteria is not appropriate for either deductive or inductive inferences, they do have an important role to play in inferences to the best explanation. As such, causal criteria, exemplified by what Bradford Hill referred to as "aspects of [statistical] associations", have an indispensible part to play in the goal of making justified causal claims. PMID:19534788

  7. Checklist of tapeworms (Platyhelminthes, Cestoda) of vertebrates in Finland

    PubMed Central

    Haukisalmi, Voitto

    2015-01-01

    Abstract A checklist of tapeworms (Cestoda) of vertebrates (fishes, birds and mammals) in Finland is presented, based on published observations, specimens deposited in the collections of the Finnish Museum of Natural History (Helsinki) and the Zoological Museum of the University of Turku, and additional specimens identified by the present author. The checklist includes 170 tapeworm species from 151 host species, comprising 447 parasite species/host species combinations. Thirty of the tapeworm species and 96 of the parasite/host species combinations have not been previously reported from Finland. The total number of tapeworm species in Finland (170 spp.) is significantly lower than the corresponding figure for the Iberian Peninsula (257 spp.), Slovakia (225 spp.) and Poland (279 spp.). The difference between Finland and the other three regions is particularly pronounced for anseriform, podicipediform, charadriiform and passeriform birds, reflecting inadequate and/or biased sampling of these birds in Finland. It is predicted that there are actually ca. 270 species of tapeworms in Finland, assuming that true number of bird tapeworms in Finland corresponds to that in other European countries with more comprehensive knowledge of the local tapeworm fauna. The other main pattern emerging from the present data is the seemingly unexplained absence in (northern) Fennoscandia of several mammalian tapeworms that otherwise have extensive distributions in the Holarctic region or in Eurasia, including the northern regions. Previously unknown type specimens, that is, the holotype of Bothrimonus nylandicus Schneider, 1902 (a junior synonym of Diplocotyle olrikii Krabbe, 1874) (MZH 127096) and the syntypes of Caryophyllaeides fennica (Schneider, 1902) (MZH 127097) were located in the collections of the Finnish Museum of Natural History. PMID:26668540

  8. Tobacco industry strategy to undermine tobacco control in Finland

    PubMed Central

    Hiilamo, H

    2003-01-01

    Objective: To identify and explain tobacco industry strategy in undermining tobacco control measures in Finland and results of these interferences in tobacco policy development during the 1980s and early 1990s. Methods: Tobacco industry documents, which have been publicly available on the internet as a result of litigation in the USA, were analysed. Documents were sought by Finland and by names of organisations and tobacco control activists. Documents were accessed and assessed between September 2000 and November 2002. Tactics of the tobacco industry activities were categorised as presented by Saloojee and Dagli. Results: The international tobacco companies utilised similar strategies in Finland as in other industrial markets to fight tobacco control and legislation, the health advocacy movement, and litigation. These activities slowed down the development and implementation of the Tobacco Act in Finland. However, despite the extensive pressure, the industry was not able to prevent the most progressive tobacco legislation in Europe from being passed and coming into force in Finland in 1977 and in 1995. Conclusion: Denying the health hazards caused by tobacco—despite indisputable scientific evidence—decreased the credibility of the tobacco industry. Strategy of denial was falsely chosen, as health advocacy groups were active both in society and the parliamentary system. The strong influence of the tobacco industry may have in fact increased the visibility of tobacco control in Finland as the litigation process was also drawing attention to negative health effects of tobacco. Therefore the tobacco industry did not manage to convince public opinion. However, the tobacco industry did obtain experience in Finland in how to object to tobacco control measures. PMID:14660780

  9. Illness causal beliefs in Turkish immigrants

    PubMed Central

    Minas, Harry; Klimidis, Steven; Tuncer, Can

    2007-01-01

    Background People hold a wide variety of beliefs concerning the causes of illness. Such beliefs vary across cultures and, among immigrants, may be influenced by many factors, including level of acculturation, gender, level of education, and experience of illness and treatment. This study examines illness causal beliefs in Turkish-immigrants in Australia. Methods Causal beliefs about somatic and mental illness were examined in a sample of 444 members of the Turkish population of Melbourne. The socio-demographic characteristics of the sample were broadly similar to those of the Melbourne Turkish community. Five issues were examined: the structure of causal beliefs; the relative frequency of natural, supernatural and metaphysical beliefs; ascription of somatic, mental, or both somatic and mental conditions to the various causes; the correlations of belief types with socio-demographic, modernizing and acculturation variables; and the relationship between causal beliefs and current illness. Results Principal components analysis revealed two broad factors, accounting for 58 percent of the variation in scores on illness belief scales, distinctly interpretable as natural and supernatural beliefs. Second, beliefs in natural causes were more frequent than beliefs in supernatural causes. Third, some causal beliefs were commonly linked to both somatic and mental conditions while others were regarded as more specific to either somatic or mental disorders. Last, there was a range of correlations between endorsement of belief types and factors defining heterogeneity within the community, including with demographic factors, indicators of modernizing and acculturative processes, and the current presence of illness. Conclusion Results supported the classification of causal beliefs proposed by Murdock, Wilson & Frederick, with a division into natural and supernatural causes. While belief in natural causes is more common, belief in supernatural causes persists despite modernizing and

  10. Linear structures, causal sets and topology

    NASA Astrophysics Data System (ADS)

    Hudetz, Laurenz

    2015-11-01

    Causal set theory and the theory of linear structures (which has recently been developed by Tim Maudlin as an alternative to standard topology) share some of their main motivations. In view of that, I raise and answer the question how these two theories are related to each other and to standard topology. I show that causal set theory can be embedded into Maudlin's more general framework and I characterise what Maudlin's topological concepts boil down to when applied to discrete linear structures that correspond to causal sets. Moreover, I show that all topological aspects of causal sets that can be described in Maudlin's theory can also be described in the framework of standard topology. Finally, I discuss why these results are relevant for evaluating Maudlin's theory. The value of this theory depends crucially on whether it is true that (a) its conceptual framework is as expressive as that of standard topology when it comes to describing well-known continuous as well as discrete models of spacetime and (b) it is even more expressive or fruitful when it comes to analysing topological aspects of discrete structures that are intended as models of spacetime. On one hand, my theorems support (a). The theory is rich enough to incorporate causal set theory and its definitions of topological notions yield a plausible outcome in the case of causal sets. On the other hand, the results undermine (b). Standard topology, too, has the conceptual resources to capture those topological aspects of causal sets that are analysable within Maudlin's framework. This fact poses a challenge for the proponents of Maudlin's theory to prove it fruitful.

  11. Aquifer Properties in Hepokangas, Northern Finland

    NASA Astrophysics Data System (ADS)

    Pihlaja, M. Sc.

    2012-04-01

    Hepokangas study area is located in northern Finland, app. 60 km north-east of the city of Oulu. It consists of an esker ridge which ranges in elevation from 95 to 105 m.a.s.l. Consequently, all Quaternary deposits in the area have been influenced by erosional and depositional processes during two Baltic Sea stages (Ancylus Lake and Littorina Sea). Therefore, raised beaches are found on the esker slopes and fine grained sediments on the lowlands. The studied aquifer, the Hepokangas esker is part of an discontinuous chain of eskers which, in total, is about 100 km long and is elongated from north-west to south-east. The direction indicates that the esker was deposited by the melt waters during the latest phase of Weichselian glaciation. The primary part of the esker is located in the western segment of the area and a delta-like expansion of an esker is in the eastern part of the study area . Level of the ground water table (GWT) was measured at 14 ground water pipes which were located in varying parts of the Hepokangas formation. Ground penetrating radar (GPR) surveys were conducted on the primary part of the esker in order to determine internal structures and estimate permeability of the formation. Ground water flow directions were interpreted based on these measurements. The GWT varies from 91.91 to 97.98 m.a.s.l. Since the Hepokangas formation is surrounded by mires the height of the GWT decreased towards them. There was a water pumping station on the primary part of the formation, but no clear effect to the GWT could be seen to be caused by that. From the GPR results, some locations of the coarse grain sediments with high permeability were found.

  12. Upgrading the Northern Finland Seismological Network

    NASA Astrophysics Data System (ADS)

    Narkilahti, Janne; Kozlovskaya, Elena; Silvennoinen, Hanna; Hurskainen, Riitta; Nevalainen, Jouni

    2016-04-01

    The Finnish National Seismic Network (FNSN) comprises national Helsinki University Seismological network (HE) ISUH and the Northern Finland Seismological Network (FN) hosted by the Sodankylä Geophysical Observatory (SGO) of the University of Oulu. The FN network currently consists of four real-time permanent stations equipped with Streckeisen STS-2 broad band seismometers that are recording continuous digital seismic data. At present, the network is a part of GEOFON Extended Virtual Network and of the ORFEUS Virtual European Broadband Seismograph Network. In the future, the network will be the part of EPOS-European Plate Observing System research infrastructure. As a part of EPOS project activities, the SGO started to upgrade their own network in 2014. The main target of the network upgrade is to increase the permanent station coverage in the European Arctic region, particularly behind the Polar Circle. Another target is to transform the network into a broadband seismic array capable to detect long-period seismic signals originating from seismic events in the Arctic. The first upgrade phase started in 2014, when two new stations were installed and now are working in the test regime. These stations are used as prototypes for testing seismic equipment and technical solutions for real-time data transmission and vault construction under cold climate conditions. The first prototype station is installed in a surface vault and equipped with Nanometrics Trillium 120P sensor, while the other one is installed in a borehole and equipped with Trillium Posthole seismometer. These prototype stations have provided to us valuable experience on the downhole and surface deployment of broadband seismic instruments. We also have been able to compare the capabilities and performance of high sensitivity broadband sensor deployed in borehole with that deployed in surface vault. The results of operation of prototype stations will be used in site selection and installation of four new

  13. Singular Clues to Causality and Their Use in Human Causal Judgment

    ERIC Educational Resources Information Center

    White, Peter A.

    2014-01-01

    It is argued that causal understanding originates in experiences of acting on objects. Such experiences have consistent features that can be used as clues to causal identification and judgment. These are singular clues, meaning that they can be detected in single instances. A catalog of 14 singular clues is proposed. The clues function as…

  14. The Cradle of Causal Reasoning: Newborns' Preference for Physical Causality

    ERIC Educational Resources Information Center

    Mascalzoni, Elena; Regolin, Lucia; Vallortigara, Giorgio; Simion, Francesca

    2013-01-01

    Perception of mechanical (i.e. physical) causality, in terms of a cause-effect relationship between two motion events, appears to be a powerful mechanism in our daily experience. In spite of a growing interest in the earliest causal representations, the role of experience in the origin of this sensitivity is still a matter of dispute. Here, we…

  15. Dimensions of Causal Understanding: The Role of Complex Causal Models in Students' Understanding of Science

    ERIC Educational Resources Information Center

    Perkins, David N.; Grotzer, Tina A.

    2005-01-01

    This article argues that an important source of the difficulties posed by particular concepts and theories is the narrow range of "types of causal models" with which most learners are familiar. Most learners are familiar with relatively simple styles of causal models, but many concepts and theories in science depend on styles substantially more…

  16. The Mental Representation of Causal Conditional Reasoning: Mental Models or Causal Models

    ERIC Educational Resources Information Center

    Ali, Nilufa; Chater, Nick; Oaksford, Mike

    2011-01-01

    In this paper, two experiments are reported investigating the nature of the cognitive representations underlying causal conditional reasoning performance. The predictions of causal and logical interpretations of the conditional diverge sharply when inferences involving "pairs" of conditionals--such as "if P[subscript 1] then Q" and "if P[subscript…

  17. Causal impressions: predicting when, not just whether.

    PubMed

    Young, Michael E; Rogers, Ester T; Beckmann, Joshua S

    2005-03-01

    In 1739, David Hume established the so-called cues to causality--environmental cues that are important to the inference of causality. Although this descriptive account has been corroborated experimentally, it has not been established why these cues are useful, except that they may reflect statistical regularities in the environment. One of the cues to causality, covariation, helps predict whether an effect will occur, but not its time of occurrence. In the present study, evidence is provided that spatial and temporal contiguity improve an observer's ability to predict when an effect will occur, thus complementing the utility of covariation as a predictor of whether an effect will occur. While observing Michotte's (1946/1963) launching effect, participants showed greater accuracy and precision in their predictions of the onset of movement by the launched object when there was spatial and temporal contiguity. Furthermore, when auditory cues that bridged a delayed launch were included, causal ratings and predictability were similarly affected. These results suggest that the everyday inference of causality relies on our ability to predict whether and when an effect will occur.

  18. Causal conjunction fallacies: the roles of causal strength and mental resources.

    PubMed

    Crisp, Aimée Kay; Feeney, Aidan

    2009-12-01

    In two experiments we tested the prediction derived from Tversky and Kahneman's (1983) work on the causal conjunction fallacy that the strength of the causal connection between constituent events directly affects the magnitude of the causal conjunction fallacy. We also explored whether any effects of perceived causal strength were due to graded output from heuristic Type 1 reasoning processes or the result of analytic Type 2 reasoning processes. As predicted, Experiment 1 demonstrated that fallacy rates were higher for strongly than for weakly related conjunctions. Weakly related conjunctions in turn attracted higher rates of fallacious responding than did unrelated conjunctions. Experiment 2 showed that a concurrent memory load increased rates of fallacious responding for strongly related but not for weakly related conjunctions. We interpret these results as showing that manipulations of the strength of the perceived causal relationship between the conjuncts result in graded output from heuristic reasoning process and that additional mental resources are required to suppress strong heuristic output.

  19. Checklist of the leaf-mining flies (Diptera, Agromyzidae) of Finland

    PubMed Central

    Kahanpää, Jere

    2014-01-01

    Abstract A checklist of the Agromyzidae (Diptera) recorded from Finland is presented. 279 (or 280) species are currently known from the country. Phytomyza linguae Lundqvist, 1947 is recorded as new to Finland. PMID:25337025

  20. Screening for late-onset Pompe disease in Finland.

    PubMed

    Palmio, Johanna; Auranen, Mari; Kiuru-Enari, Sari; Löfberg, Mervi; Bodamer, Olaf; Udd, Bjarne

    2014-11-01

    Pompe disease (glycogen storage disease type II) is caused by autosomal recessive mutations in GAA gene. The estimated frequency of late-onset Pompe disease is around 1:60,000. However, only two infantile and one late-onset Pompe patients have been reported in Finland with a population of 5 million. We screened for late-onset Pompe disease in a cohort of undetermined myopathy patients with proximal muscle weakness and/or elevated serum creatine kinase values. Acid α-glucosidase (GAA) activity in dried blood spots was measured and clinical data collected in 108 patients. Four patients had low normal GAA activity; all the others had activities well within the normal range. Re-analyses of these patients did not reveal new Pompe patients. Our findings suggest that Pompe disease is extremely rare in Finland. Finland is an example of an isolated population with enrichment of certain mutations for genetic disorders and low occurrence of some autosomal recessive diseases.

  1. Causal Phenotype Discovery via Deep Networks

    PubMed Central

    Kale, David C.; Che, Zhengping; Bahadori, Mohammad Taha; Li, Wenzhe; Liu, Yan; Wetzel, Randall

    2015-01-01

    The rapid growth of digital health databases has attracted many researchers interested in using modern computational methods to discover and model patterns of health and illness in a research program known as computational phenotyping. Much of the work in this area has focused on traditional statistical learning paradigms, such as classification, prediction, clustering, pattern mining. In this paper, we propose a related but different paradigm called causal phenotype discovery, which aims to discover latent representations of illness that are causally predictive. We illustrate this idea with a two-stage framework that combines the latent representation learning power of deep neural networks with state-of-the-art tools from causal inference. We apply this framework to two large ICU time series data sets and show that it can learn features that are predictively useful, that capture complex physiologic patterns associated with critical illnesses, and that are potentially more clinically meaningful than manually designed features. PMID:26958203

  2. Normalizing the causality between time series

    NASA Astrophysics Data System (ADS)

    Liang, X. San

    2015-08-01

    Recently, a rigorous yet concise formula was derived to evaluate information flow, and hence the causality in a quantitative sense, between time series. To assess the importance of a resulting causality, it needs to be normalized. The normalization is achieved through distinguishing a Lyapunov exponent-like, one-dimensional phase-space stretching rate and a noise-to-signal ratio from the rate of information flow in the balance of the marginal entropy evolution of the flow recipient. It is verified with autoregressive models and applied to a real financial analysis problem. An unusually strong one-way causality is identified from IBM (International Business Machines Corporation) to GE (General Electric Company) in their early era, revealing to us an old story, which has almost faded into oblivion, about "Seven Dwarfs" competing with a giant for the mainframe computer market.

  3. The causal meaning of Hamilton's rule.

    PubMed

    Okasha, Samir; Martens, Johannes

    2016-03-01

    Hamilton's original derivation of his rule for the spread of an altruistic gene (rb>c) assumed additivity of costs and benefits. Recently, it has been argued that an exact version of the rule holds under non-additive pay-offs, so long as the cost and benefit terms are suitably defined, as partial regression coefficients. However, critics have questioned both the biological significance and the causal meaning of the resulting rule. This paper examines the causal meaning of the generalized Hamilton's rule in a simple model, by computing the effect of a hypothetical experiment to assess the cost of a social action and comparing it to the partial regression definition. The two do not agree. A possible way of salvaging the causal meaning of Hamilton's rule is explored, by appeal to R. A. Fisher's 'average effect of a gene substitution'.

  4. Causal binding of actions to their effects.

    PubMed

    Buehner, Marc J; Humphreys, Gruffydd R

    2009-10-01

    According to widely held views in cognitive science harking back to David Hume, causality cannot be perceived directly, but instead is inferred from patterns of sensory experience, and the quality of these inferences is determined by perceivable quantities such as contingency and contiguity. We report results that suggest a reversal of Hume's conjecture: People's sense of time is warped by the experience of causality. In a stimulus-anticipation task, participants' response behavior reflected a shortened experience of time in the case of target stimuli participants themselves had generated, relative to equidistant, equally predictable stimuli they had not caused. These findings suggest that causality in the mind leads to temporal binding of cause and effect, and extend and generalize beyond earlier claims of intentional binding between action and outcome.

  5. The causal meaning of Hamilton's rule.

    PubMed

    Okasha, Samir; Martens, Johannes

    2016-03-01

    Hamilton's original derivation of his rule for the spread of an altruistic gene (rb>c) assumed additivity of costs and benefits. Recently, it has been argued that an exact version of the rule holds under non-additive pay-offs, so long as the cost and benefit terms are suitably defined, as partial regression coefficients. However, critics have questioned both the biological significance and the causal meaning of the resulting rule. This paper examines the causal meaning of the generalized Hamilton's rule in a simple model, by computing the effect of a hypothetical experiment to assess the cost of a social action and comparing it to the partial regression definition. The two do not agree. A possible way of salvaging the causal meaning of Hamilton's rule is explored, by appeal to R. A. Fisher's 'average effect of a gene substitution'. PMID:27069669

  6. Dietary changes in Finland--success stories and future challenges.

    PubMed

    Prättälä, Ritva

    2003-12-01

    The paper describes dietary changes and related nutrition policies and interventions in Finland since the 1960s. Dietary changes are interpreted from the lifestyle perspective, in which food consumption patterns are assumed to be formed by the interplay of individual choices and structural chances, such as socioeconomic and cultural conditions. Finland can demonstrate a success story when it comes to decreased use of dairy fats and increased use of vegetables and fruit. However, the prevalence of overweight has increased. Nutrition policies and interventions together with sociocultural factors have supported the shift towards healthy nutrition. The same factors have promoted overweight, as well.

  7. On causality in polymer scalar field theory

    NASA Astrophysics Data System (ADS)

    García-Chung, Angel A.; Morales-Técotl, Hugo A.

    2011-10-01

    The properties of spacetime corresponding to a proposed quantum gravity theory might modify the high energy behavior of quantum fields. Motivated by loop quantum gravity, recently, Hossain et al [1] have considered a polymer field algebra that replaces the standard canonical one in order to calculate the propagator of a real scalar field in flat spacetime. This propagator features Lorentz violations. Motivated by the relation between Lorentz invariance and causality in standard Quantum Field Theory, in this work we investigate the causality behavior of the polymer scalar field.

  8. 75 FR 32640 - Defense Federal Acquisition Regulation Supplement; Finland- Public Interest Exception to the Buy...

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-06-08

    ...; Finland-- Public Interest Exception to the Buy American Act AGENCY: Defense Acquisition Regulations System... the acquisition of articles, materials, and supplies produced or manufactured in Finland. DATES... government of Finland and the Government of the United States has been in effect since 1991. The...

  9. Age and Meanings of Violence: Women's Experiences of Partner Violence in Finland

    ERIC Educational Resources Information Center

    Piispa, Minna

    2004-01-01

    The first survey carried out in Finland specifically to study men's violence against women showed that partner violence is quite common in Finland and it is directed especially toward young women. The statistical findings don't support the idea that violence has become more widespread in Finland. Life situation factors that are usually viewed as…

  10. Century-scale causal relationships between global dry/wet conditions and the state of the Pacific and Atlantic Oceans

    NASA Astrophysics Data System (ADS)

    Sun, Qiaohong; Miao, Chiyuan; AghaKouchak, Amir; Duan, Qingyun

    2016-06-01

    The Granger causality test is used to examine the effects of the El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the North Atlantic Oscillation (NAO) on global dry/wet conditions. The results show robust relationships between dry/wet conditions and the ocean states, as assessed through a multi-index (standardized precipitation evapotranspiration index and standardized precipitation index) and multiscale (3 months and 12 months) evaluation. The influence of ENSO events is widespread, dominating about 38% of the global land surface (excluding Antarctica). Southern and western North America, northern South America, and eastern Russia are influenced by the PDO. The NAO influences not only dry/wet conditions in Europe but also dry/wet conditions in northern Africa. Similarly, climate variability in southern Europe and northern Africa may be due to the concurrence of the ENSO and the NAO. Knowledge of the spatial influence of ocean states on global dry/wet conditions is valuable for improving drought and flood forecasting.

  11. Century-scale causal relationships between global drought conditions and the state of the Pacific and Atlantic Oceans

    NASA Astrophysics Data System (ADS)

    Sun, Qiaohong; Miao, Chiyuan; Duan, Qingyun

    2016-04-01

    Drought is one of the costliest and least understood natural hazards. The El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) are atmosphere-ocean coupled modes of climate variability that occur in the Pacific and Atlantic Oceans. In this study, the Granger causality test is used to examine the effects of ENSO, PDO, and NAO on global drought conditions. The results show robust relationships between drought conditions and the ocean states, as assessed through a multi-index (SPEI and SPI) and multiscalar (3-month and 12-month) evaluation. The influence of ENSO events is widespread, dominating about 40% of the global land droughts. Southern and western North America, northern South America, and eastern Russia are more influenced by PDO. Results show that NAO influence on drought is not restricted to Europe and includes northern Africa. The role of NAO is most evident at 3-month time scale. Moreover, the results provide evidence that drought conditions can be affected by multiple factors. ENSO and PDO may reinforce each other to dominate climate variability over North America and northern South America. Climate variability in southern Europe and northern Africa may be forced by the concurrence of ENSO and NAO. The spatial patterns of the influence of ocean states on global droughts provide valuable information for improving drought forecasting.

  12. Children with well controlled epilepsy possess different spatio-temporal patterns of causal network connectivity during a visual working memory task.

    PubMed

    Protopapa, Foteini; Siettos, Constantinos I; Myatchin, Ivan; Lagae, Lieven

    2016-04-01

    Using spectral Granger causality (GC) we identified distinct spatio-temporal causal connectivity (CC) patterns in groups of control and epileptic children during the execution of a one-back matching visual discrimination working memory task. Differences between control and epileptic groups were determined for both GO and NOGO conditions. The analysis was performed on a set of 19-channel EEG cortical activity signals. We show that for the GO task, the highest brain activity in terms of the density of the CC networks is observed in α band for the control group while for the epileptic group the CC network seems disrupted as reflected by the small number of connections. For the NOGO task, the denser CC network was observed in θ band for the control group while widespread differences between the control and the epileptic group were located bilaterally at the left temporal-midline and parietal areas. In order to test the discriminative power of our analysis, we performed a pattern analysis approach based on fuzzy classification techniques. The performance of the classification scheme was evaluated using permutation tests. The analysis demonstrated that, on average, 87.6 % of the subjects were correctly classified in control and epileptic. Thus, our findings may provide a helpful insight on the mechanisms pertaining to the cognitive response of children with well controlled epilepsy and could potentially serve as "functional" biomarkers for early diagnosis.

  13. Pride and Prejudice and Causal Indicators

    ERIC Educational Resources Information Center

    Lee, Nick; Chamberlain, Laura

    2016-01-01

    Aguirre-Urreta, Rönkkö, and Marakas' (2016) paper in "Measurement: Interdisciplinary Research and Perspectives" (hereafter referred to as ARM2016) is an important and timely piece of scholarship, in that it provides strong analytic support to the growing theoretical literature that questions the underlying ideas behind causal and…

  14. Comments: Causal Interpretations of Mediation Effects

    ERIC Educational Resources Information Center

    Jo, Booil; Stuart, Elizabeth A.

    2012-01-01

    The authors thank Dr. Lindsay Page for providing a nice illustration of the use of the principal stratification framework to define causal effects, and a Bayesian model for effect estimation. They hope that her well-written article will help expose education researchers to these concepts and methods, and move the field of mediation analysis in…

  15. Escaping Myopia: Teaching Students about Historical Causality

    ERIC Educational Resources Information Center

    Waring, Scott M.

    2010-01-01

    There are so many aspects to teaching history that are vital to creating well-rounded historical thinkers, but one of the most fundamental and most overlooked elements is the idea of causality. Far too many students do not understand the idea of causation, that there are multiple reasons for why historical events occurred and transpired in the way…

  16. Causality and Teleology in High School Biology.

    ERIC Educational Resources Information Center

    Tamir, Pinchas

    1985-01-01

    Ability to distinguish between causal (cause-effect) and teleological (means-ends) explanations was measured in 1905 twelfth-grade biology students and found to be dependent on student knowledge. Although the inability to make these distinctions contributes to misconceptions in biology, appropriate instruction can easily remedy the problem. Sample…

  17. Inductive Reasoning about Causally Transmitted Properties

    ERIC Educational Resources Information Center

    Shafto, Patrick; Kemp, Charles; Bonawitz, Elizabeth Baraff; Coley, John D.; Tenenbaum, Joshua B.

    2008-01-01

    Different intuitive theories constrain and guide inferences in different contexts. Formalizing simple intuitive theories as probabilistic processes operating over structured representations, we present a new computational model of category-based induction about causally transmitted properties. A first experiment demonstrates undergraduates'…

  18. Marriage and Anomie: A Causal Argument

    ERIC Educational Resources Information Center

    Lee, Gary R.

    1974-01-01

    A sample of 394 married couples is employed to test the possibility of an association between marital satisfaction and personal (attitudinal) anomie. The hypothesis is supported. Conclusions are offered relevant to anomie theory, and to utilization of marital and family phenomena as independent variables in causal explanations of nonfamily events.…

  19. Causal Measurement Models: Can Criticism Stimulate Clarification?

    ERIC Educational Resources Information Center

    Markus, Keith A.

    2016-01-01

    In their 2016 work, Aguirre-Urreta et al. provided a contribution to the literature on causal measurement models that enhances clarity and stimulates further thinking. Aguirre-Urreta et al. presented a form of statistical identity involving mapping onto the portion of the parameter space involving the nomological net, relationships between the…

  20. THE CHILD'S CONCEPTION OF PHYSICAL CAUSALITY.

    ERIC Educational Resources Information Center

    PIAGET, JEAN

    THE CHILD'S CONCEPTION OF PHYSICAL CAUSALITY WAS INVESTIGATED. THREE METHODS OF INVESTIGATION WERE USED. THE FIRST METHOD WAS PURELY VERBAL, AND CONSISTED OF A SERIES OF QUESTIONS DIRECTED TO CHILDREN, REGARDING SOME NATURAL PHENOMENON. THE SECOND METHOD INVOLVED A HALF-VERBAL, HALF-PRACTICAL APPROACH, WHEREIN A SPECIFIC REFERENCE TO NATURAL…

  1. A Quantum Probability Model of Causal Reasoning

    PubMed Central

    Trueblood, Jennifer S.; Busemeyer, Jerome R.

    2012-01-01

    People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal inference problems produce unexpected results and describes a quantum inference model based on the axiomatic principles of quantum probability theory that can explain these effects. Two of the three phenomena arise from the comparison of predictive judgments (i.e., the conditional probability of an effect given a cause) with diagnostic judgments (i.e., the conditional probability of a cause given an effect). The third phenomenon is a new finding examining order effects in predictive causal judgments. The quantum inference model uses the notion of incompatibility among different causes to account for all three phenomena. Psychologically, the model assumes that individuals adopt different points of view when thinking about different causes. The model provides good fits to the data and offers a coherent account for all three causal reasoning effects thus proving to be a viable new candidate for modeling human judgment. PMID:22593747

  2. Constructing Causal Diagrams to Learn Deliberation

    ERIC Educational Resources Information Center

    Easterday, Matthew W.; Aleven, Vincent; Scheines, Richard; Carver, Sharon M.

    2009-01-01

    Policy problems like "What should we do about global warming?" are ill-defined in large part because we do not agree on a system to represent them the way we agree Algebra problems should be represented by equations. As a first step toward building a policy deliberation tutor, we investigated: (a) whether causal diagrams help students learn to…

  3. Strong curvature singularities and causal simplicity

    SciTech Connect

    Krolak, A. )

    1992-02-01

    Techniques of differential topology in Lorentzian manifolds developed by Geroch, Hawking, and Penrose are used to rule out a class of locally naked strong curvature singularities in strongly causal space-times. This result yields some support to the validity of Penrose's strong cosmic censorship hypothesis.

  4. Linear Response Laws and Causality in Electrodynamics

    ERIC Educational Resources Information Center

    Yuffa, Alex J.; Scales, John A.

    2012-01-01

    Linear response laws and causality (the effect cannot precede the cause) are of fundamental importance in physics. In the context of classical electrodynamics, students often have a difficult time grasping these concepts because the physics is obscured by the intermingling of the time and frequency domains. In this paper, we analyse the linear…

  5. Time and Order Effects on Causal Learning

    ERIC Educational Resources Information Center

    Alvarado, Angelica; Jara, Elvia; Vila, Javier; Rosas, Juan M.

    2006-01-01

    Five experiments were conducted to explore trial order and retention interval effects upon causal predictive judgments. Experiment 1 found that participants show a strong effect of trial order when a stimulus was sequentially paired with two different outcomes compared to a condition where both outcomes were presented intermixed. Experiment 2…

  6. Income inequality and health: a causal review.

    PubMed

    Pickett, Kate E; Wilkinson, Richard G

    2015-03-01

    There is a very large literature examining income inequality in relation to health. Early reviews came to different interpretations of the evidence, though a large majority of studies reported that health tended to be worse in more unequal societies. More recent studies, not included in those reviews, provide substantial new evidence. Our purpose in this paper is to assess whether or not wider income differences play a causal role leading to worse health. We conducted a literature review within an epidemiological causal framework and inferred the likelihood of a causal relationship between income inequality and health (including violence) by considering the evidence as a whole. The body of evidence strongly suggests that income inequality affects population health and wellbeing. The major causal criteria of temporality, biological plausibility, consistency and lack of alternative explanations are well supported. Of the small minority of studies which find no association, most can be explained by income inequality being measured at an inappropriate scale, the inclusion of mediating variables as controls, the use of subjective rather than objective measures of health, or follow up periods which are too short. The evidence that large income differences have damaging health and social consequences is strong and in most countries inequality is increasing. Narrowing the gap will improve the health and wellbeing of populations.

  7. [FROM STATISTICAL ASSOCIATIONS TO SCIENTIFIC CAUSALITY].

    PubMed

    Golan, Daniel; Linn, Shay

    2015-06-01

    The pathogenesis of most chronic diseases is complex and probably involves the interaction of multiple genetic and environmental risk factors. One way to learn about disease triggers is from statistically significant associations in epidemiological studies. However, associations do not necessarily prove causation. Associations can commonly result from bias, confounding and reverse causation. Several paradigms for causality inference have been developed. Henle-Koch postulates are mainly applied for infectious diseases. Austin Bradford Hill's criteria may serve as a practical tool to weigh the evidence regarding the probability that a single new risk factor for a given disease is indeed causal. These criteria are irrelevant for estimating the causal relationship between exposure to a risk factor and disease whenever biological causality has been previously established. Thus, it is highly probable that past exposure of an individual to definite carcinogens is related to his cancer, even without proving an association between this exposure and cancer in his group. For multifactorial diseases, Rothman's model of interacting sets of component causes can be applied.

  8. The metagenomic approach and causality in virology.

    PubMed

    Castrignano, Silvana Beres; Nagasse-Sugahara, Teresa Keico

    2015-01-01

    Nowadays, the metagenomic approach has been a very important tool in the discovery of new viruses in environmental and biological samples. Here we discuss how these discoveries may help to elucidate the etiology of diseases and the criteria necessary to establish a causal association between a virus and a disease.

  9. Introducing Mechanics by Tapping Core Causal Knowledge

    ERIC Educational Resources Information Center

    Klaassen, Kees; Westra, Axel; Emmett, Katrina; Eijkelhof, Harrie; Lijnse, Piet

    2008-01-01

    This article concerns an outline of an introductory mechanics course. It is based on the argument that various uses of the concept of force (e.g. from Kepler, Newton and everyday life) share an explanatory strategy based on core causal knowledge. The strategy consists of (a) the idea that a force causes a deviation from how an object would move of…

  10. Causality and the Levels of Selection.

    PubMed

    Krupp, D B

    2016-04-01

    When is it sensible to say that group selection has shaped organismal design? This question has prompted many replies but few credible solutions. New work that exposes the causal relationships between phenotypes and fitness may finally settle the matter - and a few other things besides.

  11. The metagenomic approach and causality in virology

    PubMed Central

    Castrignano, Silvana Beres; Nagasse-Sugahara, Teresa Keico

    2015-01-01

    Nowadays, the metagenomic approach has been a very important tool in the discovery of new viruses in environmental and biological samples. Here we discuss how these discoveries may help to elucidate the etiology of diseases and the criteria necessary to establish a causal association between a virus and a disease. PMID:25902566

  12. Causal and Teleological Explanations in Biology

    ERIC Educational Resources Information Center

    Yip, Cheng-Wai

    2009-01-01

    A causal explanation in biology focuses on the mechanism by which a biological process is brought about, whereas a teleological explanation considers the end result, in the context of the survival of the organism, as a reason for certain biological processes or structures. There is a tendency among students to offer a teleological explanation…

  13. Causal Poisson bracket via deformation quantization

    NASA Astrophysics Data System (ADS)

    Berra-Montiel, Jasel; Molgado, Alberto; Palacios-García, César D.

    2016-06-01

    Starting with the well-defined product of quantum fields at two spacetime points, we explore an associated Poisson structure for classical field theories within the deformation quantization formalism. We realize that the induced star-product is naturally related to the standard Moyal product through an appropriate causal Green’s functions connecting points in the space of classical solutions to the equations of motion. Our results resemble the Peierls-DeWitt bracket that has been analyzed in the multisymplectic context. Once our star-product is defined, we are able to apply the Wigner-Weyl map in order to introduce a generalized version of Wick’s theorem. Finally, we include some examples to explicitly test our method: the real scalar field, the bosonic string and a physically motivated nonlinear particle model. For the field theoretic models, we have encountered causal generalizations of the creation/annihilation relations, and also a causal generalization of the Virasoro algebra for the bosonic string. For the nonlinear particle case, we use the approximate solution in terms of the Green’s function, in order to construct a well-behaved causal bracket.

  14. Learning Practices of Femininity through Gendered Craft Education in Finland

    ERIC Educational Resources Information Center

    Kokko, Sirpa

    2009-01-01

    This paper discusses the processes and practices that link crafts and gender in the upbringing and education of girls. The paper is based on a study conducted among female primary school trainee teachers in Finland. The data are comprised of their experiences with crafts as schoolgirls. The methods of the study were memory work and writing of…

  15. Molecular Epidemiology of Tuberculosis in Finland, 2008-2011

    PubMed Central

    Smit, Pieter Willem; Haanperä, Marjo; Rantala, Pirre; Couvin, David; Lyytikäinen, Outi; Rastogi, Nalin; Ruutu, Petri; Soini, Hanna

    2013-01-01

    In industrialized countries the majority of tuberculosis (TB) cases are linked to immigration. In Finland, most cases are still Finnish born but the number of foreign born cases is steadily increasing. In this 4-year population based study, the TB situation in Finland was characterized by a genotypic analysis of Mycobacterium tuberculosis isolates. A total of 1048 M. tuberculosis isolates (representing 99.4% of all culture positive cases) were analyzed by spoligotyping and MIRU. Spoligotype lineages belonging to the Euro-American family were predominant among the Finnish isolates, particularly T (n=346, 33.0%) and Haarlem (n=237, 22.6%) strains. The lineage signature was unknown for 130 (12.4%) isolates. Out of the 17 multi-drug resistant TB strains, 10 (58.8%) belonged to the Beijing lineage. In total, 23 new SIT designations were given and 51 orphan strains were found, of which 58 patterns were unique to Finland. Phylogeographical TB mapping as compared to neighboring countries showed that the population structure in Finland most closely resembled that observed in Sweden. By combining spoligotyping and MIRU results, 98 clusters comprising 355 isolates (33.9%) were found. Only 10 clusters contained both Finnish and foreign born cases. In conclusion, a large proportion of the M. tuberculosis isolates were from Finnish born elderly patients. Moreover, many previously unidentified spoligotype profiles and isolates belonging to unknown lineages were encountered. PMID:24386443

  16. Multicultural Education in Finland: Renewed Intercultural Competencies to the Rescue?

    ERIC Educational Resources Information Center

    Dervin, Fred; Paatela-Nieminen, Martina; Kuoppala, Kaisa; Riitaoja, Anna-Leena

    2012-01-01

    This paper reviews discourses on multicultural education and the concept of intercultural competencies in the European and Nordic country of Finland. We focus on their present uses and perceptions by decision-makers, researchers, and also student teachers. Some prognosis for the future is made based on a short case study from art teacher education…

  17. Students' Attitudes towards Craft and Technology in Iceland and Finland

    ERIC Educational Resources Information Center

    Thorsteinsson, Gísli; Ólafsson, Brynjar; Autio, Ossi

    2012-01-01

    Craft education in both Finland and Iceland originated over 140 years ago and was influenced by the Scandinavian Sloyd pedagogy. Since then, the subject has moved away from craft and towards technology, with the aim being to increase students' technological abilities. In the beginning, the subject largely focused on the students copying artefacts,…

  18. Surveying Supported Employment in Finland: A Follow-up

    ERIC Educational Resources Information Center

    Saloviita, Timo; Pirttimaa, Raija

    2007-01-01

    The longitudinal status of supported employment in Finland was examined via a 2003 nationwide survey sent to job coaches involved in supporting workers with intellectual and other disabilities. Sustained supported employment, defined as "paid work in integrated settings with ongoing supports that contained at least two on-site visits per month at…

  19. University Selection in Finland: How the Decision Is Made

    ERIC Educational Resources Information Center

    Keskinen, Esko; Tiuraniemi, Juhani; Liimola, Anna

    2008-01-01

    Purpose: The purpose of this paper is to find out which factors contribute to the decisions of the students when they choose their place of study among the six psychology departments of the Universities in Finland. Design/methodology/approach: The study involved a survey questionnaire. Responses were received from 1,668 people. Findings: It was…

  20. Thematic Review on Adult Learning: Finland. Country Note. Revised.

    ERIC Educational Resources Information Center

    Organisation for Economic Cooperation and Development, Paris (France).

    This country note analyzes main issues concerning adult learning and policy responses in Finland. Section 2 describes the political, economic, and social context in which adult learning fits. Sections 3-6 follow these four themes impinging on adult participation in learning: inadequate incentives and motivations; complex pathways between learning…

  1. School Autonomy, Leadership and Student Achievement: Reflections from Finland

    ERIC Educational Resources Information Center

    Saarivirta, Toni; Kumpulainen, Kristiina

    2016-01-01

    Purpose: The purpose of this paper is to provide national information on school autonomy, leadership and student achievements in Finland. Design/methodology/approach: The paper is a literature review on Finnish studies focusing on school autonomy, leadership and student achievement. The studies have been reviewed on the basis of a content…

  2. 75 FR 30431 - Carboxymethylcellulose from Finland, Mexico, Netherlands, and Sweden

    Federal Register 2010, 2011, 2012, 2013, 2014

    2010-06-01

    ... 207), as most recently amended at 74 FR 2847 (January 16, 2009). \\1\\ No response to this request for... carboxymethylcellulose from Finland, Mexico, Netherlands, and Sweden (70 FR 39734). The Commission is conducting reviews... suspension, and carboxymethylcellulose that is cross-linked through heat treatment. (4) The Domestic...

  3. The use of videoconferencing for mental health services in Finland.

    PubMed

    Ohinmaa, Arto; Roine, Risto; Hailey, David; Kuusimäki, Marja-Leena; Winblad, Ilkka

    2008-01-01

    The utilization of telemental health (TMH) services in Finland was surveyed in 2006. In total, 135 health-care units provided responses. Eighty-four responses were received from primary care units (health-care centres and clinics) and eight from other clinics, in all hospital districts. The overall rate of TMH consultations was 4 per 100,000 population. The highest TMH consultation per population ratio, 22 per 100,000, was in northern Finland. Most of the sites used telepsychiatry services for less than 10% of clinical outpatient services. The sites with over 20% utilization of clinical TMH services from all psychiatric consultations were all rural health centres. Compared with Finland, the utilization rates of TMH were higher in Canada; that might be due to differences between the countries in the organization of mental health services in primary and specialized care. In Finland TMH consultations made up only a very small proportion of all mental health services. The use of TMH was particularly common in remote areas; however, there were many rural centres that did not utilize clinical TMH. TMH was widely utilized for continuing and medical education.

  4. Needs for Rural Research in the Northern Finland Context

    ERIC Educational Resources Information Center

    Muilu, Toivo

    2010-01-01

    The aim of this paper is to discuss the needs and demands which rural research faces at the interface between research and development. The case study area is northern Finland, which constitutes the most remote and sparsely populated areas of the European Union. This paper is based on the tradition of rural research since the 1980s in connection…

  5. Motor vehicle exhaust emissions and control in Finland

    SciTech Connect

    Laurikko, J.

    1989-01-01

    This paper outlines the status and trends of atmospheric pollution in Finland caused by motor vehicles and evaluates the effect of the current regulatory policy. Details of new emission regulations for passenger cars and heavy duty vehicles are given. Research activities and items of particular concern like the effect of low ambient temperature on emissions are also discussed.

  6. My Lifelong Learning Realm: An Autoethnography Experiential Learning in Finland

    ERIC Educational Resources Information Center

    Rajbhandari, Mani Man Singh

    2011-01-01

    My journey to write autoethnography report started with inclination to learn cultural and social phenomena in Finland. This was my realm of learning through experiential learning. The ontological philosophy was perceived through objectivistic and subjectivistic approaches. The lifelong experiential learning realm was a benchmark for me to perceive…

  7. Teacher Education in Italy, Germany, England, Sweden and Finland

    ERIC Educational Resources Information Center

    Ostinelli, Giorgio

    2009-01-01

    This article presents a brief analysis of teacher education in five European countries: Italy, Germany, England, Sweden and Finland. In the post-industrial world, the sense of teaching has profoundly changed, influenced by a rapidly evolving socio-economic context. The responses given by each country are different, but two tendencies emerge: on…

  8. Coxsackievirus A6 and hand, foot, and mouth disease, Finland.

    PubMed

    Osterback, Riikka; Vuorinen, Tytti; Linna, Mervi; Susi, Petri; Hyypiä, Timo; Waris, Matti

    2009-09-01

    During fall 2008, an outbreak of hand, foot, and mouth disease (HFMD) with onychomadesis (nail shedding) as a common feature occurred in Finland. We identified an unusual enterovirus type, coxsackievirus A6 (CVA6), as the causative agent. CVA6 infections may be emerging as a new and major cause of epidemic HFMD.

  9. Serodiagnosis of primary infections with human parvovirus 4, Finland.

    PubMed

    Lahtinen, Anne; Kivelä, Pia; Hedman, Lea; Kumar, Arun; Kantele, Anu; Lappalainen, Maija; Liitsola, Kirsi; Ristola, Matti; Delwart, Eric; Sharp, Colin; Simmonds, Peter; Söderlund-Venermo, Maria; Hedman, Klaus

    2011-01-01

    To determine the prevalence of parvovirus 4 infection and its clinical and sociodemographic correlations in Finland, we used virus-like particle-based serodiagnostic procedures (immunoglobulin [Ig] G, IgM, and IgG avidity) and PCR. We found 2 persons with parvovirus 4 primary infection who had mild or asymptomatic clinical features among hepatitis C virus-infected injection drug users.

  10. Thematic Review on Adult Learning: Finland. Background Report.

    ERIC Educational Resources Information Center

    Organisation for Economic Cooperation and Development, Paris (France).

    In international comparisons, participation in adult learning in Finland is high. Work or career development is the main reason for participation. Persons starting with greater educational attainment participate in adult learning opportunities more. Roots of adult education and training (AET) lie in liberal education; those of occupational AET in…

  11. Molecular epidemiology of tuberculosis in Finland, 2008-2011.

    PubMed

    Smit, Pieter Willem; Haanperä, Marjo; Rantala, Pirre; Couvin, David; Lyytikäinen, Outi; Rastogi, Nalin; Ruutu, Petri; Soini, Hanna

    2013-01-01

    In industrialized countries the majority of tuberculosis (TB) cases are linked to immigration. In Finland, most cases are still Finnish born but the number of foreign born cases is steadily increasing. In this 4-year population based study, the TB situation in Finland was characterized by a genotypic analysis of Mycobacterium tuberculosis isolates. A total of 1048 M. tuberculosis isolates (representing 99.4% of all culture positive cases) were analyzed by spoligotyping and MIRU. Spoligotype lineages belonging to the Euro-American family were predominant among the Finnish isolates, particularly T (n=346, 33.0%) and Haarlem (n=237, 22.6%) strains. The lineage signature was unknown for 130 (12.4%) isolates. Out of the 17 multi-drug resistant TB strains, 10 (58.8%) belonged to the Beijing lineage. In total, 23 new SIT designations were given and 51 orphan strains were found, of which 58 patterns were unique to Finland. Phylogeographical TB mapping as compared to neighboring countries showed that the population structure in Finland most closely resembled that observed in Sweden. By combining spoligotyping and MIRU results, 98 clusters comprising 355 isolates (33.9%) were found. Only 10 clusters contained both Finnish and foreign born cases. In conclusion, a large proportion of the M. tuberculosis isolates were from Finnish born elderly patients. Moreover, many previously unidentified spoligotype profiles and isolates belonging to unknown lineages were encountered.

  12. Swedish Immersion in the Early Years in Finland

    ERIC Educational Resources Information Center

    Björklund, Siv; Mård-Miettinen, Karita; Savijärvi, Marjo

    2014-01-01

    Immersion education in Finland is a one-way (monolingual) early total Swedish programme for Finnish-speaking students. This immersion provision is offered at kindergarten level (ages 3-5), at preschool (age 6) and at primary levels (grades 1-9). Here, a brief synthesis of Finnish research studies on the early years in Swedish immersion is first…

  13. Children as Consumers of Historical Culture in Finland

    ERIC Educational Resources Information Center

    Rantala, Jukka

    2011-01-01

    The article examines the reception of history by 7-10-year-old children in Finland and the role of historical culture in the formation of children's conceptions of the past. It scrutinizes how history is used to build individual and collective identities and bring significance to the past in children's everyday lives. Interviews with 174 pupils in…

  14. Evaluating the Quality of the Child Care in Finland

    ERIC Educational Resources Information Center

    Hujala, Eeva; Fonsen, Elina; Elo, Janniina

    2012-01-01

    In this study we examine parents' and teachers' perceptions of the early childhood education and care (ECEC) quality in Finland. The study is based on the paradigm of inclusionary quality and the assessment is based on the quality evaluation model. The parents and teachers assess the quality to be good. The strength of the quality was the effect…

  15. Segregation, Integration, Inclusion--The Ideology and Reality in Finland

    ERIC Educational Resources Information Center

    Kivirauma, Joel; Klemela, Kirsi; Rinne, Risto

    2006-01-01

    In this paper, we try to examine the classical sociological points of special education, especially the organizational form of special education, social background of students and the minority status of students. The material of the study was collected mostly during 2003 from one large city in Finland. This city has more than a 100-year-long…

  16. Children's Early Numeracy in Finland and Iran

    ERIC Educational Resources Information Center

    Aunio, Pirjo; Korhonen, Johan; Bashash, Laaya; Khoshbakht, Fariba

    2014-01-01

    This research investigates similarities and differences in young children's early numeracy skills related to age, nationality and gender. The participants were five- to seven-year-old children from Finland and Iran. Early numeracy was investigated by using tasks measuring number-related relational skills (e.g. comparison, one-to-one…

  17. Multiple Outbreaks of Yersinia pseudotuberculosis Infections in Finland

    PubMed Central

    Jalava, Katri; Hallanvuo, S.; Nakari, U.-M.; Ruutu, P.; Kela, E.; Heinäsmäki, T.; Siitonen, A.; Nuorti, J. P.

    2004-01-01

    During 2001, 89 culture-confirmed cases of Yersinia pseudotuberculosis were reported in Finland; 55 (62%) were serotype O:1, and 34 (38%) were serotype O:3. Four major pulsed-field gel electrophoresis profiles were identified. A case-control study of 25 case patients and 71 healthy controls identified eating outside the home as a risk factor for infection. PMID:15184472

  18. Teaching Linear Equations: Case Studies from Finland, Flanders and Hungary

    ERIC Educational Resources Information Center

    Andrews, Paul; Sayers, Judy

    2012-01-01

    In this paper we compare how three teachers, one from each of Finland, Flanders and Hungary, introduce linear equations to grade 8 students. Five successive lessons were videotaped and analysed qualitatively to determine how teachers, each of whom was defined against local criteria as effective, addressed various literature-derived…

  19. Infection with Possible Novel Parapoxvirus in Horse, Finland, 2013.

    PubMed

    Airas, Niina; Hautaniemi, Maria; Syrjä, Pernilla; Knuuttila, Anna; Putkuri, Niina; Coulter, Lesley; McInnes, Colin J; Vapalahti, Olli; Huovilainen, Anita; Kinnunen, Paula M

    2016-07-01

    A horse in Finland exhibited generalized granulomatous inflammation and severe proliferative dermatitis. After euthanization, we detected poxvirus DNA from a skin lesion sample. The virus sequence grouped with parapoxviruses, closely resembling a novel poxvirus detected in humans in the United States after horse contact. Our findings indicate horses may be a reservoir for zoonotic parapoxvirus. PMID:27315302

  20. Infection with Possible Novel Parapoxvirus in Horse, Finland, 2013

    PubMed Central

    Hautaniemi, Maria; Syrjä, Pernilla; Knuuttila, Anna; Putkuri, Niina; Coulter, Lesley; McInnes, Colin J.; Vapalahti, Olli; Huovilainen, Anita; Kinnunen, Paula M.

    2016-01-01

    A horse in Finland exhibited generalized granulomatous inflammation and severe proliferative dermatitis. After euthanization, we detected poxvirus DNA from a skin lesion sample. The virus sequence grouped with parapoxviruses, closely resembling a novel poxvirus detected in humans in the United States after horse contact. Our findings indicate horses may be a reservoir for zoonotic parapoxvirus. PMID:27315302

  1. Career Burnout and Its Relationship to Couple Burnout in Finland.

    ERIC Educational Resources Information Center

    Laes, Timo; Laes, Tuula

    This study is part of cross cultural research on the relationship between career and couple burnout in six countries (England, Finland, Israel, Portugal, Spain, and the USA.) This pilot study presents first results of Finnish data. Female elementary school teachers (N=56) and male students (N=70) in academic professional education completed the…

  2. Teaching Popular Music in Finland: What's Up, What's Ahead?

    ERIC Educational Resources Information Center

    Vakeva, Lauri

    2006-01-01

    This article describes the history and current situation of popular music pedagogy in Finland. While popular music is widely accepted in the curriculum, there are differences in its application in the comprehensive schools and music institutions. Popular styles were first introduced into Finnish music education by secondary school music teachers;…

  3. Checklist of the family Syrphidae (Diptera) of Finland

    PubMed Central

    Haarto, Antti; Kerppola, Sakari

    2014-01-01

    Abstract A checklist of the Syrphidae (Diptera) recorded from Finland. Three species of Syrphidae, Platycheirus modestus Ide, 1926, Cheilosia barovskii (Stackelberg, 1930) and Mallota tricolor Loew, 1871, are published as new to the Finnish fauna. Platycheirus modestus is also new to the Palaearctic. PMID:25337020

  4. Acid sulfate soils are an environmental hazard in Finland

    NASA Astrophysics Data System (ADS)

    Pihlaja, Jouni

    2016-04-01

    Acid sulfate soils (ASS) create significant threats to the environment on coastal regions of the Baltic Sea in Finland. The sediments were deposited during the ancient Litorina Sea phase of the Baltic Sea about 7500-4500 years ago. Finland has larger spatial extent of the ASS than any other European country. Mostly based on anthropogenic reasons (cultivation, trenching etc.) ASS deposits are currently being exposed to oxygen which leads to chemical reaction creating sulfuric acid. The acidic waters then dissolve metals form the soil. Acidic surface run off including the metals are then leached into the water bodies weakening the water quality and killing fish or vegetation. In constructed areas acidic waters may corrode building materials. Geological Survey of Finland (GTK) is mapping ASS deposits in Finland. The goal is to map a total of 5 million hectares of the potentially ASS affected region. It has been estimated that the problematic Litorina Sea deposits, which are situated 0-100 m above the recent Baltic Sea shoreline, cover 500 000 hectares area. There are several phases in mapping. The work begins at the office with gathering the existing data, interpreting airborne geophysical data and compiling a field working plan. In the field, quality of the soil is studied and in uncertain cases samples are taken to laboratory analyses. Also electrical conductivity and pH of soil and water are measured in the field. Laboratory methods include multielemental determinations with ICP-OES, analyses of grain size and humus content (LOI), and incubation. So far, approximately 60 % of the potential ASS affected regions in Finland are mapped. Over 15 000 sites have been studied in the field and 4000 laboratory analyses are done. The spatial database presented in the scale of 1: 250 000 can be viewed at the GTK's web pages (http://gtkdata.gtk.fi/hasu/index.html).

  5. Interpretational Confounding or Confounded Interpretations of Causal Indicators?

    ERIC Educational Resources Information Center

    Bainter, Sierra A.; Bollen, Kenneth A.

    2014-01-01

    In measurement theory, causal indicators are controversial and little understood. Methodological disagreement concerning causal indicators has centered on the question of whether causal indicators are inherently sensitive to interpretational confounding, which occurs when the empirical meaning of a latent construct departs from the meaning…

  6. Preschool Children Learn about Causal Structure from Conditional Interventions

    ERIC Educational Resources Information Center

    Schulz, Laura E.; Gopnik, Alison; Glymour, Clark

    2007-01-01

    The conditional intervention principle is a formal principle that relates patterns of interventions and outcomes to causal structure. It is a central assumption of experimental design and the causal Bayes net formalism. Two studies suggest that preschoolers can use the conditional intervention principle to distinguish causal chains, common cause…

  7. CAUSAL ANALYSIS AND PROBABILITY DATA: EXAMPLES FOR IMPAIRED AQUATIC CONDITION

    EPA Science Inventory

    Causal analysis is plausible reasoning applied to diagnosing observed effect(s), for example, diagnosing

    cause of biological impairment in a stream. Sir Bradford Hill basically defined the application of causal

    analysis when he enumerated the elements of causality f...

  8. The Specification of Causal Models with Tetrad IV: A Review

    ERIC Educational Resources Information Center

    Landsheer, J. A.

    2010-01-01

    Tetrad IV is a program designed for the specification of causal models. It is specifically designed to search for causal relations, but also offers the possibility to estimate the parameters of a structural equation model. It offers a remarkable graphical user interface, which facilitates building, evaluating, and searching for causal models. The…

  9. How to Be Causal: Time, Spacetime and Spectra

    ERIC Educational Resources Information Center

    Kinsler, Paul

    2011-01-01

    I explain a simple definition of causality in widespread use, and indicate how it links to the Kramers-Kronig relations. The specification of causality in terms of temporal differential equations then shows us the way to write down dynamical models so that their causal nature "in the sense used here" should be obvious to all. To extend existing…

  10. Rationales in Children's Causal Learning from Others' Actions

    ERIC Educational Resources Information Center

    Sobel, David M.; Sommerville, Jessica A.

    2009-01-01

    Shown commensurate actions and information by an adult, preschoolers' causal learning was influenced by the pedagogical context in which these actions occurred. Four-year-olds who were provided with a reason for an experimenter's action relevant to learning causal structure showed more accurate causal learning than children exposed to the same…

  11. A Quantitative Causal Model Theory of Conditional Reasoning

    ERIC Educational Resources Information Center

    Fernbach, Philip M.; Erb, Christopher D.

    2013-01-01

    The authors propose and test a causal model theory of reasoning about conditional arguments with causal content. According to the theory, the acceptability of modus ponens (MP) and affirming the consequent (AC) reflect the conditional likelihood of causes and effects based on a probabilistic causal model of the scenario being judged. Acceptability…

  12. Mind and Meaning: Piaget and Vygotsky on Causal Explanation.

    ERIC Educational Resources Information Center

    Beilin, Harry

    1996-01-01

    Piaget's theory has been characterized as descriptive and not explanatory, not qualifying as causal explanation. Piaget was consistent in showing how his theory was both explanatory and causal. Vygotsky also endorsed causal-genetic explanation but, on the basis of knowledge of only Piaget's earliest works, he claimed that Piaget's theory was not…

  13. A Bayesian Theory of Sequential Causal Learning and Abstract Transfer

    ERIC Educational Resources Information Center

    Lu, Hongjing; Rojas, Randall R.; Beckers, Tom; Yuille, Alan L.

    2016-01-01

    Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about…

  14. Perceived Causalities of Physical Events Are Influenced by Social Cues

    ERIC Educational Resources Information Center

    Zhou, Jifan; Huang, Xiang; Jin, Xinyi; Liang, Junying; Shui, Rende; Shen, Mowei

    2012-01-01

    In simple mechanical events, we can directly perceive causal interactions of the physical objects. Physical cues (especially spatiotemporal features of the display) are found to associate with causal perception. Here, we demonstrate that cues of a completely different domain--"social cues"--also impact the causal perception of "physical" events:…

  15. Assessing Understanding of Complex Causal Networks Using an Interactive Game

    ERIC Educational Resources Information Center

    Ross, Joel

    2013-01-01

    Assessing people's understanding of the causal relationships found in large-scale complex systems may be necessary for addressing many critical social concerns, such as environmental sustainability. Existing methods for assessing systems thinking and causal understanding frequently use the technique of cognitive causal mapping. However, the…

  16. A Self-Agency Bias in Preschoolers' Causal Inferences

    ERIC Educational Resources Information Center

    Kushnir, Tamar; Wellman, Henry M.; Gelman, Susan A.

    2009-01-01

    Preschoolers' causal learning from intentional actions--causal interventions--is subject to a self-agency bias. The authors propose that this bias is evidence-based, in other words, that it is responsive to causal uncertainty. In the current studies, two causes (one child controlled, one experimenter controlled) were associated with one or two…

  17. Omission of Causal Indicators: Consequences and Implications for Measurement

    ERIC Educational Resources Information Center

    Aguirre-Urreta, Miguel I.; Rönkkö, Mikko; Marakas, George M.

    2016-01-01

    One of the central assumptions of the causal-indicator literature is that all causal indicators must be included in the research model and that the exclusion of one or more relevant causal indicators would have severe negative consequences by altering the meaning of the latent variable. In this research we show that the omission of a relevant…

  18. Toward an Intersectional Understanding of Process Causality and Social Context

    ERIC Educational Resources Information Center

    Anderson, Gary L.; Scott, Janelle

    2012-01-01

    Maxwell and Donmoyer both argue in this issue of "Qualitative Inquiry" that narrow definitions of causality in educational research tend to disqualify qualitative research from influence (and funding) among policy makers. They propose a process view of causality that would allow qualitative researchers to make causal claims more grounded in the…

  19. Recursive retrospective revaluation of causal judgments.

    PubMed

    Macho, Siegfried; Burkart, Judith

    2002-11-01

    Recursive causal evaluation is an iterative process in which the evaluation of a target cause, T, is based on the outcome of the evaluation of another cause, C, the evaluation of which itself depends on the evaluation of a 3rd cause, D. Retrospective revaluation consists of backward processing of information as indicated by the fact that the evaluation of T is influenced by subsequent information that is not concerned with T directly. Two experiments demonstrate recursive retrospective revaluation with contingency information presented in list format as well as with trial-by-trial acquisition. Existing associative models are unable to predict the results. The model of recursive causal disambiguation that conceptualizes the revaluation as a recursive process of disambiguation predicts the pattern of results correctly.

  20. Causal Network Inference Via Group Sparse Regularization.

    PubMed

    Bolstad, Andrew; Van Veen, Barry D; Nowak, Robert

    2011-06-11

    This paper addresses the problem of inferring sparse causal networks modeled by multivariate autoregressive (MAR) processes. Conditions are derived under which the Group Lasso (gLasso) procedure consistently estimates sparse network structure. The key condition involves a "false connection score" ψ. In particular, we show that consistent recovery is possible even when the number of observations of the network is far less than the number of parameters describing the network, provided that ψ < 1. The false connection score is also demonstrated to be a useful metric of recovery in nonasymptotic regimes. The conditions suggest a modified gLasso procedure which tends to improve the false connection score and reduce the chances of reversing the direction of causal influence. Computational experiments and a real network based electrocorticogram (ECoG) simulation study demonstrate the effectiveness of the approach.

  1. Blocking and unblocking in human causal learning.

    PubMed

    Le Pelley, M E; Oakeshott, S M; McLaren, I P L

    2005-01-01

    Three experiments sought to develop the suggestion that, under some circumstances, common associative learning mechanisms might underlie animal conditioning and human causal learning, by demonstrating, in humans, an effect analogous to the unblocking by reinforcer omission observed in animal conditioning. Experiment 1 found no such effect. Experiment 2, designed to prevent inhibitory influences that might have masked excitatory unblocking in Experiment 1, demonstrated unblocking, indicating common human-animal associative learning mechanisms in which the associability of a stimulus varies as a function of its predictive history. Experiment 3, using a similar design but with a procedure promoting application of rational inference processes, failed to detect the same unblocking effect, indicating that associative and cognitive mechanisms may influence human causal learning.

  2. Sentencing goals, causal attributions, ideology, and personality.

    PubMed

    Carroll, J S; Perkowitz, W T; Lurigio, A J; Weaver, F M

    1987-01-01

    Disparity in sentencing of criminals has been related to a variety of individual difference variables. We propose a framework establishing resonances or coherent patterns among sentencing goals, causal attributions, ideology, and personality. Two studies are described, one with law and criminology students, the other with probation officers. Relations among the different types of variables reveal two resonances among both students and officers. One comprises various conservative and moralistic elements: a tough, punitive stance toward crime; belief in individual causality for crime; high scores on authoritarianism, dogmatism, and internal locus of control; lower moral stage; and political conservatism. The second comprises various liberal elements: rehabilitation, belief in economic and other external determinants of crime, higher moral stage, and belief in the powers and responsibilities of government to correct social problems. Implications of these results are discussed for individual differences in sentencing, attribution theory, and attempts to reduce disparity.

  3. Causal Network Inference Via Group Sparse Regularization

    PubMed Central

    Bolstad, Andrew; Van Veen, Barry D.; Nowak, Robert

    2011-01-01

    This paper addresses the problem of inferring sparse causal networks modeled by multivariate autoregressive (MAR) processes. Conditions are derived under which the Group Lasso (gLasso) procedure consistently estimates sparse network structure. The key condition involves a “false connection score” ψ. In particular, we show that consistent recovery is possible even when the number of observations of the network is far less than the number of parameters describing the network, provided that ψ < 1. The false connection score is also demonstrated to be a useful metric of recovery in nonasymptotic regimes. The conditions suggest a modified gLasso procedure which tends to improve the false connection score and reduce the chances of reversing the direction of causal influence. Computational experiments and a real network based electrocorticogram (ECoG) simulation study demonstrate the effectiveness of the approach. PMID:21918591

  4. Causal Model of a Health Services System

    PubMed Central

    Anderson, James G.

    1972-01-01

    Path analysis is used to construct a causal model of the health services system serving the state of New Mexico. The model includes a network specifying the causal relationships among a set of social, demographic, and economic variables hypothesized to be related to the health status of the population; a set of mathematical equations that permit prediction of the effects of changes in the values of any one variable on all other variables in the model; and estimates of path coefficients based on U.S. Census data and vital statistics. The model is used to predict both direct and indirect effects on health status of changes in population structure resulting from natural causes or from the intervention of health programs. PMID:5025955

  5. Causality Is Inconsistent With Quantum Field Theory

    SciTech Connect

    Wolf, Fred Alan

    2011-11-29

    Causality in quantum field theory means the vanishing of commutators for spacelike separated fields (VCSSF). I will show that VCSSF is not tenable. For VCSSF to be tenable, and therefore, to have both retarded and advanced propagators vanish in the elsewhere, a superposition of negative energy antiparticle and positive energy particle propagators, traveling forward in time, and a superposition of negative energy particle and positive energy antiparticle propagators, traveling backward in time, are required. Hence VCSSF predicts non-vanishing probabilities for both negative energy particles in the forward-through-time direction and positive energy antiparticles in the backwards-through-time direction. Therefore, since VCSSF is unrealizable in a stable universe, tachyonic propagation must occur in denial of causality.

  6. The Causal Effects of Father Absence

    PubMed Central

    McLanahan, Sara; Tach, Laura; Schneider, Daniel

    2014-01-01

    The literature on father absence is frequently criticized for its use of cross-sectional data and methods that fail to take account of possible omitted variable bias and reverse causality. We review studies that have responded to this critique by employing a variety of innovative research designs to identify the causal effect of father absence, including studies using lagged dependent variable models, growth curve models, individual fixed effects models, sibling fixed effects models, natural experiments, and propensity score matching models. Our assessment is that studies using more rigorous designs continue to find negative effects of father absence on offspring well-being, although the magnitude of these effects is smaller than what is found using traditional cross-sectional designs. The evidence is strongest and most consistent for outcomes such as high school graduation, children’s social-emotional adjustment, and adult mental health. PMID:24489431

  7. Loop anomalies in the causal approach

    NASA Astrophysics Data System (ADS)

    Grigore, Dan-Radu

    2015-01-01

    We consider gauge models in the causal approach and study one-loop contributions to the chronological products and the anomalies they produce. We prove that in order greater than 4 there are no one-loop anomalies. Next we analyze one-loop anomalies in the second- and third-order of the perturbation theory. We prove that the even parity contributions (with respect to parity) do not produce anomalies; for the odd parity contributions we reobtain the well-known result.

  8. Symmetry and causality properties of physical fields

    PubMed Central

    Jakobsen, H. P.; Ørsted, B.; Segal, I. E.; Speh, B.; Vergne, M.

    1978-01-01

    Representations of groups of causality-preserving transformations on locally Minkowskian space-times, by actions on classes of wave functions of designated transformation properties, are analyzed, in extension of the conventional theoretical treatment of free relativistic particles. In particular, the constraints of positivity of the energy and finiteness of propagation velocity are developed, and the concept of mass is explored, within the indicated framework. PMID:16592512

  9. Causal tapestries for psychology and physics.

    PubMed

    Sulis, William H

    2012-04-01

    Archetypal dynamics is a formal approach to the modeling of information flow in complex systems used to study emergence. It is grounded in the Fundamental Triad of realisation (system), interpretation (archetype) and representation (formal model). Tapestries play a fundamental role in the framework of archetypal dynamics as a formal representational system. They represent information flow by means of multi layered, recursive, interlinked graphical structures that express both geometry (form or sign) and logic (semantics). This paper presents a detailed mathematical description of a specific tapestry model, the causal tapestry, selected for use in describing behaving systems such as appear in psychology and physics from the standpoint of Process Theory. Causal tapestries express an explicit Lorentz invariant transient now generated by means of a reality game. Observables are represented by tapestry informons while subjective or hidden components (for example intellectual and emotional processes) are incorporated into the reality game that determines the tapestry dynamics. As a specific example, we formulate a random graphical dynamical system using causal tapestries.

  10. Dynamic causal modeling with genetic algorithms.

    PubMed

    Pyka, M; Heider, D; Hauke, S; Kircher, T; Jansen, A

    2011-01-15

    In the last years, dynamic causal modeling has gained increased popularity in the neuroimaging community as an approach for the estimation of effective connectivity from functional magnetic resonance imaging (fMRI) data. The algorithm calls for an a priori defined model, whose parameter estimates are subsequently computed upon the given data. As the number of possible models increases exponentially with additional areas, it rapidly becomes inefficient to compute parameter estimates for all models in order to reveal the family of models with the highest posterior probability. In the present study, we developed a genetic algorithm for dynamic causal models and investigated whether this evolutionary approach can accelerate the model search. In this context, the configuration of the intrinsic, extrinsic and bilinear connection matrices represents the genetic code and Bayesian model selection serves as a fitness function. Using crossover and mutation, populations of models are created and compared with each other. The most probable ones survive the current generation and serve as a source for the next generation of models. Tests with artificially created data sets show that the genetic algorithm approximates the most plausible models faster than a random-driven brute-force search. The fitness landscape revealed by the genetic algorithm indicates that dynamic causal modeling has excellent properties for evolution-driven optimization techniques.

  11. Bell's theorem on arbitrary causal structures

    NASA Astrophysics Data System (ADS)

    Fritz, Tobias

    2014-03-01

    Bell's theorem is a gedankenexperiment with an underlying causal structure in the form of the letter ``M.'' I will describe how such a Bell scenario is a special case of a vastly larger class of scenarios, in which the causal structure of the ``M'' is replaced by an arbitrary directed acyclic graph (or, equivalently, by a causal set). In this formalism, the apparent difference between the notions of ``choice of setting,'' ``source,'' and ``measurement'' disappears completely and all of these become special cases of the general notion of ``event.'' I will explain how this relieves Bell's theorem of the philosophical baggage associated with free will and also present several mathematical results about these more general scenarios obtained by various people. This formalism is expected to have applications in many other areas of science: it is relevant whenever a system is probed at certain points in space and time, and at each of these points there may be hidden information not observed by the probes.

  12. [Causality in cardiology: concepts in evolution].

    PubMed

    Méndez, Gustavo F

    2005-01-01

    This paper describes several concepts about causality from Empedocles, Aristoteles and Galeno, to Koch and Hill and the evolution of these concepts related to cardiovascular diseases. Also defines cause and risk, and the philosophical theories about scientific knowledge: inductive versus refutation analysis. On these basis, the study of cardiovascular disease's causality, especially coronary heart disease, allows us the identification of several risk factors involved in its development. However, even with the presently coronary heart disease risk charts (from Framingham and European studies) the higher probability for the development of a cardiovascular ischemic event is around 40%, establishing an important degree of uncertainty. With the improvement in molecular biology techniques, genetics have attempted to analyse several genetic polymorphisms in search of the origin of coronary heart disease. Unfortunately, less than 10% of these polymorphisms have had a positive correlation with coronary heart disease being of minor risk that those obtained for having the diagnosis of type 2 diabetes mellitus or hypercholesterolemia. On these basis, the requirement of new population research projects in which clinical and genetic risk factors are to be studied for the appropriate understanding of the causality process of cardiovascular diseases must be a worldwide priority.

  13. Causal tapestries for psychology and physics.

    PubMed

    Sulis, William H

    2012-04-01

    Archetypal dynamics is a formal approach to the modeling of information flow in complex systems used to study emergence. It is grounded in the Fundamental Triad of realisation (system), interpretation (archetype) and representation (formal model). Tapestries play a fundamental role in the framework of archetypal dynamics as a formal representational system. They represent information flow by means of multi layered, recursive, interlinked graphical structures that express both geometry (form or sign) and logic (semantics). This paper presents a detailed mathematical description of a specific tapestry model, the causal tapestry, selected for use in describing behaving systems such as appear in psychology and physics from the standpoint of Process Theory. Causal tapestries express an explicit Lorentz invariant transient now generated by means of a reality game. Observables are represented by tapestry informons while subjective or hidden components (for example intellectual and emotional processes) are incorporated into the reality game that determines the tapestry dynamics. As a specific example, we formulate a random graphical dynamical system using causal tapestries. PMID:22452929

  14. Causality, initial conditions, and inflationary magnetogenesis

    NASA Astrophysics Data System (ADS)

    Tsagas, Christos G.

    2016-05-01

    The post-inflationary evolution of inflation-produced magnetic fields, conventional or not, can change dramatically when two fundamental issues are accounted for. The first is causality, which demands that local physical processes can never affect superhorizon perturbations. The second is the nature of the transition from inflation to reheating and then to the radiation era, which determine the initial conditions at the start of these epochs. Causality implies that inflationary magnetic fields do not freeze into the matter until they have re-entered the causal horizon. The nature of the cosmological transitions and the associated initial conditions, on the other hand, determine the large-scale magnetic evolution after inflation. Put together, the two can slow down the adiabatic magnetic decay on superhorizon scales throughout the Universe's post-inflationary evolution and thus lead to considerably stronger residual magnetic fields. This is "good news" for both the conventional and the nonconventional scenarios of cosmic magnetogenesis. Mechanisms operating outside standard electromagnetism, in particular, do not need to enhance their fields too much during inflation in order to produce seeds that can feed the galactic dynamo today. In fact, even conventionally produced inflationary magnetic fields might be able to sustain the dynamo.

  15. Geometry of the infalling causal patch

    NASA Astrophysics Data System (ADS)

    Freivogel, Ben; Jefferson, Robert A.; Kabir, Laurens; Yang, I.-Sheng

    2015-02-01

    The firewall paradox states that an observer falling into an old black hole must see a violation of unitarity, locality, or the equivalence principle. Motivated by this remarkable conflict, we analyze the causal structure of black hole spacetimes in order to determine whether all the necessary ingredients for the paradox fit within a single observer's causal patch. We particularly focus on the question of whether the interior partner modes of the outgoing Hawking quanta can, in principle, be measured by an infalling observer. Since the relevant modes are spread over the entire sphere, we answer a simple geometrical question: can any observer see an entire sphere behind the horizon? We find that for all static black holes in 3 +1 and higher dimensions, with any value of the cosmological constant, no single observer can see both the early Hawking radiation and the interior modes with low angular momentum. We present a detailed description of the causal patch geometry of the Schwarzschild black hole in 3 +1 dimensions, where an infalling observer comes closest to being able to measure the relevant modes.

  16. A causal dispositional account of fitness.

    PubMed

    Triviño, Vanessa; Nuño de la Rosa, Laura

    2016-09-01

    The notion of fitness is usually equated to reproductive success. However, this actualist approach presents some difficulties, mainly the explanatory circularity problem, which have lead philosophers of biology to offer alternative definitions in which fitness and reproductive success are distinguished. In this paper, we argue  that none of these alternatives is satisfactory and, inspired by Mumford and Anjum's dispositional theory of causation, we offer a definition of fitness as a causal dispositional property. We argue that, under this framework, the distinctiveness that biologists usually attribute to fitness-namely, the fact that fitness is something different from both the physical traits of an organism and the number of offspring it leaves-can be explained, and the main problems associated with the concept of fitness can be solved. Firstly, we introduce Mumford and Anjum's dispositional theory of causation and present our definition of fitness as a causal disposition. We explain in detail each of the elements involved in our definition, namely: the relationship between fitness and the functional dispositions that compose it, the emergent character of fitness, and the context-sensitivity of fitness. Finally, we explain how fitness and realized fitness, as well as expected and realized fitness are distinguished in our approach to fitness as a causal disposition.

  17. Curvature constraints from the causal entropic principle

    SciTech Connect

    Bozek, Brandon; Albrecht, Andreas; Phillips, Daniel

    2009-07-15

    Current cosmological observations indicate a preference for a cosmological constant that is drastically smaller than what can be explained by conventional particle physics. The causal entropic principle (Bousso et al.) provides an alternative approach to anthropic attempts to predict our observed value of the cosmological constant by calculating the entropy created within a causal diamond. We have extended this work to use the causal entropic principle to predict the preferred curvature within the 'multiverse'. We have found that values larger than {rho}{sub k}=40{rho}{sub m} are disfavored by more than 99.99% peak value at {rho}{sub {lambda}}=7.9x10{sup -123} and {rho}{sub k}=4.3{rho}{sub m} for open universes. For universes that allow only positive curvature or both positive and negative curvature, we find a correlation between curvature and dark energy that leads to an extended region of preferred values. Our universe is found to be disfavored to an extent depending on the priors on curvature. We also provide a comparison to previous anthropic constraints on open universes and discuss future directions for this work.

  18. A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

    PubMed

    Lu, Hongjing; Rojas, Randall R; Beckers, Tom; Yuille, Alan L

    2016-03-01

    Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.

  19. Lifelong Learning in Finland: The Extent to Which Vocational Education and Training Policy Is Nurturing Lifelong Learning in Finland. CEDEFOP Panorama Series.

    ERIC Educational Resources Information Center

    Nyyssola, Kari; Hamalainen, Kimmo

    The extent to which vocational education and training policy is nurturing lifelong learning in Finland was examined. The analysis focused on the following issues: the political and structural framework of education in Finland; mechanisms supporting lifelong learning; and pedagogical solutions and learning environments facilitating lifelong…

  20. Developing Causal Understanding with Causal Maps: The Impact of Total Links, Temporal Flow, and Lateral Position of Outcome Nodes

    ERIC Educational Resources Information Center

    Jeong, Allan; Lee, Woon Jee

    2012-01-01

    This study examined some of the methodological approaches used by students to construct causal maps in order to determine which approaches help students understand the underlying causes and causal mechanisms in a complex system. This study tested the relationship between causal understanding (ratio of root causes correctly/incorrectly identified,…

  1. Causal structure in categorical quantum mechanics

    NASA Astrophysics Data System (ADS)

    Lal, Raymond Ashwin

    Categorical quantum mechanics is a way of formalising the structural features of quantum theory using category theory. It uses compound systems as the primitive notion, which is formalised by using symmetric monoidal categories. This leads to an elegant formalism for describing quantum protocols such as quantum teleportation. In particular, categorical quantum mechanics provides a graphical calculus that exposes the information flow of such protocols in an intuitive way. However, the graphical calculus also reveals surprising features of these protocols; for example, in the quantum teleportation protocol, information appears to flow `backwards-in-time'. This leads to question of how causal structure can be described within categorical quantum mechanics, and how this might lead to insight regarding the structural compatibility between quantum theory and relativity. This thesis is concerned with the project of formalising causal structure in categorical quantum mechanics. We begin by studying an abstract view of Bell-type experiments, as described by `no-signalling boxes', and we show that under time-reversal no-signalling boxes generically become signalling. This conflicts with the underlying symmetry of relativistic causal structure. This leads us to consider the framework of categorical quantum mechanics from the perspective of relativistic causal structure. We derive the properties that a symmetric monoidal category must satisfy in order to describe systems in such a background causal structure. We use these properties to define a new type of category, and this provides a formal framework for describing protocols in spacetime. We explore this new structure, showing how it leads to an understanding of the counter-intuitive information flow of protocols in categorical quantum mechanics. We then find that the formal properties of our new structure are naturally related to axioms for reconstructing quantum theory, and we show how a reconstruction scheme based on

  2. How to establish causality in epilepsy surgery.

    PubMed

    Asano, Eishi; Brown, Erik C; Juhász, Csaba

    2013-09-01

    Focality in electro-clinical or neuroimaging data often motivates epileptologists to consider epilepsy surgery in patients with medically-uncontrolled seizures, while not all focal findings are causally associated with the generation of epileptic seizures. With the help of Hill's criteria, we have discussed how to establish causality in the context of the presurgical evaluation of epilepsy. The strengths of EEG include the ability to determine the temporal relationship between cerebral activities and clinical events; thus, scalp video-EEG is necessary in the evaluation of the majority of surgical candidates. The presence of associated ictal discharges can confirm the epileptic nature of a particular spell and whether an observed neuroimaging abnormality is causally associated with the epileptic seizure. Conversely, one should be aware that scalp EEG has a limited spatial resolution and sometimes exhibits propagated epileptiform discharges more predominantly than in situ discharges generated at the seizure-onset zone. Intraoperative or extraoperative electrocorticography (ECoG) is utilized when noninvasive presurgical evaluation, including anatomical and functional neuroimaging, fails to determine the margin between the presumed epileptogenic zone and eloquent cortex. Retrospective as well as prospective studies have reported that complete resection of the seizure-onset zone on ECoG was associated with a better seizure outcome, but not all patients became seizure-free following such resective surgery. Some retrospective studies suggested that resection of sites showing high-frequency oscillations (HFOs) at >80Hz on interictal or ictal ECoG was associated with a better seizure outcome. Others reported that functionally-important areas may generate HFOs of a physiological nature during rest as well as sensorimotor and cognitive tasks. Resection of sites showing task-related augmentation of HFOs has been reported to indeed result in functional loss following surgery

  3. Information causality from an entropic and a probabilistic perspective

    SciTech Connect

    Al-Safi, Sabri W.; Short, Anthony J.

    2011-10-15

    The information causality principle is a generalization of the no-signaling principle which implies some of the known restrictions on quantum correlations. But despite its clear physical motivation, information causality is formulated in terms of a rather specialized game and figure of merit. We explore different perspectives on information causality, discussing the probability of success as the figure of merit, a relation between information causality and the nonlocal ''inner-product game,'' and the derivation of a quadratic bound for these games. We then examine an entropic formulation of information causality with which one can obtain the same results, arguably in a simpler fashion.

  4. Acute Human Inkoo and Chatanga Virus Infections, Finland

    PubMed Central

    Kantele, Anu; Levanov, Lev; Kivistö, Ilkka; Brummer-Korvenkontio, Markus; Vaheri, Antti; Vapalahti, Olli

    2016-01-01

    Inkoo virus (INKV) and Chatanga virus (CHATV), which are circulating in Finland, are mosquitoborne California serogroup orthobunyaviruses that have a high seroprevalence among humans. Worldwide, INKV infection has been poorly described, and CHATV infection has been unknown. Using serum samples collected in Finland from 7,961 patients suspected of having viral neurologic disease or Puumala virus infection during the summers of 2001–2013, we analyzed the samples to detect California serogroup infections. IgM seropositivity revealed 17 acute infections, and cross-neutralization tests confirmed presence of INKV or CHATV infections. All children (<16 years of age) with INKV infection were hospitalized; adults were outpatients with mild disease, except for 1 who was hospitalized with CHATV infection. Symptoms included fever, influenza-like illness, nausea or vomiting, disorientation, nuchal rigidity, headache, drowsiness, and seizures. Although many INKV and CHATV infections appear to be subclinical, these viruses can cause more severe disease, especially in children. PMID:27088268

  5. Heat Wave–Associated Vibriosis, Sweden and Finland, 2014

    PubMed Central

    Trinanes, Joaquin A.; Salmenlinna, Saara; Löfdahl, Margareta; Siitonen, Anja; Taylor, Nick G.H.; Martinez-Urtaza, Jaime

    2016-01-01

    During summer 2014, a total of 89 Vibrio infections were reported in Sweden and Finland, substantially more yearly infections than previously have been reported in northern Europe. Infections were spread across most coastal counties of Sweden and Finland, but unusually, numerous infections were reported in subarctic regions; cases were reported as far north as 65°N, ≈100 miles (160 km) from the Arctic Circle. Most infections were caused by non-O1/O139 V. cholerae (70 cases, corresponding to 77% of the total, all strains were negative for the cholera toxin gene). An extreme heat wave in northern Scandinavia during summer 2014 led to unprecedented high sea surface temperatures, which appear to have been responsible for the emergence of Vibrio bacteria at these latitudes. The emergence of vibriosis in high-latitude regions requires improved diagnostic detection and clinical awareness of these emerging pathogens. PMID:27314874

  6. Finland on a road towards a modern legal biobanking infrastructure.

    PubMed

    Soini, Sirpa

    2013-06-01

    Finland has enacted a Biobank Act that will come into force on 1 September 2013. Finland is regarded as a highly successful environment for medical research using population samples and data for many reasons. One of the rationales behind the new legislation was to solve the problems due to the overly strict informed consent doctrine hindering access to old samples and data and asking for multi-purpose consents. Yet although consent is the primary justification to use biobank samples and data, the Biobank Act allows asking for a consent for several unspecified future research purposes. The guiding principles of the Biobank Act are promotion of trust, equal access to data and samples, protection of privacy, acceleration of innovation activities, and bringing biobank activities under public scrutiny. To the author's knowledge, this is the first "all purpose" Biobank Act in Europe applied to all biobanks in one country.

  7. Heat Wave-Associated Vibriosis, Sweden and Finland, 2014.

    PubMed

    Baker-Austin, Craig; Trinanes, Joaquin A; Salmenlinna, Saara; Löfdahl, Margareta; Siitonen, Anja; Taylor, Nick G H; Martinez-Urtaza, Jaime

    2016-07-01

    During summer 2014, a total of 89 Vibrio infections were reported in Sweden and Finland, substantially more yearly infections than previously have been reported in northern Europe. Infections were spread across most coastal counties of Sweden and Finland, but unusually, numerous infections were reported in subarctic regions; cases were reported as far north as 65°N, ≈100 miles (160 km) from the Arctic Circle. Most infections were caused by non-O1/O139 V. cholerae (70 cases, corresponding to 77% of the total, all strains were negative for the cholera toxin gene). An extreme heat wave in northern Scandinavia during summer 2014 led to unprecedented high sea surface temperatures, which appear to have been responsible for the emergence of Vibrio bacteria at these latitudes. The emergence of vibriosis in high-latitude regions requires improved diagnostic detection and clinical awareness of these emerging pathogens. PMID:27314874

  8. Causal-explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions.

    PubMed

    Lombrozo, Tania

    2010-12-01

    Both philosophers and psychologists have argued for the existence of distinct kinds of explanations, including teleological explanations that cite functions or goals, and mechanistic explanations that cite causal mechanisms. Theories of causation, in contrast, have generally been unitary, with dominant theories focusing either on counterfactual dependence or on physical connections. This paper argues that both approaches to causation are psychologically real, with different modes of explanation promoting judgments more or less consistent with each approach. Two sets of experiments isolate the contributions of counterfactual dependence and physical connections in causal ascriptions involving events with people, artifacts, or biological traits, and manipulate whether the events are construed teleologically or mechanistically. The findings suggest that when events are construed teleologically, causal ascriptions are sensitive to counterfactual dependence and relatively insensitive to the presence of physical connections, but when events are construed mechanistically, causal ascriptions are sensitive to both counterfactual dependence and physical connections. The conclusion introduces an account of causation, an "exportable dependence theory," that provides a way to understand the contributions of physical connections and teleology in terms of the functions of causal ascriptions. PMID:20801434

  9. Causal-explanatory pluralism: How intentions, functions, and mechanisms influence causal ascriptions.

    PubMed

    Lombrozo, Tania

    2010-12-01

    Both philosophers and psychologists have argued for the existence of distinct kinds of explanations, including teleological explanations that cite functions or goals, and mechanistic explanations that cite causal mechanisms. Theories of causation, in contrast, have generally been unitary, with dominant theories focusing either on counterfactual dependence or on physical connections. This paper argues that both approaches to causation are psychologically real, with different modes of explanation promoting judgments more or less consistent with each approach. Two sets of experiments isolate the contributions of counterfactual dependence and physical connections in causal ascriptions involving events with people, artifacts, or biological traits, and manipulate whether the events are construed teleologically or mechanistically. The findings suggest that when events are construed teleologically, causal ascriptions are sensitive to counterfactual dependence and relatively insensitive to the presence of physical connections, but when events are construed mechanistically, causal ascriptions are sensitive to both counterfactual dependence and physical connections. The conclusion introduces an account of causation, an "exportable dependence theory," that provides a way to understand the contributions of physical connections and teleology in terms of the functions of causal ascriptions.

  10. The third harmonic in the Russia-Finland DC interconnection

    SciTech Connect

    Kazachkov, Yu. ); Boyarsky, A.; Kraichik, Yu. )

    1994-10-01

    During 12 years of operation of the DC back-to-back tie between Russia and Finland some undesirable phenomena at frequencies close to the third harmonic have been noticed. They may become more severe after the planned upgrading of the converter station. Steady state and transient processes with dominant third harmonic and their improvement by means of filters in the power and control circuits have been studied. Recordings of steady states with noticeable third harmonic are included.

  11. Mercury pollution near an industrial source in southwest Finland

    SciTech Connect

    Hynninen, V.; Lodenius, M.

    1986-02-01

    Mercury is very sparse in Finnish rocks and soils. Some mercury occurs in the ore of the Outokumpu mine, SE Finland. Metal ore from this mine is refined in the metallurgical plants at Kokkola and Harjavalta. Elevated mercury contents have been observed in the environment of the plant at Kokkola but no data have been published about the possible mercury contamination around the Harjavalta plant.

  12. The selective power of causality on memory errors.

    PubMed

    Marsh, Jessecae K; Kulkofsky, Sarah

    2015-01-01

    We tested the influence of causal links on the production of memory errors in a misinformation paradigm. Participants studied a set of statements about a person, which were presented as either individual statements or pairs of causally linked statements. Participants were then provided with causally plausible and causally implausible misinformation. We hypothesised that studying information connected with causal links would promote representing information in a more abstract manner. As such, we predicted that causal information would not provide an overall protection against memory errors, but rather would preferentially help in the rejection of misinformation that was causally implausible, given the learned causal links. In two experiments, we measured whether the causal linkage of information would be generally protective against all memory errors or only selectively protective against certain types of memory errors. Causal links helped participants reject implausible memory lures, but did not protect against plausible lures. Our results suggest that causal information may promote an abstract storage of information that helps prevent only specific types of memory errors.

  13. Health reform in Finland: current proposals and unresolved challenges.

    PubMed

    Saltman, Richard B; Teperi, Juha

    2016-07-01

    The Finnish health care system is widely respected for its pilot role in creating primary-care-led health systems. In the early 1990s, however, a severe economic downturn in Finland reduced public funding and weakened the Finnish system's deeply decentralized model of health care administration. Recent Bank of Finland projections forecasting several decades of slow economic growth, combined with the impact of an aging population, appear to make major reform of the existing public system inevitable. Over the last several years, political attention has focused mostly on administrative consolidation inside the public sector, particularly integration of health and social services. Current proposals call for a reformed health sector governance structure based on a new meso-level configuration of public administration. In addition, Finland's national government has proposed replacing the current multi-channel public funding structure (which includes health insurance subsidies for occupational health services) with a single-channel public funding structure. This commentary examines several key issues involved in reforming the delivery structure of the Finnish health care system. It also explores possible alternative strategies to reform current funding arrangements. The article concludes with a brief discussion of implications from this Finnish experience for the wider health reform debate. PMID:26865494

  14. Green energy products in the United Kingdom, Germany and Finland

    NASA Astrophysics Data System (ADS)

    Hast, Aira; McDermott, Liisa; Järvelä, Marja; Syri, Sanna

    2014-12-01

    In liberalized electricity markets, suppliers are offering several kinds of voluntary green electricity products marketed as environmentally friendly. This paper focuses on the development of these voluntary markets at household level in the UK, Germany and Finland. Since there are already existing renewable energy policies regulating and encouraging the use of renewable energy, it is important to consider whether voluntary products offer real additional benefits above these policies. Problems such as double counting or re-marketing hydropower produced in existing plants are identified. According to our study, the demand varies between countries: in Germany the number of green electricity customers has increased and is also higher than in the UK or Finland. Typically the average additional cost to consumer from buying green electricity product instead of standard electricity product is in the range of 0-5% in all studied countries, although the level of price premium depends on several factors like electricity consumption. Case study of Finland and literature show that the impacts of green energy are not solely environmental. Renewable energy can benefit local public policy.

  15. Causality and Causal Inference in Social Work: Quantitative and Qualitative Perspectives

    PubMed Central

    Palinkas, Lawrence A.

    2015-01-01

    Achieving the goals of social work requires matching a specific solution to a specific problem. Understanding why the problem exists and why the solution should work requires a consideration of cause and effect. However, it is unclear whether it is desirable for social workers to identify cause and effect, whether it is possible for social workers to identify cause and effect, and, if so, what is the best means for doing so. These questions are central to determining the possibility of developing a science of social work and how we go about doing it. This article has four aims: (1) provide an overview of the nature of causality; (2) examine how causality is treated in social work research and practice; (3) highlight the role of quantitative and qualitative methods in the search for causality; and (4) demonstrate how both methods can be employed to support a “science” of social work. PMID:25821393

  16. Civil Engineering Applications of Ground Penetrating Radar in Finland

    NASA Astrophysics Data System (ADS)

    Pellinen, Terhi; Huuskonen-Snicker, Eeva; Olkkonen, Martta-Kaisa; Eskelinen, Pekka

    2014-05-01

    Ground penetrating radar (GPR) has been used in Finland since 1980's for civil engineering applications. First applications in this field were road surveys and dam inspections. Common GPR applications in road surveys include the thickness evaluation of the pavement, subgrade soil evaluation and evaluation of the soil moisture and frost susceptibility. Since the 1990's, GPR has been used in combination with other non-destructive testing (NDT) methods in road surveys. Recently, more GPR applications have been adopted, such as evaluating bridges, tunnels, railways and concrete elements. Nowadays, compared with other countries GPR is relatively widely used in Finland for road surveys. Quite many companies, universities and research centers in Finland have their own GPR equipment and are involved in the teaching and research of the GPR method. However, further research and promotion of the GPR techniques are still needed since GPR could be used more routinely. GPR has been used to evaluate the air void content of asphalt pavements for years. Air void content is an important quality measure of pavement condition for both the new and old asphalt pavements. The first Finnish guideline was released in 1999 for the method. Air void content is obtained from the GPR data by measuring the dielectric value as continuous record. To obtain air void content data, few pavement cores must be taken for calibration. Accuracy of the method is however questioned because there are other factors that affect the dielectric value of the asphalt layer, in addition to the air void content. Therefore, a research project is currently carried out at Aalto University in Finland. The overall objective is to investigate if the existing GPR technique used in Finland is accurate enough to be used as QC/QA tool in assessing the compaction of asphalt pavements. The project is funded by the Finnish Transport Agency. Further research interests at Aalto University include developing new microwave asphalt

  17. Neuroleptic malignant syndrome: mechanisms, interactions, and causality.

    PubMed

    Gillman, P Ken

    2010-09-15

    This review focuses on new data from recent publications concerning how compounding interactions between different thermoregulatory pathways influence the development of hyperthermia and/or neuroleptic malignant syndrome (NMS), and the fundamental issue of the presumed causal role of antipsychotic drugs. The formal criteria for substantiating cause-effect relationships in medical science, established by Hill, are applied to NMS and, for comparison, also to malignant hyperthermia and serotonin toxicity. The risk of morbidities related to hyperthermia is reviewed from human and experimental data: temperatures in excess of 39.5°C cause physiological and cellular dysfunction and high mortality. The most temperature-sensitive elements of neural cells are mitochondrial and plasma membranes, in which irreversible changes occur around 40°C. Temperatures of up to 39°C are "normal" in mammals, so, the term hyperthermia should be reserved for temperatures of 39.5°C or greater. The implicitly accepted presumption that NMS is a hypermetabolic and hyperthermic syndrome is questionable and does not explain the extensive morbidity in the majority of cases, where the temperature is less than 39°C. The thermoregulatory effects of dopamine and acetylcholine are outlined, especially because they are probably the main pathways by which neuroleptic drugs might affect thermoregulation. It is notable that even potent antagonism of these mechanisms rarely causes temperature elevation and that multiple mechanisms, including the acute phase response, stress-induced hyperthermia, drugs effects, etc., involving compounding interactions, are required to precipitate hyperthermia. The application of the Hill criteria clearly supports causality for drugs inducing both MH and ST but do not support causality for NMS.

  18. Causal Loop Analysis of coastal geomorphological systems

    NASA Astrophysics Data System (ADS)

    Payo, Andres; Hall, Jim W.; French, Jon; Sutherland, James; van Maanen, Barend; Nicholls, Robert J.; Reeve, Dominic E.

    2016-03-01

    As geomorphologists embrace ever more sophisticated theoretical frameworks that shift from simple notions of evolution towards single steady equilibria to recognise the possibility of multiple response pathways and outcomes, morphodynamic modellers are facing the problem of how to keep track of an ever-greater number of system feedbacks. Within coastal geomorphology, capturing these feedbacks is critically important, especially as the focus of activity shifts from reductionist models founded on sediment transport fundamentals to more synthesist ones intended to resolve emergent behaviours at decadal to centennial scales. This paper addresses the challenge of mapping the feedback structure of processes controlling geomorphic system behaviour with reference to illustrative applications of Causal Loop Analysis at two study cases: (1) the erosion-accretion behaviour of graded (mixed) sediment beds, and (2) the local alongshore sediment fluxes of sand-rich shorelines. These case study examples are chosen on account of their central role in the quantitative modelling of geomorphological futures and as they illustrate different types of causation. Causal loop diagrams, a form of directed graph, are used to distil the feedback structure to reveal, in advance of more quantitative modelling, multi-response pathways and multiple outcomes. In the case of graded sediment bed, up to three different outcomes (no response, and two disequilibrium states) can be derived from a simple qualitative stability analysis. For the sand-rich local shoreline behaviour case, two fundamentally different responses of the shoreline (diffusive and anti-diffusive), triggered by small changes of the shoreline cross-shore position, can be inferred purely through analysis of the causal pathways. Explicit depiction of feedback-structure diagrams is beneficial when developing numerical models to explore coastal morphological futures. By explicitly mapping the feedbacks included and neglected within a

  19. Rapidity Correlation Structures from Causal Hydrodynamics

    NASA Astrophysics Data System (ADS)

    Gavin, Sean; Moschelli, George; Zin, Christopher

    2016-08-01

    Viscous diffusion can broaden the rapidity dependence of two-particle transverse momentum fluctuations. Surprisingly, measurements at RHIC by the STAR collaboration demonstrate that this broadening is accompanied by the appearance of unanticipated structure in the rapidity distribution of these fluctuations in the most central collisions. Although a first order classical Navier-Stokes theory can roughly explain the rapidity broadening, it cannot explain the additional structure. We propose that the rapidity structure can be explained using the second order causal Israel-Stewart hydrodynamics with stochastic noise.

  20. Foundational perspectives on causality in large-scale brain networks

    NASA Astrophysics Data System (ADS)

    Mannino, Michael; Bressler, Steven L.

    2015-12-01

    A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical

  1. Foundational perspectives on causality in large-scale brain networks.

    PubMed

    Mannino, Michael; Bressler, Steven L

    2015-12-01

    A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical

  2. God Does Not Play Dice: Causal Determinism and Preschoolers' Causal Inferences

    ERIC Educational Resources Information Center

    Schulz, Laura E.; Sommerville, Jessica

    2006-01-01

    Three studies investigated children's belief in causal determinism. If children are determinists, they should infer unobserved causes whenever observed causes appear to act stochastically. In Experiment 1, 4-year-olds saw a stochastic generative cause and inferred the existence of an unobserved inhibitory cause. Children traded off inferences…

  3. Interference between Cues Requires a Causal Scenario: Favorable Evidence for Causal Reasoning Models in Learning Processes

    ERIC Educational Resources Information Center

    Luque, David; Cobos, Pedro L.; Lopez, Francisco J.

    2008-01-01

    In an interference-between-cues design (IbC), the expression of a learned Cue A-Outcome 1 association has been shown to be impaired if another cue, B, is separately paired with the same outcome in a second learning phase. The present study examined whether IbC could be caused by associative mechanisms independent of causal reasoning processes.…

  4. Temporal predictability enhances judgements of causality in elemental causal induction from both observation and intervention.

    PubMed

    Greville, W James; Buehner, Marc J

    2016-01-01

    When the temporal interval or delay separating cause and effect is consistent over repeated instances, it becomes possible to predict when the effect will follow from the cause, hence temporal predictability serves as an appropriate term for describing consistent cause-effect delays. It has been demonstrated that in instrumental action-outcome learning tasks, enhancing temporal predictability by holding the cause-effect interval constant elicits higher judgements of causality compared to conditions involving variable temporal intervals. Here, we examine whether temporal predictability exerts a similar influence when causal learning takes place through observation rather than intervention through instrumental action. Four experiments demonstrated that judgements of causality were higher when the temporal interval was constant than when it was variable, and that judgements declined with increasing variability. We further found that this beneficial effect of predictability was stronger in situations where the effect base-rate was zero (Experiments 1 and 3). The results therefore clearly indicate that temporal predictability enhances impressions of causality, and that this effect is robust and general. Factors that could mediate this effect are discussed.

  5. Causal-Explanatory Pluralism: How Intentions, Functions, and Mechanisms Influence Causal Ascriptions

    ERIC Educational Resources Information Center

    Lombrozo, Tania

    2010-01-01

    Both philosophers and psychologists have argued for the existence of distinct kinds of explanations, including teleological explanations that cite functions or goals, and mechanistic explanations that cite causal mechanisms. Theories of causation, in contrast, have generally been unitary, with dominant theories focusing either on counterfactual…

  6. Demographics, Causality, Work Salience, and the Career Maturity of African-American Students: A Causal Model.

    ERIC Educational Resources Information Center

    Naidoo, Anthony V.; Bowman, Sharon L.; Gerstein, Lawrence H.

    1998-01-01

    A model proposing that causality and work salience moderate the influence of gender, educational level, and socioeconomic status on career maturity was tested with 288 African-American students. Work salience had the strongest direct effect on career maturity. For these students home/family had higher salience than did work. (SK)

  7. Evidence for online processing during causal learning.

    PubMed

    Liu, Pei-Pei; Luhmann, Christian C

    2015-03-01

    Many models of learning describe both the end product of learning (e.g., causal judgments) and the cognitive mechanisms that unfold on a trial-by-trial basis. However, the methods employed in the literature typically provide only indirect evidence about the unfolding cognitive processes. Here, we utilized a simultaneous secondary task to measure cognitive processing during a straightforward causal-learning task. The results from three experiments demonstrated that covariation information is not subject to uniform cognitive processing. Instead, we observed systematic variation in the processing dedicated to individual pieces of covariation information. In particular, observations that are inconsistent with previously presented covariation information appear to elicit greater cognitive processing than do observations that are consistent with previously presented covariation information. In addition, the degree of cognitive processing appears to be driven by learning per se, rather than by nonlearning processes such as memory and attention. Overall, these findings suggest that monitoring learning processes at a finer level may provide useful psychological insights into the nature of learning.

  8. Causal mechanisms in airfoil-circulation formation

    NASA Astrophysics Data System (ADS)

    Zhu, J. Y.; Liu, T. S.; Liu, L. Q.; Zou, S. F.; Wu, J. Z.

    2015-12-01

    In this paper, we trace the dynamic origin, rather than any kinematic interpretations, of lift in two-dimensional flow to the physical root of airfoil circulation. We show that the key causal process is the vorticity creation by tangent pressure gradient at the airfoil surface via no-slip condition, of which the theoretical basis has been given by Lighthill ["Introduction: Boundary layer theory," in Laminar Boundary Layers, edited by L. Rosenhead (Clarendon Press, 1963), pp. 46-113], which we further elaborate. This mechanism can be clearly revealed in terms of vorticity formulation but is hidden in conventional momentum formulation, and hence has long been missing in the history of one's efforts to understand lift. By a careful numerical simulation of the flow around a NACA-0012 airfoil, and using both Eulerian and Lagrangian descriptions, we illustrate the detailed transient process by which the airfoil gains its circulation and demonstrate the dominating role of relevant dynamical causal mechanisms at the boundary. In so doing, we find that the various statements for the establishment of Kutta condition in steady inviscid flow actually correspond to a sequence of events in unsteady viscous flow.

  9. Causal Inference for Spatial Constancy across Saccades.

    PubMed

    Atsma, Jeroen; Maij, Femke; Koppen, Mathieu; Irwin, David E; Medendorp, W Pieter

    2016-03-01

    Our ability to interact with the environment hinges on creating a stable visual world despite the continuous changes in retinal input. To achieve visual stability, the brain must distinguish the retinal image shifts caused by eye movements and shifts due to movements of the visual scene. This process appears not to be flawless: during saccades, we often fail to detect whether visual objects remain stable or move, which is called saccadic suppression of displacement (SSD). How does the brain evaluate the memorized information of the presaccadic scene and the actual visual feedback of the postsaccadic visual scene in the computations for visual stability? Using a SSD task, we test how participants localize the presaccadic position of the fixation target, the saccade target or a peripheral non-foveated target that was displaced parallel or orthogonal during a horizontal saccade, and subsequently viewed for three different durations. Results showed different localization errors of the three targets, depending on the viewing time of the postsaccadic stimulus and its spatial separation from the presaccadic location. We modeled the data through a Bayesian causal inference mechanism, in which at the trial level an optimal mixing of two possible strategies, integration vs. separation of the presaccadic memory and the postsaccadic sensory signals, is applied. Fits of this model generally outperformed other plausible decision strategies for producing SSD. Our findings suggest that humans exploit a Bayesian inference process with two causal structures to mediate visual stability. PMID:26967730

  10. Immunity in arterial hypertension: associations or causalities?

    PubMed

    Anders, Hans-Joachim; Baumann, Marcus; Tripepi, Giovanni; Mallamaci, Francesca

    2015-12-01

    Numerous studies describe associations between markers of inflammation and arterial hypertension (aHT), but does that imply causality? Interventional studies that reduce blood pressure reduced also markers of inflammation, but does immunosuppression improve hypertension? Here, we review the available mechanistic data. Aberrant immunity can trigger endothelial dysfunction but is hardly ever the primary cause of aHT. Innate and adaptive immunity get involved once hypertension has caused vascular wall injury as immunity is a modifier of endothelial dysfunction and vascular wall remodelling. As vascular remodelling progresses, immunity-related mechanisms can become significant cofactors for cardiovascular (CV) disease progression; vice versa, suppressing immunity can improve hypertension and CV outcomes. Innate and adaptive immunity both contribute to vascular wall remodelling. Innate immunity is driven by danger signals that activate Toll-like receptors and other pattern-recognition receptors. Adaptive immunity is based on loss of tolerance against vascular autoantigens and includes autoreactive T-cell immunity as well as non-HLA angiotensin II type 1 receptor-activating autoantibodies. Such processes involve numerous other modulators such as regulatory T cells. Together, immunity is not causal for hypertension but rather an important secondary pathomechanism and a potential therapeutic target in hypertension.

  11. Emergent horizons and causal structures in holography

    NASA Astrophysics Data System (ADS)

    Banerjee, Avik; Kundu, Arnab; Kundu, Sandipan

    2016-09-01

    The open string metric arises kinematically in studying fluctuations of open string degrees of freedom on a D-brane. An observer, living on a probe D-brane, can send signals through the spacetime by using such fluctuations on the probe, that propagate in accordance with a metric which is conformal to the open string metric. Event horizons can emerge in the open string metric when one considers a D-brane with an electric field on its worldvolume. Here, we emphasize the role of and investigate, in details, the causal structure of the resulting open string event horizon and demonstrate, among other things, its close similarities to an usual black hole event horizon in asymptotically AdS-spaces. To that end, we analyze relevant geodesics, Penrose diagrams and various causal holographic observables for a given open string metric. For analytical control, most of our calculations are performed in an asymptotically AdS3-background, however, we argue that the physics is qualitatively the same in higher dimensions. We also discuss how this open string metric arises from an underlying D-brane configuration in string theory.

  12. Causal Drift, Robust Signaling, and Complex Disease

    PubMed Central

    Wagner, Andreas

    2015-01-01

    The phenotype of many regulatory circuits in which mutations can cause complex, polygenic diseases is to some extent robust to DNA mutations that affect circuit components. Here I demonstrate how such mutational robustness can prevent the discovery of genetic disease determinants. To make my case, I use a mathematical model of the insulin signaling pathway implicated in type 2 diabetes, whose signaling output is governed by 15 genetically determined parameters. Using multiple complementary measures of a parameter’s importance for this phenotype, I show that any one disease determinant that is crucial in one genetic background will be virtually irrelevant in other backgrounds. In an evolving population that drifts through the parameter space of this or other robust circuits through DNA mutations, the genetic changes that can cause disease will vary randomly over time. I call this phenomenon causal drift. It means that mutations causing disease in one (human or non-human) population may have no effect in another population, and vice versa. Causal drift casts doubt on our ability to infer the molecular mechanisms of complex diseases from non-human model organisms. PMID:25774510

  13. A definition of causal effect for epidemiological research.

    PubMed

    Hernán, M A

    2004-04-01

    Estimating the causal effect of some exposure on some outcome is the goal of many epidemiological studies. This article reviews a formal definition of causal effect for such studies. For simplicity, the main description is restricted to dichotomous variables and assumes that no random error attributable to sampling variability exists. The appendix provides a discussion of sampling variability and a generalisation of this causal theory. The difference between association and causation is described-the redundant expression "causal effect" is used throughout the article to avoid confusion with a common use of "effect" meaning simply statistical association-and shows why, in theory, randomisation allows the estimation of causal effects without further assumptions. The article concludes with a discussion on the limitations of randomised studies. These limitations are the reason why methods for causal inference from observational data are needed.

  14. The production potential of wind power in Finland

    NASA Astrophysics Data System (ADS)

    Peltola, Esa

    1989-03-01

    The production potential of wind power in Finland is estimated by mapping and classifying the coastal areas and the archipelago of Finland by the terrain and by land use restrictions. Estimates for production costs are given based on present cost levels of wind turbines. An area of 106,000 sq km was mapped. The classification by terrain was made using topographic maps in scale 1:100,000. The restrictions of land use were classified according to regional plans published by regional authorities. The production potential was calculated for land-based and island-based wind power plants using areas belonging to terrain class 1 (coastal areas, open farm lands) and to land use category with no restrictions. These areas have an area of 2000 sq km, which is about 2 percent of the total area investigated. The terrain classification was used to described the wind conditions in coastal Finland. The mean wind speed at the height of 100 m is 7 to 8 m/s on off-shore areas near the coast line and on a narrow strip on shore and 6 to 7 m/s at the height of 50 m. The wind speed declines fast from coast line to inland locations. The production potential for land based wind power plants was about 4.3 TWh/a using wind turbines of about 50 m both in hub height and in rotor diameter and having rated power of about 1 MW. Production costs of less than 0.50 FIM/kWh were estimated for some 1.3 TWh/a of this potential.

  15. Assessment of doses to game animals in Finland.

    PubMed

    Vetikko, Virve; Kostiainen, Eila

    2013-11-01

    A study was carried out to assess the dose rates to game animals in Finland affected by the radioactive caesium deposition that occurred after the accident at the Chernobyl nuclear power plant in Ukraine in 1986. The aim of this assessment was to obtain new information on the dose rates to mammals and birds under Finnish conditions. Dose rates were calculated using the ERICA Assessment Tool developed within the EC 6th Framework Programme. The input data consisted of measured activity concentrations of (137)Cs and (134)Cs in soil and lake water samples and in flesh samples of selected animal species obtained for environmental monitoring. The study sites were located in the municipality of Lammi, Southern Finland, where the average (137)Cs deposition was 46.5 kBq m(-2) (1 October 1987). The study sites represented the areas receiving the highest deposition in Finland after the Chernobyl accident. The selected species included moose (Alces alces), arctic hare (Lepus timidus) and several bird species: black grouse (Tetrao tetrix), hazel hen (Bonasia bonasia), mallard (Anas platurhynchos), goldeneye (Bucephala clangula) and teal (Anas crecca). For moose, dose rates were calculated for the years 1986-1990 and for the 2000s. For all other species, maximal measured activity concentrations were used. The results showed that the dose rates to these species did not exceed the default screening level of 10 μGy h(-1) used as a protection criterion. The highest total dose rate (internal and external summed), 3.7 μGy h(-1), was observed for the arctic hare in 1986. Although the dose rate of 3.7 μGy h(-1) cannot be considered negligible given the uncertainties involved in predicting the dose rates, the possible harmful effects related to this dose rate are too small to be assessed based on current knowledge on the biological effects of low doses in mammals.

  16. Causal inference and the hierarchical structure of experience

    PubMed Central

    Johnson, Samuel G. B.; Keil, Frank C.

    2014-01-01

    Children and adults make rich causal inferences about the physical and social world, even in novel situations where they cannot rely on prior knowledge of causal mechanisms. We propose that this capacity is supported in part by constraints provided by event structure—the cognitive organization of experience into discrete events that are hierarchically organized. These event-structured causal inferences are guided by a level-matching principle, with events conceptualized at one level of an event hierarchy causally matched to other events at that same level, and a boundary-blocking principle, with events causally matched to other events that are parts of the same superordinate event. These principles are used to constrain inferences about plausible causal candidates in unfamiliar situations, both in diagnosing causes (Experiment 1) and predicting effects (Experiment 2). The results could not be explained by construal level (Experiment 3) or similarity-matching (Experiment 4), and were robust across a variety of physical and social causal systems. Taken together, these experiments demonstrate a novel way in which non-causal information we extract from the environment can help to constrain inferences about causal structure. PMID:25347533

  17. Establishing causal coherence across sentences: an ERP study

    PubMed Central

    Kuperberg, Gina R.; Paczynski, Martin; Ditman, Tali

    2011-01-01

    This study examined neural activity associated with establishing causal relationships across sentences during online comprehension. ERPs were measured while participants read and judged the relatedness of three-sentence scenarios in which the final sentence was highly causally related, intermediately related and causally unrelated to its context. Lexico-semantic co-occurrence was matched across the three conditions using a Latent Semantic Analysis. Critical words in causally unrelated scenarios evoked a larger N400 than words in both highly causally related and intermediately related scenarios, regardless of whether they appeared before or at the sentence-final position. At midline sites, the N400 to intermediately related sentence-final words was attenuated to the same degree as to highly causally related words, but otherwise the N400 to intermediately related words fell in between that evoked by highly causally related and intermediately related words. No modulation of the Late Positivity/P600 component was observed across conditions. These results indicate that both simple and complex causal inferences can influence the earliest stages of semantically processing an incoming word. Further, they suggest that causal coherence, at the situation level, can influence incremental word-by-word discourse comprehension, even when semantic relationships between individual words are matched. PMID:20175676

  18. A general, multivariate definition of causal effects in epidemiology.

    PubMed

    Flanders, W Dana; Klein, Mitchel

    2015-07-01

    Population causal effects are often defined as contrasts of average individual-level counterfactual outcomes, comparing different exposure levels. Common examples include causal risk difference and risk ratios. These and most other examples emphasize effects on disease onset, a reflection of the usual epidemiological interest in disease occurrence. Exposure effects on other health characteristics, such as prevalence or conditional risk of a particular disability, can be important as well, but contrasts involving these other measures may often be dismissed as non-causal. For example, an observed prevalence ratio might often viewed as an estimator of a causal incidence ratio and hence subject to bias. In this manuscript, we provide and evaluate a definition of causal effects that generalizes those previously available. A key part of the generalization is that contrasts used in the definition can involve multivariate, counterfactual outcomes, rather than only univariate outcomes. An important consequence of our generalization is that, using it, one can properly define causal effects based on a wide variety of additional measures. Examples include causal prevalence ratios and differences and causal conditional risk ratios and differences. We illustrate how these additional measures can be useful, natural, easily estimated, and of public health importance. Furthermore, we discuss conditions for valid estimation of each type of causal effect, and how improper interpretation or inferences for the wrong target population can be sources of bias.

  19. Causality and stability of cosmic jets

    NASA Astrophysics Data System (ADS)

    Porth, Oliver; Komissarov, Serguei S.

    2015-09-01

    In stark contrast to their laboratory and terrestrial counterparts, cosmic jets appear to be very stable. They are able to penetrate vast spaces, which exceed by up to a billion times the size of their central engines. We propose that the reason behind this remarkable property is the loss of causal connectivity across these jets, caused by their rapid expansion in response to fast decline of external pressure with the distance from the `jet engine'. In atmospheres with power-law pressure distribution, pext ∝ z-κ, the total loss of causal connectivity occurs, when κ > 2 - the steepness which is expected to be quite common for many astrophysical environments. This conclusion does not seem to depend on the physical nature of jets - it applies both to relativistic and non-relativistic flows, both magnetically dominated and unmagnetized jets. In order to verify it, we have carried out numerical simulations of moderately magnetized and moderately relativistic jets. The results give strong support to our hypothesis and provide with valuable insights. In particular, we find that the z-pinched inner cores of magnetic jets expand slower than their envelopes and become susceptible to instabilities even when the whole jet is stable. This may result in local dissipation and emission without global disintegration of the flow. Cosmic jets may become globally unstable when they enter flat sections of external atmospheres. We propose that the Fanaroff-Riley (FR) morphological division of extragalactic radio sources into two classes is related to this issue. In particular, we argue that the low power FR-I jets become reconfined, causally connected and globally unstable on the scale of galactic X-ray coronas, whereas more powerful FR-II jets reconfine much further out, already on the scale of radio lobes and remain largely intact until they terminate at hotspots. Using this idea, we derived the relationship between the critical jet power and the optical luminosity of the host

  20. Integrating Internships with Professional Study in Pharmacy Education in Finland.

    PubMed

    Pitkä, Katja; Löfhjelm, Ulla; Passi, Sanna; Airaksinen, Marja

    2014-11-15

    Pharmacy internships are an important part of undergraduate pharmacy education worldwide. Internships in Finland are integrated into professional study during the second and third year, which has several pedagogic advantages, such as better understanding of the association between academic studies and pharmaceutical work-life during the studies, and enhanced self-reflection through the feedback from preceptors and peers during the internships. The objective of this paper is to describe the Finnish integrated internship using the pharmacy curriculum at the University of Helsinki as an example.

  1. Integrating Internships with Professional Study in Pharmacy Education in Finland

    PubMed Central

    Löfhjelm, Ulla; Passi, Sanna; Airaksinen, Marja

    2014-01-01

    Pharmacy internships are an important part of undergraduate pharmacy education worldwide. Internships in Finland are integrated into professional study during the second and third year, which has several pedagogic advantages, such as better understanding of the association between academic studies and pharmaceutical work-life during the studies, and enhanced self-reflection through the feedback from preceptors and peers during the internships. The objective of this paper is to describe the Finnish integrated internship using the pharmacy curriculum at the University of Helsinki as an example. PMID:26056411

  2. Satanic abuse, with focus on the situation in Finland.

    PubMed

    Segerberg, M

    1997-12-01

    This paper outlines Satanism and devil worship as practised in the Western countries and reviews the occurrence of Satanism in Finland. Two principal groups can be distinguished: the Satanists, mainly adults embracing the philosophical aspects of Satanism with no interest in hurting others, and the devil worshippers of Satanic cults, who accept teenagers into their group and whose activity may take violent forms. The main Satanic cult activity is vandalism, but other activities are now becoming more aggressive: causing bodily and mental harm to members and victims and luring young people into criminal activity. The views of the police and the medical community are discussed in this paper and current intervention is examined.

  3. Applying Causal Reasoning to Analyze Value Systems

    NASA Astrophysics Data System (ADS)

    Macedo, Patrícia; Camarinha-Matos, Luis M.

    Collaborative networked organizations are composed of heterogeneous and autonomous entities. Thus it is natural that each member has its own set of values and preferences, as a result, conflicts among partners might emerge due to some values misalignment. Therefore, tools to support the analysis of Value Systems in a collaborative context are relevant to improve the network management. Since a Value System reflects the set of values and preferences of an actor, which are cognitive issues, a cognitive approach based on qualitative causal maps is suggested. Qualitative inference methods are presented in order to assess the potential for conflicts among network members and the positive impact between members' Value Systems. The software tool developed, in order to support the proposed framework and the qualitative inference methods, is briefly presented.

  4. Equity Theory Ratios as Causal Schemas.

    PubMed

    Arvanitis, Alexios; Hantzi, Alexandra

    2016-01-01

    Equity theory approaches justice evaluations based on ratios of exchange inputs to exchange outcomes. Situations are evaluated as just if ratios are equal and unjust if unequal. We suggest that equity ratios serve a more fundamental cognitive function than the evaluation of justice. More particularly, we propose that they serve as causal schemas for exchange outcomes, that is, they assist in determining whether certain outcomes are caused by inputs of other people in the context of an exchange process. Equality or inequality of ratios in this sense points to an exchange process. Indeed, Study 1 shows that different exchange situations, such as disproportional or balanced proportional situations, create perceptions of give-and-take on the basis of equity ratios. Study 2 shows that perceptions of justice are based more on communicatively accepted rules of interaction than equity-based evaluations, thereby offering a distinction between an attribution and an evaluation cognitive process for exchange outcomes. PMID:27594846

  5. Causality constraints in conformal field theory

    NASA Astrophysics Data System (ADS)

    Hartman, Thomas; Jain, Sachin; Kundu, Sandipan

    2016-05-01

    Causality places nontrivial constraints on QFT in Lorentzian signature, for example fixing the signs of certain terms in the low energy Lagrangian. In d dimensional conformal field theory, we show how such constraints are encoded in crossing symmetry of Euclidean correlators, and derive analogous constraints directly from the conformal bootstrap (analytically). The bootstrap setup is a Lorentzian four-point function corresponding to propagation through a shockwave. Crossing symmetry fixes the signs of certain log terms that appear in the conformal block expansion, which constrains the interactions of low-lying operators. As an application, we use the bootstrap to rederive the well known sign constraint on the (∂ ϕ)4 coupling in effective field theory, from a dual CFT. We also find constraints on theories with higher spin conserved currents. Our analysis is restricted to scalar correlators, but we argue that similar methods should also impose nontrivial constraints on the interactions of spinning operators.

  6. Causal structure and electrodynamics on Finsler spacetimes

    NASA Astrophysics Data System (ADS)

    Pfeifer, Christian; Wohlfarth, Mattias N. R.

    2011-08-01

    We present a concise new definition of Finsler spacetimes that generalizes Lorentzian metric manifolds and provides consistent backgrounds for physics. Extending standard mathematical constructions known from Finsler spaces, we show that geometric objects like the Cartan nonlinear connection and its curvature are well defined almost everywhere on Finsler spacetimes, including their null structure. This allows us to describe the complete causal structure in terms of timelike and null curves; these are essential to model physical observers and the propagation of light. We prove that the timelike directions form an open convex cone with a null boundary, as is the case in Lorentzian geometry. Moreover, we develop action integrals for physical field theories on Finsler spacetimes, and tools to deduce the corresponding equations of motion. These are applied to construct a theory of electrodynamics that confirms the claimed propagation of light along Finsler null geodesics.

  7. "Causal reasoning" in rats: a reappraisal.

    PubMed

    Dwyer, D M; Starns, J; Honey, R C

    2009-10-01

    It has recently been argued that rats engage in causal reasoning and they do so in a way that is consistent with Bayes net theories (Blaisdell, Sawa, Leising & Waldmann, 2006). This argument was based upon the finding that the tendency of cues to elicit approach to a food-well was reduced when their presentation was contingent on lever pressing. There is, however, an alternative interpretation of the critical experimental findings that is based on the simple principle of response competition: wherein lever pressing interferes with the tendency to approach the food well. Here the authors replicated Experiments 1 and 2a of Blaisdell et al. (2006) and found reciprocal patterns of lever pressing and food well approach during the critical cues. These results lend direct support for an interpretation in terms of response competition while providing evidence contrary to Bayes net theories, and are readily interpreted within the theoretical framework provided by traditional associative learning theory.

  8. Lightweight causal and atomic group multicast

    NASA Technical Reports Server (NTRS)

    Birman, Kenneth P.; Schiper, Andre; Stephenson, Pat

    1991-01-01

    The ISIS toolkit is a distributed programming environment based on support for virtually synchronous process groups and group communication. A suite of protocols is presented to support this model. The approach revolves around a multicast primitive, called CBCAST, which implements a fault-tolerant, causally ordered message delivery. This primitive can be used directly or extended into a totally ordered multicast primitive, called ABCAST. It normally delivers messages immediately upon reception, and imposes a space overhead proportional to the size of the groups to which the sender belongs, usually a small number. It is concluded that process groups and group communication can achieve performance and scaling comparable to that of a raw message transport layer. This finding contradicts the widespread concern that this style of distributed computing may be unacceptably costly.

  9. Equity Theory Ratios as Causal Schemas.

    PubMed

    Arvanitis, Alexios; Hantzi, Alexandra

    2016-01-01

    Equity theory approaches justice evaluations based on ratios of exchange inputs to exchange outcomes. Situations are evaluated as just if ratios are equal and unjust if unequal. We suggest that equity ratios serve a more fundamental cognitive function than the evaluation of justice. More particularly, we propose that they serve as causal schemas for exchange outcomes, that is, they assist in determining whether certain outcomes are caused by inputs of other people in the context of an exchange process. Equality or inequality of ratios in this sense points to an exchange process. Indeed, Study 1 shows that different exchange situations, such as disproportional or balanced proportional situations, create perceptions of give-and-take on the basis of equity ratios. Study 2 shows that perceptions of justice are based more on communicatively accepted rules of interaction than equity-based evaluations, thereby offering a distinction between an attribution and an evaluation cognitive process for exchange outcomes.

  10. Macroscopically local correlations can violate information causality.

    PubMed

    Cavalcanti, Daniel; Salles, Alejo; Scarani, Valerio

    2010-01-01

    Although quantum mechanics is a very successful theory, its foundations are still a subject of intense debate. One of the main problems is that quantum mechanics is based on abstract mathematical axioms, rather than on physical principles. Quantum information theory has recently provided new ideas from which one could obtain physical axioms constraining the resulting statistics one can obtain in experiments. Information causality (IC) and macroscopic locality (ML) are two principles recently proposed to solve this problem. However, none of them were proven to define the set of correlations one can observe. In this study, we show an extension of IC and study its consequences. It is shown that the two above-mentioned principles are inequivalent: if the correlations allowed by nature were the ones satisfying ML, IC would be violated. This gives more confidence in IC as a physical principle, defining the possible correlation allowed by nature. PMID:21266986

  11. Equity Theory Ratios as Causal Schemas

    PubMed Central

    Arvanitis, Alexios; Hantzi, Alexandra

    2016-01-01

    Equity theory approaches justice evaluations based on ratios of exchange inputs to exchange outcomes. Situations are evaluated as just if ratios are equal and unjust if unequal. We suggest that equity ratios serve a more fundamental cognitive function than the evaluation of justice. More particularly, we propose that they serve as causal schemas for exchange outcomes, that is, they assist in determining whether certain outcomes are caused by inputs of other people in the context of an exchange process. Equality or inequality of ratios in this sense points to an exchange process. Indeed, Study 1 shows that different exchange situations, such as disproportional or balanced proportional situations, create perceptions of give-and-take on the basis of equity ratios. Study 2 shows that perceptions of justice are based more on communicatively accepted rules of interaction than equity-based evaluations, thereby offering a distinction between an attribution and an evaluation cognitive process for exchange outcomes.

  12. "Causal reasoning" in rats: a reappraisal.

    PubMed

    Dwyer, D M; Starns, J; Honey, R C

    2009-10-01

    It has recently been argued that rats engage in causal reasoning and they do so in a way that is consistent with Bayes net theories (Blaisdell, Sawa, Leising & Waldmann, 2006). This argument was based upon the finding that the tendency of cues to elicit approach to a food-well was reduced when their presentation was contingent on lever pressing. There is, however, an alternative interpretation of the critical experimental findings that is based on the simple principle of response competition: wherein lever pressing interferes with the tendency to approach the food well. Here the authors replicated Experiments 1 and 2a of Blaisdell et al. (2006) and found reciprocal patterns of lever pressing and food well approach during the critical cues. These results lend direct support for an interpretation in terms of response competition while providing evidence contrary to Bayes net theories, and are readily interpreted within the theoretical framework provided by traditional associative learning theory. PMID:19839709

  13. Equity Theory Ratios as Causal Schemas

    PubMed Central

    Arvanitis, Alexios; Hantzi, Alexandra

    2016-01-01

    Equity theory approaches justice evaluations based on ratios of exchange inputs to exchange outcomes. Situations are evaluated as just if ratios are equal and unjust if unequal. We suggest that equity ratios serve a more fundamental cognitive function than the evaluation of justice. More particularly, we propose that they serve as causal schemas for exchange outcomes, that is, they assist in determining whether certain outcomes are caused by inputs of other people in the context of an exchange process. Equality or inequality of ratios in this sense points to an exchange process. Indeed, Study 1 shows that different exchange situations, such as disproportional or balanced proportional situations, create perceptions of give-and-take on the basis of equity ratios. Study 2 shows that perceptions of justice are based more on communicatively accepted rules of interaction than equity-based evaluations, thereby offering a distinction between an attribution and an evaluation cognitive process for exchange outcomes. PMID:27594846

  14. European Linguistic Diversity--For Whom? The Cases of Finland and Sweden. Mercator Working Papers.

    ERIC Educational Resources Information Center

    Lainio, Jarmo

    Linguistic diversity in the Nordic countries has several dimensions. One main division is between what the majority group thinks it is about versus what the minority group thinks it is about. This paper examines the situation in Finland and Sweden, noting implications for linguistic diversity. Finland and Sweden contain five main varieties of…

  15. The Prevalence and Context of Family Violence against Children in Finland.

    ERIC Educational Resources Information Center

    Sariola, Heikki; Uutela, Antti

    1992-01-01

    Questionnaires completed by approximately 7,600 15 year olds in Finland indicated that mild family violence was reported by 72% of respondents and severe violence by 8%. Severe violence was most common in families with a stepfather. Overall, the frequency of violence toward children in Finland was significantly lower than in the United States. (DB)

  16. Globalization and Leadership and Management: A Comparative Analysis of Primary Schools in England and Finland

    ERIC Educational Resources Information Center

    Webb, Rosemary; Vulliamy, Graham; Sarja, Anneli; Hamalainen, Seppo

    2006-01-01

    This article analyses the impact of processes of globalization on both policy and practice in relation to primary school leadership and management in England and Finland. Data are drawn from case study research carried out from 1994-1996 in six schools in Finland and six schools in England and a follow-up study on teacher professionalism…

  17. A Recipe for Success: A Comparative View of Mathematics Teacher Education in Finland and Singapore

    ERIC Educational Resources Information Center

    Gísladóttir, Berglind; Jóhannsdóttir, Björg

    2010-01-01

    Finland and Singapore are both nations that have excelled in mathematics on international assessments, such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). Evidence of Finland's educational success emerged with the outcome of the first PISA study in 2000. Since…

  18. Finnish Lessons: What Can the World Learn from Educational Change in Finland? Series on School Reform

    ERIC Educational Resources Information Center

    Sahlberg, Pasi

    2011-01-01

    "Finnish Lessons" is a first-hand, comprehensive account of how Finland built a world-class education system during the past three decades. The author traces the evolution of education policies in Finland and highlights how they differ from the United States and other industrialized countries. He shows how rather than relying on competition,…

  19. Accountability and Evaluation: Decision-Making Structures and the Utilization of Evaluation in Finland.

    ERIC Educational Resources Information Center

    Laukkanen, Reijo

    1998-01-01

    Contextual changes in society in Finland, as elsewhere, have caused new demands for evaluation. This is particularly apparent in light of curriculum developments in compulsory education in Finland. The use of evaluations and the importance of planning evaluation for the benefit of the user are discussed. Professional, consumerist, and public…

  20. Comparative Study of Teaching Content in Teacher Education Programmes in Canada, Denmark, Finland and Singapore

    ERIC Educational Resources Information Center

    Rasmussen, Jens; Bayer, Martin

    2014-01-01

    This article presents the results of a comparative study of the content in selected teacher education programmes for primary and lower secondary teachers in Canada, Denmark, Finland and Singapore. First and foremost, the study is a comparison between teacher education programmes in, on the one hand, Canada, Finland and Singapore, all of which…