Walking Flexibility after Hemispherectomy: Split-Belt Treadmill Adaptation and Feedback Control

ERIC Educational Resources Information Center

Choi, Julia T.; Vining, Eileen P. G.; Reisman, Darcy S.; Bastian, Amy J.

2009-01-01

Walking flexibility depends on use of feedback or reactive control to respond to unexpected changes in the environment, and the ability to adapt feedforward or predictive control for sustained alterations. Recent work has demonstrated that cerebellar damage impairs feedforward adaptation, but not feedback control, during human split-belt treadmill…

Self-adaptive relevance feedback based on multilevel image content analysis

NASA Astrophysics Data System (ADS)

Gao, Yongying; Zhang, Yujin; Fu, Yu

2001-01-01

In current content-based image retrieval systems, it is generally accepted that obtaining high-level image features is a key to improve the querying. Among the related techniques, relevance feedback has become a hot research aspect because it combines the information from the user to refine the querying results. In practice, many methods have been proposed to achieve the goal of relevance feedback. In this paper, a new scheme for relevance feedback is proposed. Unlike previous methods for relevance feedback, our scheme provides a self-adaptive operation. First, based on multi- level image content analysis, the relevant images from the user could be automatically analyzed in different levels and the querying could be modified in terms of different analysis results. Secondly, to make it more convenient to the user, the procedure of relevance feedback could be led with memory or without memory. To test the performance of the proposed method, a practical semantic-based image retrieval system has been established, and the querying results gained by our self-adaptive relevance feedback are given.

Self-adaptive relevance feedback based on multilevel image content analysis

NASA Astrophysics Data System (ADS)

Gao, Yongying; Zhang, Yujin; Fu, Yu

2000-12-01

In current content-based image retrieval systems, it is generally accepted that obtaining high-level image features is a key to improve the querying. Among the related techniques, relevance feedback has become a hot research aspect because it combines the information from the user to refine the querying results. In practice, many methods have been proposed to achieve the goal of relevance feedback. In this paper, a new scheme for relevance feedback is proposed. Unlike previous methods for relevance feedback, our scheme provides a self-adaptive operation. First, based on multi- level image content analysis, the relevant images from the user could be automatically analyzed in different levels and the querying could be modified in terms of different analysis results. Secondly, to make it more convenient to the user, the procedure of relevance feedback could be led with memory or without memory. To test the performance of the proposed method, a practical semantic-based image retrieval system has been established, and the querying results gained by our self-adaptive relevance feedback are given.

Utilizing feedback in adaptive SAR ATR systems

NASA Astrophysics Data System (ADS)

Horsfield, Owen; Blacknell, David

2009-05-01

Existing SAR ATR systems are usually trained off-line with samples of target imagery or CAD models, prior to conducting a mission. If the training data is not representative of mission conditions, then poor performance may result. In addition, it is difficult to acquire suitable training data for the many target types of interest. The Adaptive SAR ATR Problem Set (AdaptSAPS) program provides a MATLAB framework and image database for developing systems that adapt to mission conditions, meaning less reliance on accurate training data. A key function of an adaptive system is the ability to utilise truth feedback to improve performance, and it is this feature which AdaptSAPS is intended to exploit. This paper presents a new method for SAR ATR that does not use training data, based on supervised learning. This is achieved by using feature-based classification, and several new shadow features have been developed for this purpose. These features allow discrimination of vehicles from clutter, and classification of vehicles into two classes: targets, comprising military combat types, and non-targets, comprising bulldozers and trucks. The performance of the system is assessed using three baseline missions provided with AdaptSAPS, as well as three additional missions. All performance metrics indicate a distinct learning trend over the course of a mission, with most third and fourth quartile performance levels exceeding 85% correct classification. It has been demonstrated that these performance levels can be maintained even when truth feedback rates are reduced by up to 55% over the course of a mission.

Visuomotor adaptation needs a validation of prediction error by feedback error

PubMed Central

Gaveau, Valérie; Prablanc, Claude; Laurent, Damien; Rossetti, Yves; Priot, Anne-Emmanuelle

2014-01-01

The processes underlying short-term plasticity induced by visuomotor adaptation to a shifted visual field are still debated. Two main sources of error can induce motor adaptation: reaching feedback errors, which correspond to visually perceived discrepancies between hand and target positions, and errors between predicted and actual visual reafferences of the moving hand. These two sources of error are closely intertwined and difficult to disentangle, as both the target and the reaching limb are simultaneously visible. Accordingly, the goal of the present study was to clarify the relative contributions of these two types of errors during a pointing task under prism-displaced vision. In “terminal feedback error” condition, viewing of their hand by subjects was allowed only at movement end, simultaneously with viewing of the target. In “movement prediction error” condition, viewing of the hand was limited to movement duration, in the absence of any visual target, and error signals arose solely from comparisons between predicted and actual reafferences of the hand. In order to prevent intentional corrections of errors, a subthreshold, progressive stepwise increase in prism deviation was used, so that subjects remained unaware of the visual deviation applied in both conditions. An adaptive aftereffect was observed in the “terminal feedback error” condition only. As far as subjects remained unaware of the optical deviation and self-assigned pointing errors, prediction error alone was insufficient to induce adaptation. These results indicate a critical role of hand-to-target feedback error signals in visuomotor adaptation; consistent with recent neurophysiological findings, they suggest that a combination of feedback and prediction error signals is necessary for eliciting aftereffects. They also suggest that feedback error updates the prediction of reafferences when a visual perturbation is introduced gradually and cognitive factors are eliminated or strongly

Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems

PubMed Central

Xia, Feng; Ma, Longhua; Peng, Chen; Sun, Youxian; Dong, Jinxiang

2008-01-01

There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS) scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting cross-layer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An event-driven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN. PMID:27879934

Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks.

PubMed

Zou, An-Min; Dev Kumar, Krishna; Hou, Zeng-Guang

2010-09-01

This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.

Adaptive Fuzzy Control for Nonstrict Feedback Systems With Unmodeled Dynamics and Fuzzy Dead Zone via Output Feedback.

PubMed

Wang, Lijie; Li, Hongyi; Zhou, Qi; Lu, Renquan

2017-09-01

This paper investigates the problem of observer-based adaptive fuzzy control for a category of nonstrict feedback systems subject to both unmodeled dynamics and fuzzy dead zone. Through constructing a fuzzy state observer and introducing a center of gravity method, unmeasurable states are estimated and the fuzzy dead zone is defuzzified, respectively. By employing fuzzy logic systems to identify the unknown functions. And combining small-gain approach with adaptive backstepping control technique, a novel adaptive fuzzy output feedback control strategy is developed, which ensures that all signals involved are semi-globally uniformly bounded. Simulation results are given to demonstrate the effectiveness of the presented method.

Adaptive feedback synchronization of a unified chaotic system

NASA Astrophysics Data System (ADS)

Lu, Junan; Wu, Xiaoqun; Han, Xiuping; Lü, Jinhu

2004-08-01

This Letter further improves and extends the work of Wang et al. [Phys. Lett. A 312 (2003) 34]. In detailed, the linear feedback synchronization and adaptive feedback synchronization with only one controller for a unified chaotic system are discussed here. It is noticed that this unified system contains the noted Lorenz and Chen systems. Two chaotic synchronization theorems are attained. Also, numerical simulations are given to show the effectiveness of these methods.

Introduction of new technologies and decision making processes: a framework to adapt a Local Health Technology Decision Support Program for other local settings.

PubMed

Poulin, Paule; Austen, Lea; Scott, Catherine M; Poulin, Michelle; Gall, Nadine; Seidel, Judy; Lafrenière, René

2013-01-01

Introducing new health technologies, including medical devices, into a local setting in a safe, effective, and transparent manner is a complex process, involving many disciplines and players within an organization. Decision making should be systematic, consistent, and transparent. It should involve translating and integrating scientific evidence, such as health technology assessment (HTA) reports, with context-sensitive evidence to develop recommendations on whether and under what conditions a new technology will be introduced. However, the development of a program to support such decision making can require considerable time and resources. An alternative is to adapt a preexisting program to the new setting. We describe a framework for adapting the Local HTA Decision Support Program, originally developed by the Department of Surgery and Surgical Services (Calgary, AB, Canada), for use by other departments. The framework consists of six steps: 1) development of a program review and adaptation manual, 2) education and readiness assessment of interested departments, 3) evaluation of the program by individual departments, 4) joint evaluation via retreats, 5) synthesis of feedback and program revision, and 6) evaluation of the adaptation process. Nine departments revised the Local HTA Decision Support Program and expressed strong satisfaction with the adaptation process. Key elements for success were identified. Adaptation of a preexisting program may reduce duplication of effort, save resources, raise the health care providers' awareness of HTA, and foster constructive stakeholder engagement, which enhances the legitimacy of evidence-informed recommendations for introducing new health technologies. We encourage others to use this framework for program adaptation and to report their experiences.

Decision feedback loop for tracking a polyphase modulated carrier

NASA Technical Reports Server (NTRS)

Simon, M. K. (Inventor)

1974-01-01

A multiple phase modulated carrier tracking loop for use in a frequency shift keying system is described in which carrier tracking efficiency is improved by making use of the decision signals made on the data phase transmitted in each T-second interval. The decision signal is used to produce a pair of decision-feedback quadrature signals for enhancing the loop's performance in developing a loop phase error signal.

Effect of feedback mode and task difficulty on quality of timing decisions in a zero-sum game.

PubMed

Tikuisis, Peter; Vartanian, Oshin; Mandel, David R

2014-09-01

The objective was to investigate the interaction between the mode of performance outcome feedback and task difficulty on timing decisions (i.e., when to act). Feedback is widely acknowledged to affect task performance. However, the extent to which feedback display mode and its impact on timing decisions is moderated by task difficulty remains largely unknown. Participants repeatedly engaged a zero-sum game involving silent duels with a computerized opponent and were given visual performance feedback after each engagement. They were sequentially tested on three different levels of task difficulty (low, intermediate, and high) in counterbalanced order. Half received relatively simple "inside view" binary outcome feedback, and the other half received complex "outside view" hit rate probability feedback. The key dependent variables were response time (i.e., time taken to make a decision) and survival outcome. When task difficulty was low to moderate, participants were more likely to learn and perform better from hit rate probability feedback than binary outcome feedback. However, better performance with hit rate feedback exacted a higher cognitive cost manifested by higher decision response time. The beneficial effect of hit rate probability feedback on timing decisions is partially moderated by task difficulty. Performance feedback mode should be judiciously chosen in relation to task difficulty for optimal performance in tasks involving timing decisions.

Adaptive disengagement buffers self-esteem from negative social feedback.

PubMed

Leitner, Jordan B; Hehman, Eric; Deegan, Matthew P; Jones, James M

2014-11-01

The degree to which self-esteem hinges on feedback in a domain is known as a contingency of self-worth, or engagement. Although previous research has conceptualized engagement as stable, it would be advantageous for individuals to dynamically regulate engagement. The current research examined whether the tendency to disengage from negative feedback accounts for variability in self-esteem. We created the Adaptive Disengagement Scale (ADS) to capture individual differences in the tendency to disengage self-esteem from negative outcomes. Results demonstrated that the ADS is reliable and valid (Studies 1 and 2). Furthermore, in response to negative social feedback, higher scores on the ADS predicted greater state self-esteem (Study 3), and this relationship was mediated by disengagement (Study 4). These findings demonstrate that adaptive disengagement protects self-esteem from negative outcomes and that the ADS is a valid measure of individual differences in the implementation of this process. © 2014 by the Society for Personality and Social Psychology, Inc.

Public Health Climate Change Adaptation Planning Using Stakeholder Feedback.

PubMed

Eidson, Millicent; Clancy, Kathleen A; Birkhead, Guthrie S

2016-01-01

Public health climate change adaptation planning is an urgent priority requiring stakeholder feedback. The 10 Essential Public Health Services can be applied to adaptation activities. To develop a state health department climate and health adaptation plan as informed by stakeholder feedback. With Centers for Disease Control and Prevention (CDC) funding, the New York State Department of Health (NYSDOH) implemented a 2010-2013 climate and health planning process, including 7 surveys on perceptions and adaptation priorities. New York State Department of Health program managers participated in initial (n = 41, denominator unknown) and follow-up (72.2%) needs assessments. Surveillance system information was collected from 98.1% of surveillance system managers. For adaptation prioritization surveys, participants included 75.4% of NYSDOH leaders; 60.3% of local health departments (LHDs); and 53.7% of other stakeholders representing environmental, governmental, health, community, policy, academic, and business organizations. Interviews were also completed with 38.9% of other stakeholders. In 2011 surveys, 34.1% of state health program directors believed that climate change would impact their program priorities. However, 84.6% of state health surveillance system managers provided ideas for using databases for climate and health monitoring/surveillance. In 2012 surveys, 46.5% of state health leaders agreed they had sufficient information about climate and health compared to 17.1% of LHDs (P = .0046) and 40.9% of other stakeholders (nonsignificant difference). Significantly fewer (P < .0001) LHDs (22.9%) were incorporating or considering incorporating climate and health into planning compared to state health leaders (55.8%) and other stakeholders (68.2%). Stakeholder groups agreed on the 4 highest priority adaptation categories including core public health activities such as surveillance, coordination/collaboration, education, and policy development. Feedback from diverse

Semantic richness effects in lexical decision: The role of feedback.

PubMed

Yap, Melvin J; Lim, Gail Y; Pexman, Penny M

2015-11-01

Across lexical processing tasks, it is well established that words with richer semantic representations are recognized faster. This suggests that the lexical system has access to meaning before a word is fully identified, and is consistent with a theoretical framework based on interactive and cascaded processing. Specifically, semantic richness effects are argued to be produced by feedback from semantic representations to lower-level representations. The present study explores the extent to which richness effects are mediated by feedback from lexical- to letter-level representations. In two lexical decision experiments, we examined the joint effects of stimulus quality and four semantic richness dimensions (imageability, number of features, semantic neighborhood density, semantic diversity). With the exception of semantic diversity, robust additive effects of stimulus quality and richness were observed for the targeted dimensions. Our results suggest that semantic feedback does not typically reach earlier levels of representation in lexical decision, and further reinforces the idea that task context modulates the processing dynamics of early word recognition processes.

Toward an Expanded Definition of Adaptive Decision Making.

ERIC Educational Resources Information Center

Phillips, Susan D.

1997-01-01

Uses the lifespan, life-space model to examine the definition of adaptive decision making. Reviews the existing definition of adaptive decision making as "rational" decision making and offers alternate perspectives on decision making with an emphasis on the implications of using the model. Makes suggestions for future theory, research,…

Rapid feedback responses correlate with reach adaptation and properties of novel upper limb loads.

PubMed

Cluff, Tyler; Scott, Stephen H

2013-10-02

A hallmark of voluntary motor control is the ability to adjust motor patterns for novel mechanical or visuomotor contexts. Recent work has also highlighted the importance of feedback for voluntary control, leading to the hypothesis that feedback responses should adapt when we learn new motor skills. We tested this prediction with a novel paradigm requiring that human subjects adapt to a viscous elbow load while reaching to three targets. Target 1 required combined shoulder and elbow motion, target 2 required only elbow motion, and target 3 (probe target) required shoulder but no elbow motion. This simple approach controlled muscle activity at the probe target before, during, and after the application of novel elbow loads. Our paradigm allowed us to perturb the elbow during reaching movements to the probe target and identify several key properties of adapted stretch responses. Adapted long-latency responses expressed (de-) adaptation similar to reaching errors observed when we introduced (removed) the elbow load. Moreover, reaching errors during learning correlated with changes in the long-latency response, showing subjects who adapted more to the elbow load displayed greater modulation of their stretch responses. These adapted responses were sensitive to the size and direction of the viscous training load. Our results highlight an important link between the adaptation of feedforward and feedback control and suggest a key part of motor adaptation is to adjust feedback responses to the requirements of novel motor skills.

Rodent models of adaptive decision making.

PubMed

Izquierdo, Alicia; Belcher, Annabelle M

2012-01-01

Adaptive decision making affords the animal the ability to respond quickly to changes in a dynamic environment: one in which attentional demands, cost or effort to procure the reward, and reward contingencies change frequently. The more flexible the organism is in adapting choice behavior, the more command and success the organism has in navigating its environment. Maladaptive decision making is at the heart of much neuropsychiatric disease, including addiction. Thus, a better understanding of the mechanisms that underlie normal, adaptive decision making helps achieve a better understanding of certain diseases that incorporate maladaptive decision making as a core feature. This chapter presents three general domains of methods that the experimenter can manipulate in animal decision-making tasks: attention, effort, and reward contingency. Here, we present detailed methods of rodent tasks frequently employed within these domains: the Attentional Set-Shift Task, Effortful T-maze Task, and Visual Discrimination Reversal Learning. These tasks all recruit regions within the frontal cortex and the striatum, and performance is heavily modulated by the neurotransmitter dopamine, making these assays highly valid measures in the study of psychostimulant addiction.

The absence or temporal offset of visual feedback does not influence adaptation to novel movement dynamics.

PubMed

McKenna, Erin; Bray, Laurence C Jayet; Zhou, Weiwei; Joiner, Wilsaan M

2017-10-01

Delays in transmitting and processing sensory information require correctly associating delayed feedback to issued motor commands for accurate error compensation. The flexibility of this alignment between motor signals and feedback has been demonstrated for movement recalibration to visual manipulations, but the alignment dependence for adapting movement dynamics is largely unknown. Here we examined the effect of visual feedback manipulations on force-field adaptation. Three subject groups used a manipulandum while experiencing a lag in the corresponding cursor motion (0, 75, or 150 ms). When the offset was applied at the start of the session (continuous condition), adaptation was not significantly different between groups. However, these similarities may be due to acclimation to the offset before motor adaptation. We tested additional subjects who experienced the same delays concurrent with the introduction of the perturbation (abrupt condition). In this case adaptation was statistically indistinguishable from the continuous condition, indicating that acclimation to feedback delay was not a factor. In addition, end-point errors were not significantly different across the delay or onset conditions, but end-point correction (e.g., deceleration duration) was influenced by the temporal offset. As an additional control, we tested a group of subjects who performed without visual feedback and found comparable movement adaptation results. These results suggest that visual feedback manipulation (absence or temporal misalignment) does not affect adaptation to novel dynamics, independent of both acclimation and perceptual awareness. These findings could have implications for modeling how the motor system adjusts to errors despite concurrent delays in sensory feedback information. NEW & NOTEWORTHY A temporal offset between movement and distorted visual feedback (e.g., visuomotor rotation) influences the subsequent motor recalibration, but the effects of this offset for

Feedback and feedforward adaptation to visuomotor delay during reaching and slicing movements.

PubMed

Botzer, Lior; Karniel, Amir

2013-07-01

It has been suggested that the brain and in particular the cerebellum and motor cortex adapt to represent the environment during reaching movements under various visuomotor perturbations. It is well known that significant delay is present in neural conductance and processing; however, the possible representation of delay and adaptation to delayed visual feedback has been largely overlooked. Here we investigated the control of reaching movements in human subjects during an imposed visuomotor delay in a virtual reality environment. In the first experiment, when visual feedback was unexpectedly delayed, the hand movement overshot the end-point target, indicating a vision-based feedback control. Over the ensuing trials, movements gradually adapted and became accurate. When the delay was removed unexpectedly, movements systematically undershot the target, demonstrating that adaptation occurred within the vision-based feedback control mechanism. In a second experiment designed to broaden our understanding of the underlying mechanisms, we revealed similar after-effects for rhythmic reversal (out-and-back) movements. We present a computational model accounting for these results based on two adapted forward models, each tuned for a specific modality delay (proprioception or vision), and a third feedforward controller. The computational model, along with the experimental results, refutes delay representation in a pure forward vision-based predictor and suggests that adaptation occurred in the forward vision-based predictor, and concurrently in the state-based feedforward controller. Understanding how the brain compensates for conductance and processing delays is essential for understanding certain impairments concerning these neural delays as well as for the development of brain-machine interfaces. © 2013 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

Eco-evolutionary feedbacks, adaptive dynamics and evolutionary rescue theory

PubMed Central

Ferriere, Regis; Legendre, Stéphane

2013-01-01

Adaptive dynamics theory has been devised to account for feedbacks between ecological and evolutionary processes. Doing so opens new dimensions to and raises new challenges about evolutionary rescue. Adaptive dynamics theory predicts that successive trait substitutions driven by eco-evolutionary feedbacks can gradually erode population size or growth rate, thus potentially raising the extinction risk. Even a single trait substitution can suffice to degrade population viability drastically at once and cause ‘evolutionary suicide’. In a changing environment, a population may track a viable evolutionary attractor that leads to evolutionary suicide, a phenomenon called ‘evolutionary trapping’. Evolutionary trapping and suicide are commonly observed in adaptive dynamics models in which the smooth variation of traits causes catastrophic changes in ecological state. In the face of trapping and suicide, evolutionary rescue requires that the population overcome evolutionary threats generated by the adaptive process itself. Evolutionary repellors play an important role in determining how variation in environmental conditions correlates with the occurrence of evolutionary trapping and suicide, and what evolutionary pathways rescue may follow. In contrast with standard predictions of evolutionary rescue theory, low genetic variation may attenuate the threat of evolutionary suicide and small population sizes may facilitate escape from evolutionary traps. PMID:23209163

Entropy-functional-based online adaptive decision fusion framework with application to wildfire detection in video.

PubMed

Gunay, Osman; Toreyin, Behçet Ugur; Kose, Kivanc; Cetin, A Enis

2012-05-01

In this paper, an entropy-functional-based online adaptive decision fusion (EADF) framework is developed for image analysis and computer vision applications. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. It is assumed that there is an oracle, who is usually a human operator, providing feedback to the decision fusion method. A video-based wildfire detection system was developed to evaluate the performance of the decision fusion algorithm. In this case, image data arrive sequentially, and the oracle is the security guard of the forest lookout tower, verifying the decision of the combined algorithm. The simulation results are presented.

Behavioral assessment of adaptive feedback equalization in a digital hearing aid.

PubMed

French-St George, M; Wood, D J; Engebretson, A M

1993-01-01

An evaluation was made of the efficacy of a digital feedback equalization algorithm employed by the Central Institute for the Deaf Wearable Adaptive Digital Hearing Aid. Three questions were addressed: 1) Does acoustic feedback limit gain adjustments made by hearing aid users? 2) Does feedback equalization permit users with hearing-impairment to select more gain without feedback? and, 3) If more gain is used when feedback equalization is active, does word identification performance improve? Nine subjects with hearing impairment participated in the study. Results suggest that listeners with hearing impairment are indeed limited by acoustic feedback when listening to soft speech (55 dB A) in quiet. The average listener used an additional 4 dB gain when feedback equalization was active. This additional gain resulted in an average 10 rationalized arcsine units (RAU) improvement in word identification score.

Myopic Regret Avoidance: Feedback Avoidance and Learning in Repeated Decision Making

ERIC Educational Resources Information Center

Reb, Jochen; Connolly, Terry

2009-01-01

Decision makers can become trapped by "myopic regret avoidance" in which rejecting feedback to avoid short-term "outcome regret" (regret associated with counterfactual outcome comparisons) leads to reduced learning and greater long-term regret over continuing poor decisions. In a series of laboratory experiments involving repeated choices among…

Students' Perceived Usefulness of Formative Feedback for a Computer-Adaptive Test

ERIC Educational Resources Information Center

Lilley, Mariana; Barker, Trevor

2007-01-01

In this paper we report on research related to the provision of automated feedback based on a computer adaptive test (CAT), used in formative assessment. A cohort of 76 second year university undergraduates took part in a formative assessment with a CAT and were provided with automated feedback on their performance. A sample of students responded…

An Examination of Students' Adaptation, Aggression, and Apprehension Traits with Their Instructional Feedback Orientations

ERIC Educational Resources Information Center

Malachowski, Colleen C.; Martin, Matthew M.; Vallade, Jessalyn I.

2013-01-01

Feedback orientations refer to students' perceptions of instructional feedback utility, retention, sensitivity, and confidentiality. In this paper, we report three studies that investigated the relationships among feedback orientations and communication traits. Specifically, we examined the associations among communication adaptation traits (Study…

Model-Based Adaptive Event-Triggered Control of Strict-Feedback Nonlinear Systems.

PubMed

Li, Yuan-Xin; Yang, Guang-Hong

2018-04-01

This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated. A positive lower bound on the minimum intersample time is guaranteed to avoid accumulation point. The closed-loop stability of the resulting nonlinear impulsive dynamical system is rigorously proved via Lyapunov analysis under an adaptive event sampling condition. In comparing with the traditional adaptive backstepping design with a fixed sample period, the event-triggered method samples the state and updates the NN weights only when it is necessary. Therefore, the number of transmissions can be significantly reduced. Finally, two simulation examples are presented to show the effectiveness of the proposed control method.

Output Feedback Adaptive Control of Non-Minimum Phase Systems Using Optimal Control Modification

NASA Technical Reports Server (NTRS)

Nguyen, Nhan; Hashemi, Kelley E.; Yucelen, Tansel; Arabi, Ehsan

2018-01-01

This paper describes output feedback adaptive control approaches for non-minimum phase SISO systems with relative degree 1 and non-strictly positive real (SPR) MIMO systems with uniform relative degree 1 using the optimal control modification method. It is well-known that the standard model-reference adaptive control (MRAC) cannot be used to control non-SPR plants to track an ideal SPR reference model. Due to the ideal property of asymptotic tracking, MRAC attempts an unstable pole-zero cancellation which results in unbounded signals for non-minimum phase SISO systems. The optimal control modification can be used to prevent the unstable pole-zero cancellation which results in a stable adaptation of non-minimum phase SISO systems. However, the tracking performance using this approach could suffer if the unstable zero is located far away from the imaginary axis. The tracking performance can be recovered by using an observer-based output feedback adaptive control approach which uses a Luenberger observer design to estimate the state information of the plant. Instead of explicitly specifying an ideal SPR reference model, the reference model is established from the linear quadratic optimal control to account for the non-minimum phase behavior of the plant. With this non-minimum phase reference model, the observer-based output feedback adaptive control can maintain stability as well as tracking performance. However, in the presence of the mismatch between the SPR reference model and the non-minimum phase plant, the standard MRAC results in unbounded signals, whereas a stable adaptation can be achieved with the optimal control modification. An application of output feedback adaptive control for a flexible wing aircraft illustrates the approaches.

Adaptive optimal stochastic state feedback control of resistive wall modes in tokamaks

NASA Astrophysics Data System (ADS)

Sun, Z.; Sen, A. K.; Longman, R. W.

2006-01-01

An adaptive optimal stochastic state feedback control is developed to stabilize the resistive wall mode (RWM) instability in tokamaks. The extended least-square method with exponential forgetting factor and covariance resetting is used to identify (experimentally determine) the time-varying stochastic system model. A Kalman filter is used to estimate the system states. The estimated system states are passed on to an optimal state feedback controller to construct control inputs. The Kalman filter and the optimal state feedback controller are periodically redesigned online based on the identified system model. This adaptive controller can stabilize the time-dependent RWM in a slowly evolving tokamak discharge. This is accomplished within a time delay of roughly four times the inverse of the growth rate for the time-invariant model used.

Adaptive Optimal Stochastic State Feedback Control of Resistive Wall Modes in Tokamaks

NASA Astrophysics Data System (ADS)

Sun, Z.; Sen, A. K.; Longman, R. W.

2007-06-01

An adaptive optimal stochastic state feedback control is developed to stabilize the resistive wall mode (RWM) instability in tokamaks. The extended least square method with exponential forgetting factor and covariance resetting is used to identify the time-varying stochastic system model. A Kalman filter is used to estimate the system states. The estimated system states are passed on to an optimal state feedback controller to construct control inputs. The Kalman filter and the optimal state feedback controller are periodically redesigned online based on the identified system model. This adaptive controller can stabilize the time dependent RWM in a slowly evolving tokamak discharge. This is accomplished within a time delay of roughly four times the inverse of the growth rate for the time-invariant model used.

Repeated causal decision making.

PubMed

Hagmayer, York; Meder, Björn

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 such situations and how they use their knowledge to adapt to changes in the decision context. Our studies show that decision makers' behavior is strongly contingent on their causal beliefs and that people exploit their causal knowledge to assess the consequences of changes in the decision problem. A high consistency between hypotheses about causal structure, causally expected values, and actual choices was observed. The experiments show that (a) existing causal hypotheses guide the interpretation of decision feedback, (b) consequences of decisions are used to revise existing causal beliefs, and (c) decision makers use the experienced feedback to induce a causal model of the choice situation even when they have no initial causal hypotheses, which (d) enables them to adapt their choices to changes of the decision problem. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

Performance improvement of optical wireless communication through fog with a decision feedback equalizer.

PubMed

Aharonovich, Marius; Arnon, Shlomi

2005-08-01

Optical wireless communication (OWC) systems use the atmosphere as a propagation medium. However, a common problem is that from time to time moderate cloud and fog emerge between the receiver and the transmitter. These adverse weather conditions impose temporal broadening and power loss on the optical signal, which reduces the digital signal-to-noise ratio (DSNR), produces significant intersymbol interference (ISI), and degrades the communication system's bit error rate (BER) and throughput. We propose and investigate the use of a combined adaptive bandwidth mechanism and decision feedback equalizer (DFE) to mitigate these atmospheric multipath effects. Based on theoretical analysis and simulations of DSNR penalties, BER, and optimum system bandwidths, we show that a DFE improves the outdoor OWC system immunity to ISI in foggy weather while maintaining high throughput and desired low BER.

Farmer Decision-Making for Climate Adaptation

NASA Astrophysics Data System (ADS)

Lubell, M.; Niles, M.; Salerno, J.

2015-12-01

This talk will provide an overview of several studies of how farmers make decisions about climate change adaptation and mitigation. A particular focus will be the "limiting factors hypothesis", which argues that farmers will respond to the climate variables that usually have the largest impact on their crop productivity. For example, the most limiting factor in California is usually water so how climate change affects water will be the largest drive of climate adaptation decisions. This basic idea is drawn from the broader theory of "psychological distance", which argue that human decisions are more attuned to ideas that are psychologically closer in space, time, or other factors. Empirical examples come from California, New Zealand, and Africa.

A Bayesian Account of Vocal Adaptation to Pitch-Shifted Auditory Feedback

PubMed Central

Hahnloser, Richard H. R.

2017-01-01

Motor systems are highly adaptive. Both birds and humans compensate for synthetically induced shifts in the pitch (fundamental frequency) of auditory feedback stemming from their vocalizations. Pitch-shift compensation is partial in the sense that large shifts lead to smaller relative compensatory adjustments of vocal pitch than small shifts. Also, compensation is larger in subjects with high motor variability. To formulate a mechanistic description of these findings, we adapt a Bayesian model of error relevance. We assume that vocal-auditory feedback loops in the brain cope optimally with known sensory and motor variability. Based on measurements of motor variability, optimal compensatory responses in our model provide accurate fits to published experimental data. Optimal compensation correctly predicts sensory acuity, which has been estimated in psychophysical experiments as just-noticeable pitch differences. Our model extends the utility of Bayesian approaches to adaptive vocal behaviors. PMID:28135267

Adaptive Strategy Selection in Decision Making.

DTIC Science & Technology

1986-07-31

information processing capabilities of a decision maker, given any " reasonable " time limit for making the decision. If use of a more normative rule...DECISION MAKING JOHN W. PAYNE DTIC DUKE UNIVERSITY L.CT E AUG 13 JAMES R. BETTMAN DUKE. UNIVERSITY ERIC J. JOHNSON CARNEGIE-MELLON UNIVERSITY...REPORT & PERIOD COVERED ADAPTIVE STRATEGY SELECTION IN DECISION MAKING Research 6. PERFORMING ORO. REPORT NUMSER 7. AUTNORfe) e. CONTRACT ON GRANT

Free-space optics mode-wavelength division multiplexing system using LG modes based on decision feedback equalization

NASA Astrophysics Data System (ADS)

Amphawan, Angela; Ghazi, Alaan; Al-dawoodi, Aras

2017-11-01

A free-space optics mode-wavelength division multiplexing (MWDM) system using Laguerre-Gaussian (LG) modes is designed using decision feedback equalization for controlling mode coupling and combating inter symbol interference so as to increase channel diversity. In this paper, a data rate of 24 Gbps is achieved for a FSO MWDM channel of 2.6 km in length using feedback equalization. Simulation results show significant improvement in eye diagrams and bit-error rates before and after decision feedback equalization.

Observer-Based Adaptive Fault-Tolerant Tracking Control of Nonlinear Nonstrict-Feedback Systems.

PubMed

Wu, Chengwei; Liu, Jianxing; Xiong, Yongyang; Wu, Ligang

2017-06-28

This paper studies an output-based adaptive fault-tolerant control problem for nonlinear systems with nonstrict-feedback form. Neural networks are utilized to identify the unknown nonlinear characteristics in the system. An observer and a general fault model are constructed to estimate the unavailable states and describe the fault, respectively. Adaptive parameters are constructed to overcome the difficulties in the design process for nonstrict-feedback systems. Meanwhile, dynamic surface control technique is introduced to avoid the problem of ''explosion of complexity''. Furthermore, based on adaptive backstepping control method, an output-based adaptive neural tracking control strategy is developed for the considered system against actuator fault, which can ensure that all the signals in the resulting closed-loop system are bounded, and the system output signal can be regulated to follow the response of the given reference signal with a small error. Finally, the simulation results are provided to validate the effectiveness of the control strategy proposed in this paper.

Age Differences in Adaptive Decision Making: The Role of Numeracy

ERIC Educational Resources Information Center

Chen, Yiwei; Wang, Jiaxi; Kirk, Robert M.; Pethtel, Olivia L.; Kiefner, Allison E.

2014-01-01

The primary purposes of the present study were to examine age differences in adaptive decision making and to evaluate the role of numeracy in mediating the relationship between age and adaptive decision making. Adaptive decision making was assessed by the Cups task (Levin, Weller, Pederson, & Harshman, 2007). Forty-six younger (18 to 24 years…

What is adaptive about adaptive decision making? A parallel constraint satisfaction account.

PubMed

Glöckner, Andreas; Hilbig, Benjamin E; Jekel, Marc

2014-12-01

There is broad consensus that human cognition is adaptive. However, the vital question of how exactly this adaptivity is achieved has remained largely open. Herein, we contrast two frameworks which account for adaptive decision making, namely broad and general single-mechanism accounts vs. multi-strategy accounts. We propose and fully specify a single-mechanism model for decision making based on parallel constraint satisfaction processes (PCS-DM) and contrast it theoretically and empirically against a multi-strategy account. To achieve sufficiently sensitive tests, we rely on a multiple-measure methodology including choice, reaction time, and confidence data as well as eye-tracking. Results show that manipulating the environmental structure produces clear adaptive shifts in choice patterns - as both frameworks would predict. However, results on the process level (reaction time, confidence), in information acquisition (eye-tracking), and from cross-predicting choice consistently corroborate single-mechanisms accounts in general, and the proposed parallel constraint satisfaction model for decision making in particular. Copyright © 2014 Elsevier B.V. All rights reserved.

Computer-supported feedback message tailoring: theory-informed adaptation of clinical audit and feedback for learning and behavior change.

PubMed

Landis-Lewis, Zach; Brehaut, Jamie C; Hochheiser, Harry; Douglas, Gerald P; Jacobson, Rebecca S

2015-01-21

Evidence shows that clinical audit and feedback can significantly improve compliance with desired practice, but it is unclear when and how it is effective. Audit and feedback is likely to be more effective when feedback messages can influence barriers to behavior change, but barriers to change differ across individual health-care providers, stemming from differences in providers' individual characteristics. The purpose of this article is to invite debate and direct research attention towards a novel audit and feedback component that could enable interventions to adapt to barriers to behavior change for individual health-care providers: computer-supported tailoring of feedback messages. We argue that, by leveraging available clinical data, theory-informed knowledge about behavior change, and the knowledge of clinical supervisors or peers who deliver feedback messages, a software application that supports feedback message tailoring could improve feedback message relevance for barriers to behavior change, thereby increasing the effectiveness of audit and feedback interventions. We describe a prototype system that supports the provision of tailored feedback messages by generating a menu of graphical and textual messages with associated descriptions of targeted barriers to behavior change. Supervisors could use the menu to select messages based on their awareness of each feedback recipient's specific barriers to behavior change. We anticipate that such a system, if designed appropriately, could guide supervisors towards giving more effective feedback for health-care providers. A foundation of evidence and knowledge in related health research domains supports the development of feedback message tailoring systems for clinical audit and feedback. Creating and evaluating computer-supported feedback tailoring tools is a promising approach to improving the effectiveness of clinical audit and feedback.

Bayesian Decision Support for Adaptive Lung Treatments

NASA Astrophysics Data System (ADS)

McShan, Daniel; Luo, Yi; Schipper, Matt; TenHaken, Randall

2014-03-01

Purpose: A Bayesian Decision Network will be demonstrated to provide clinical decision support for adaptive lung response-driven treatment management based on evidence that physiologic metrics may correlate better with individual patient response than traditional (population-based) dose and volume-based metrics. Further, there is evidence that information obtained during the course of radiation therapy may further improve response predictions. Methods: Clinical factors were gathered for 58 patients including planned mean lung dose, and the bio-markers IL-8 and TGF-β1 obtained prior to treatment and two weeks into treatment along with complication outcomes for these patients. A Bayesian Decision Network was constructed using Netica 5.0.2 from Norsys linking these clinical factors to obtain a prediction of radiation induced lung disese (RILD) complication. A decision node was added to the network to provide a plan adaption recommendation based on the trade-off between the RILD prediction and complexity of replanning. A utility node provides the weighting cost between the competing factors. Results: The decision node predictions were optimized against the data for the 58 cases. With this decision network solution, one can consider the decision result for a new patient with specific findings to obtain a recommendation to adaptively modify the originally planned treatment course. Conclusions: A Bayesian approach allows handling and propagating probabilistic data in a logical and principled manner. Decision networks provide the further ability to provide utility-based trade-offs, reflecting non-medical but practical cost/benefit analysis. The network demonstrated illustrates the basic concept, but many other factors may affect these decisions and work on building better models are being designed and tested. Acknowledgement: Supported by NIH-P01-CA59827

Adaptive Fuzzy Output Feedback Control for Switched Nonlinear Systems With Unmodeled Dynamics.

PubMed

Tong, Shaocheng; Li, Yongming

2017-02-01

This paper investigates a robust adaptive fuzzy control stabilization problem for a class of uncertain nonlinear systems with arbitrary switching signals that use an observer-based output feedback scheme. The considered switched nonlinear systems possess the unstructured uncertainties, unmodeled dynamics, and without requiring the states being available for measurement. A state observer which is independent of switching signals is designed to solve the problem of unmeasured states. Fuzzy logic systems are used to identify unknown lumped nonlinear functions so that the problem of unstructured uncertainties can be solved. By combining adaptive backstepping design principle and small-gain approach, a novel robust adaptive fuzzy output feedback stabilization control approach is developed. The stability of the closed-loop system is proved via the common Lyapunov function theory and small-gain theorem. Finally, the simulation results are given to demonstrate the validity and performance of the proposed control strategy.

Fuzzy Adaptive Decentralized Optimal Control for Strict Feedback Nonlinear Large-Scale Systems.

PubMed

Sun, Kangkang; Sui, Shuai; Tong, Shaocheng

2018-04-01

This paper considers the optimal decentralized fuzzy adaptive control design problem for a class of interconnected large-scale nonlinear systems in strict feedback form and with unknown nonlinear functions. The fuzzy logic systems are introduced to learn the unknown dynamics and cost functions, respectively, and a state estimator is developed. By applying the state estimator and the backstepping recursive design algorithm, a decentralized feedforward controller is established. By using the backstepping decentralized feedforward control scheme, the considered interconnected large-scale nonlinear system in strict feedback form is changed into an equivalent affine large-scale nonlinear system. Subsequently, an optimal decentralized fuzzy adaptive control scheme is constructed. The whole optimal decentralized fuzzy adaptive controller is composed of a decentralized feedforward control and an optimal decentralized control. It is proved that the developed optimal decentralized controller can ensure that all the variables of the control system are uniformly ultimately bounded, and the cost functions are the smallest. Two simulation examples are provided to illustrate the validity of the developed optimal decentralized fuzzy adaptive control scheme.

Reward Expectation Modulates Feedback-Related Negativity and EEG Spectra

PubMed Central

Cohen, Michael X; Elger, Christian E.; Ranganath, Charan

2007-01-01

The ability to evaluate outcomes of previous decisions is critical to adaptive decision-making. The feedback-related negativity (FRN) is an event-related potential (ERP) modulation that distinguishes losses from wins, but little is known about the effects of outcome probability on these ERP responses. Further, little is known about the frequency characteristics of feedback processing, for example, event-related oscillations and phase synchronizations. Here, we report an EEG experiment designed to address these issues. Subjects engaged in a probabilistic reinforcement learning task in which we manipulated, across blocks, the probability of winning and losing to each of two possible decision options. Behaviorally, all subjects quickly adapted their decision-making to maximize rewards. ERP analyses revealed that the probability of reward modulated neural responses to wins, but not to losses. This was seen both across blocks as well as within blocks, as learning progressed. Frequency decomposition via complex wavelets revealed that EEG responses to losses, compared to wins, were associated with enhanced power and phase coherence in the theta frequency band. As in the ERP analyses, power and phase coherence values following wins but not losses were modulated by reward probability. Some findings between ERP and frequency analyses diverged, suggesting that these analytic approaches provide complementary insights into neural processing. These findings suggest that the neural mechanisms of feedback processing may differ between wins and losses. PMID:17257860

Adaptive Inferential Feedback Partner Training for Depression: A Pilot Study

ERIC Educational Resources Information Center

Dobkin, Roseanne DeFronzo; Allen, Lesley A.; Alloy, Lauren B.; Menza, Matthew; Gara, Michael A.; Panzarella, Catherine

2007-01-01

Adaptive inferential feedback (AIF) partner training is a cognitive technique that teaches the friends and family members of depressed patients to respond to the patients' dysfunctional thoughts in a targeted manner. These dysfunctional attributions, which AIF addresses, are a common residual feature of depression amongst remitted patients, and…

Feedback and feedforward locomotor adaptations to ankle-foot load in people with incomplete spinal cord injury.

PubMed

Gordon, Keith E; Wu, Ming; Kahn, Jennifer H; Schmit, Brian D

2010-09-01

Humans with spinal cord injury (SCI) modulate locomotor output in response to limb load. Understanding the neural control mechanisms responsible for locomotor adaptation could provide a framework for selecting effective interventions. We quantified feedback and feedforward locomotor adaptations to limb load modulations in people with incomplete SCI. While subjects airstepped (stepping performed with kinematic assistance and 100% bodyweight support), a powered-orthosis created a dorisflexor torque during the "stance phase" of select steps producing highly controlled ankle-load perturbations. When given repetitive, stance phase ankle-load, the increase in hip extension work, 0.27 J/kg above baseline (no ankle-load airstepping), was greater than the response to ankle-load applied during a single step, 0.14 J/kg (P = 0.029). This finding suggests that, at the hip, subjects produced both feedforward and feedback locomotor modulations. We estimate that, at the hip, the locomotor response to repetitive ankle-load was modulated almost equally by ongoing feedback and feedforward adaptations. The majority of subjects also showed after-effects in hip kinetic patterns that lasted 3 min in response to repetitive loading, providing additional evidence of feedforward locomotor adaptations. The magnitude of the after-effect was proportional to the response to repetitive ankle-foot load (R(2) = 0.92). In contrast, increases in soleus EMG amplitude were not different during repetitive and single-step ankle-load exposure, suggesting that ankle locomotor modulations were predominately feedback-based. Although subjects made both feedback and feedforward locomotor adaptations to changes in ankle-load, between-subject variations suggest that walking function may be related to the ability to make feedforward adaptations.

Can conservation contracts co-exist with change? Payment for ecosystem services in the context of adaptive decision-making and sustainability.

PubMed

Hayes, Tanya; Murtinho, Felipe; Cárdenas Camacho, Luis Mario; Crespo, Patricio; McHugh, Sarah; Salmerón, David

2015-01-01

This paper considers the ability of payment for ecosystem services (PES) programs to operate in the context of dynamic and complex social-ecological systems. Drawing on the experiences of two different PES programs in Latin America, we examine how PES institutions fit with the tenets of adaptive decision-making for sustainable resource management. We identify how the program goals and the connection to the market influence the incentive structure, information gathering, learning and feedback processes, and the structure of decision-making rights, specifically the ability to make and modify resource-use rules. Although limited in their generalizability, findings from the two case studies suggest a tension between the contractual model of PES and adaptive decision-making in natural resource systems. PES programs are not inherently decentralized, flexible management tools, as PES contracts tend to restrict decision-making rights and offer minimal flexibility mechanisms to change resource-use practices over the duration of the contract period. Furthermore, PES design and flexibility is heavily dependent on the goals and mission of the buyer and the respective market. If PES is to facilitate sustainable resource management, greater attention is needed to assess how the institutional design of the PES contracts influence the motivation and capacity of participants and program officers alike to adaptively manage the respective resource systems.

Can Conservation Contracts Co-exist with Change? Payment for Ecosystem Services in the Context of Adaptive Decision-Making and Sustainability

NASA Astrophysics Data System (ADS)

Hayes, Tanya; Murtinho, Felipe; Cárdenas Camacho, Luis Mario; Crespo, Patricio; McHugh, Sarah; Salmerón, David

2015-01-01

This paper considers the ability of payment for ecosystem services (PES) programs to operate in the context of dynamic and complex social-ecological systems. Drawing on the experiences of two different PES programs in Latin America, we examine how PES institutions fit with the tenets of adaptive decision-making for sustainable resource management. We identify how the program goals and the connection to the market influence the incentive structure, information gathering, learning and feedback processes, and the structure of decision-making rights, specifically the ability to make and modify resource-use rules. Although limited in their generalizability, findings from the two case studies suggest a tension between the contractual model of PES and adaptive decision-making in natural resource systems. PES programs are not inherently decentralized, flexible management tools, as PES contracts tend to restrict decision-making rights and offer minimal flexibility mechanisms to change resource-use practices over the duration of the contract period. Furthermore, PES design and flexibility is heavily dependent on the goals and mission of the buyer and the respective market. If PES is to facilitate sustainable resource management, greater attention is needed to assess how the institutional design of the PES contracts influence the motivation and capacity of participants and program officers alike to adaptively manage the respective resource systems.

Adaptive Decision Aiding in Computer-Assisted Instruction: Adaptive Computerized Training System (ACTS).

ERIC Educational Resources Information Center

Hopf-Weichel, Rosemarie; And Others

This report describes results of the first year of a three-year program to develop and evaluate a new Adaptive Computerized Training System (ACTS) for electronics maintenance training. (ACTS incorporates an adaptive computer program that learns the student's diagnostic and decision value structure, compares it to that of an expert, and adapts the…

Adaptive Fuzzy Bounded Control for Consensus of Multiple Strict-Feedback Nonlinear Systems.

PubMed

Wang, Wei; Tong, Shaocheng

2018-02-01

This paper studies the adaptive fuzzy bounded control problem for leader-follower multiagent systems, where each follower is modeled by the uncertain nonlinear strict-feedback system. Combining the fuzzy approximation with the dynamic surface control, an adaptive fuzzy control scheme is developed to guarantee the output consensus of all agents under directed communication topologies. Different from the existing results, the bounds of the control inputs are known as a priori, and they can be determined by the feedback control gains. To realize smooth and fast learning, a predictor is introduced to estimate each error surface, and the corresponding predictor error is employed to learn the optimal fuzzy parameter vector. It is proved that the developed adaptive fuzzy control scheme guarantees the uniformly ultimate boundedness of the closed-loop systems, and the tracking error converges to a small neighborhood of the origin. The simulation results and comparisons are provided to show the validity of the control strategy presented in this paper.

Information-reduced Carrier Synchronization of Iterative Decoded BPSK and QPSK using Soft Decision (Extrinsic) Feedback

NASA Technical Reports Server (NTRS)

Simon, Marvin; Valles, Esteban; Jones, Christopher

2008-01-01

This paper addresses the carrier-phase estimation problem under low SNR conditions as are typical of turbo- and LDPC-coded applications. In previous publications by the first author, closed-loop carrier synchronization schemes for error-correction coded BPSK and QPSK modulation were proposed that were based on feeding back hard data decisions at the input of the loop, the purpose being to remove the modulation prior to attempting to track the carrier phase as opposed to the more conventional decision-feedback schemes that incorporate such feedback inside the loop. In this paper, we consider an alternative approach wherein the extrinsic soft information from the iterative decoder of turbo or LDPC codes is instead used as the feedback.

Adaptive neural network decentralized backstepping output-feedback control for nonlinear large-scale systems with time delays.

PubMed

Tong, Shao Cheng; Li, Yong Ming; Zhang, Hua-Guang

2011-07-01

In this paper, two adaptive neural network (NN) decentralized output feedback control approaches are proposed for a class of uncertain nonlinear large-scale systems with immeasurable states and unknown time delays. Using NNs to approximate the unknown nonlinear functions, an NN state observer is designed to estimate the immeasurable states. By combining the adaptive backstepping technique with decentralized control design principle, an adaptive NN decentralized output feedback control approach is developed. In order to overcome the problem of "explosion of complexity" inherent in the proposed control approach, the dynamic surface control (DSC) technique is introduced into the first adaptive NN decentralized control scheme, and a simplified adaptive NN decentralized output feedback DSC approach is developed. It is proved that the two proposed control approaches can guarantee that all the signals of the closed-loop system are semi-globally uniformly ultimately bounded, and the observer errors and the tracking errors converge to a small neighborhood of the origin. Simulation results are provided to show the effectiveness of the proposed approaches.

Adaptive Neural Output Feedback Control for Nonstrict-Feedback Stochastic Nonlinear Systems With Unknown Backlash-Like Hysteresis and Unknown Control Directions.

PubMed

Yu, Zhaoxu; Li, Shugang; Yu, Zhaosheng; Li, Fangfei

2018-04-01

This paper investigates the problem of output feedback adaptive stabilization for a class of nonstrict-feedback stochastic nonlinear systems with both unknown backlashlike hysteresis and unknown control directions. A new linear state transformation is applied to the original system, and then, control design for the new system becomes feasible. By combining the neural network's (NN's) parameterization, variable separation technique, and Nussbaum gain function method, an input-driven observer-based adaptive NN control scheme, which involves only one parameter to be updated, is developed for such systems. All closed-loop signals are bounded in probability and the error signals remain semiglobally bounded in the fourth moment (or mean square). Finally, the effectiveness and the applicability of the proposed control design are verified by two simulation examples.

Observations to support adaptation: Principles, scales and decision-making

NASA Astrophysics Data System (ADS)

Pulwarty, R. S.

2012-12-01

As has been long noted, a comprehensive, coordinated observing system is the backbone of any Earth information system. Demands are increasingly placed on earth observation and prediction systems and attendant services to address the needs of economically and environmentally vulnerable sectors and investments, including energy, water, human health, transportation, agriculture, fisheries, tourism, biodiversity, and national security. Climate services include building capacity to interpret information and recognize standards and limitations of data in the promotion of social and economic development in a changing climate. This includes improving the understanding of climate in the context of a variety of temporal and spatial scales (including the influence of decadal scale forcings and land surface feedbacks on seasonal forecast reliability). Climate data and information are central for developing decision options that are sensitive to climate-related uncertainties and the design of flexible adaptation pathways. Ideally monitoring should be action oriented to support climate risk assessment and adaptation including informing robust decision making to multiple risks over the long term. Based on the experience of global observations programs and empirical research we outline- Challenges in developing effective monitoring and climate information systems to support adaptation. The types of observations of critical importance needed for sector planning to enhance food, water and energy security, and to improve early warning for disaster risk reduction Observations needed for ecosystem-based adaptation including the identification of thresholds, maintenance of biological diversity and land degradation The benefits and limits of linking regional model output to local observations including analogs and verification for adaptation planning To support these goals a robust systems of integrated observations are needed to characterize the uncertainty surrounding emergent risks

Computerized Dynamic Adaptive Tests with Immediately Individualized Feedback for Primary School Mathematics Learning

ERIC Educational Resources Information Center

Wu, Huey-Min; Kuo, Bor-Chen; Wang, Su-Chen

2017-01-01

In this study, a computerized dynamic assessment test with both immediately individualized feedback and adaptively property was applied to Mathematics learning in primary school. For evaluating the effectiveness of the computerized dynamic adaptive test, the performances of three types of remedial instructions were compared by a pre-test/post-test…

Echolocating bats rely on audiovocal feedback to adapt sonar signal design.

PubMed

Luo, Jinhong; Moss, Cynthia F

2017-10-10

Many species of bat emit acoustic signals and use information carried by echoes reflecting from nearby objects to navigate and forage. It is widely documented that echolocating bats adjust the features of sonar calls in response to echo feedback; however, it remains unknown whether audiovocal feedback contributes to sonar call design. Audiovocal feedback refers to the monitoring of one's own vocalizations during call production and has been intensively studied in nonecholocating animals. Audiovocal feedback not only is a necessary component of vocal learning but also guides the control of the spectro-temporal structure of vocalizations. Here, we show that audiovocal feedback is directly involved in the echolocating bat's control of sonar call features. As big brown bats tracked targets from a stationary position, we played acoustic jamming signals, simulating calls of another bat, timed to selectively perturb audiovocal feedback or echo feedback. We found that the bats exhibited the largest call-frequency adjustments when the jamming signals occurred during vocal production. By contrast, bats did not show sonar call-frequency adjustments when the jamming signals coincided with the arrival of target echoes. Furthermore, bats rapidly adapted sonar call design in the first vocalization following the jamming signal, revealing a response latency in the range of 66 to 94 ms. Thus, bats, like songbirds and humans, rely on audiovocal feedback to structure sonar signal design.

Neuroticism and responsiveness to error feedback: adaptive self-regulation versus affective reactivity.

PubMed

Robinson, Michael D; Moeller, Sara K; Fetterman, Adam K

2010-10-01

Responsiveness to negative feedback has been seen as functional by those who emphasize the value of reflecting on such feedback in self-regulating problematic behaviors. On the other hand, the very same responsiveness has been viewed as dysfunctional by its link to punishment sensitivity and reactivity. The present 4 studies, involving 203 undergraduate participants, sought to reconcile such discrepant views in the context of the trait of neuroticism. In cognitive tasks, individuals were given error feedback when they made mistakes. It was found that greater tendencies to slow down following error feedback were associated with higher levels of accuracy at low levels of neuroticism but lower levels of accuracy at high levels of neuroticism. Individual differences in neuroticism thus appear crucial in understanding whether behavioral alterations following negative feedback reflect proactive versus reactive mechanisms and processes. Implications for understanding the processing basis of neuroticism and adaptive self-regulation are discussed.

Self-reported strategies in decisions under risk: role of feedback, reasoning abilities, executive functions, short-term-memory, and working memory.

PubMed

Schiebener, Johannes; Brand, Matthias

2015-11-01

In decisions under objective risk conditions information about the decision options' possible outcomes and the rules for outcomes' occurrence are provided. Thus, deciders can base decision-making strategies on probabilistic laws. In many laboratory decision-making tasks, choosing the option with the highest winning probability in all trials (=maximization strategy) is probabilistically regarded the most rational behavior. However, individuals often behave less optimal, especially in case the individuals have lower cognitive functions or in case no feedback about consequences is provided in the situation. It is still unclear which cognitive functions particularly predispose individuals for using successful strategies and which strategies profit from feedback. We investigated 195 individuals with two decision-making paradigms, the Game of Dice Task (GDT) (with and without feedback), and the Card Guessing Game. Thereafter, participants reported which strategies they had applied. Interaction effects (feedback × strategy), effect sizes, and uncorrected single group comparisons suggest that feedback in the GDT tended to be more beneficial to individuals reporting exploratory strategies (e.g., use intuition). In both tasks, the self-reported use of more principled and more rational strategies was accompanied by better decision-making performance and better performances in reasoning and executive functioning tasks. The strategy groups did not significantly differ in most short-term and working-memory tasks. Thus, particularly individual differences in reasoning and executive functions seem to predispose individuals toward particular decision-making strategies. Feedback seems to be useful for individuals who rather explore the decision-making situation instead of following a certain plan.

Sex-ratio control erodes sexual selection, revealing evolutionary feedback from adaptive plasticity.

PubMed

Fawcett, Tim W; Kuijper, Bram; Weissing, Franz J; Pen, Ido

2011-09-20

Female choice is a powerful selective force, driving the elaboration of conspicuous male ornaments. This process of sexual selection has profound implications for many life-history decisions, including sex allocation. For example, females with attractive partners should produce more sons, because these sons will inherit their father's attractiveness and enjoy high mating success, thereby yielding greater fitness returns than daughters. However, previous research has overlooked the fact that there is a reciprocal feedback from life-history strategies to sexual selection. Here, using a simple mathematical model, we show that if mothers adaptively control offspring sex in relation to their partner's attractiveness, sexual selection is weakened and male ornamentation declines. This weakening occurs because the ability to determine offspring sex reduces the fitness difference between females with attractive and unattractive partners. We use individual-based, evolutionary simulations to show that this result holds under more biologically realistic conditions. Sexual selection and sex allocation thus interact in a dynamic fashion: The evolution of conspicuous male ornaments favors sex-ratio adjustment, but this conditional strategy then undermines the very same process that generated it, eroding sexual selection. We predict that, all else being equal, the most elaborate sexual displays should be seen in species with little or no control over offspring sex. The feedback process we have described points to a more general evolutionary principle, in which a conditional strategy weakens directional selection on another trait by reducing fitness differences.

Decision feedback equalizer for holographic data storage.

PubMed

Kim, Kyuhwan; Kim, Seung Hun; Koo, Gyogwon; Seo, Min Seok; Kim, Sang Woo

2018-05-20

Holographic data storage (HDS) has attracted much attention as a next-generation storage medium. Because HDS suffers from two-dimensional (2D) inter-symbol interference (ISI), the partial-response maximum-likelihood (PRML) method has been studied to reduce 2D ISI. However, the PRML method has various drawbacks. To solve the problems, we propose a modified decision feedback equalizer (DFE) for HDS. To prevent the error propagation problem, which is a typical problem in DFEs, we also propose a reliability factor for HDS. Various simulations were executed to analyze the performance of the proposed methods. The proposed methods showed fast processing speed after training, superior bit error rate performance, and consistency.

Performance Feedback Utility in a Small Organization: Effects on Organizational Outcomes and Managerial Decision Processes.

ERIC Educational Resources Information Center

Florin-Thuma, Beth C.; Boudreau, John W.

1987-01-01

Investigated the frequent but previously untested assertion that utility analysis can improve communication and decision making about human resource management programs by examining a performance feedback intervention in a small fast-food store. Results suggest substantial payoffs from performance feedback, though the store's owner-managers had…

Adaptive Reception for Underwater Communications

DTIC Science & Technology

2011-06-01

Experimental results prove the effectiveness of the receiver. 14. SUBJECT TERMS Underwater acoustic communications, adaptive algorithms , Kalman filter...the update algorithm design and the value of the spatial diversity are addressed. In this research, an adaptive multichannel equalizer made up of a...for the time-varying nature of the channel is to use an Adaptive Decision Feedback Equalizer based on either the RLS or LMS algorithm . Although this

Adaptive receiver structures for asynchronous CDMA systems

NASA Astrophysics Data System (ADS)

Rapajic, Predrag B.; Vucetic, Branka S.

1994-05-01

Adaptive linear and decision feedback receiver structures for coherent demodulation in asynchronous code division multiple access (CDMA) systems are considered. It is assumed that the adaptive receiver has no knowledge of the signature waveforms and timing of other users. The receiver is trained by a known training sequence prior to data transmission and continuously adjusted by an adaptive algorithm during data transmission. The proposed linear receiver is as simple as a standard single-user detector receiver consisting of a matched filter with constant coefficients, but achieves essential advantages with respect to timing recovery, multiple access interference elimination, near/far effect, narrowband and frequency-selective fading interference suppression, and user privacy. An adaptive centralized decision feedback receiver has the same advantages of the linear receiver but, in addition, achieves a further improvement in multiple access interference cancellation at the expense of higher complexity. The proposed receiver structures are tested by simulation over a channel with multipath propagation, multiple access interference, narrowband interference, and additive white Gaussian noise.

Using the 360 degrees multisource feedback model to evaluate teaching and professionalism.

PubMed

Berk, Ronald A

2009-12-01

Student ratings have dominated as the primary and, frequently, only measure of teaching performance at colleges and universities for the past 50 years. Recently, there has been a trend toward augmenting those ratings with other data sources to broaden and deepen the evidence base. The 360 degrees multisource feedback (MSF) model used in management and industry for half a century and in clinical medicine for the last decade seemed like a best fit to evaluate teaching performance and professionalism. To adapt the 360 degrees MSF model to the assessment of teaching performance and professionalism of medical school faculty. The salient characteristics of the MSF models in industry and medicine were extracted from the literature. These characteristics along with 14 sources of evidence from eight possible raters, including students, self, peers, outside experts, mentors, alumni, employers, and administrators, based on the research in higher education were adapted to formative and summative decisions. Three 360 degrees MSF models were generated for three different decisions: (1) formative decisions and feedback about teaching improvement; (2) summative decisions and feedback for merit pay and contract renewal; and (3) formative decisions and feedback about professional behaviors in the academic setting. The characteristics of each model were listed. Finally, a top-10 list of the most persistent and, perhaps, intractable psychometric issues in executing these models was suggested to guide future research. The 360 degrees MSF model appears to be a useful framework for implementing a multisource evaluation of faculty teaching performance and professionalism in medical schools. This model can provide more accurate, reliable, fair, and equitable decisions than the one based on just a single source.

CSI feedback-based CS for underwater acoustic adaptive modulation OFDM system with channel prediction

NASA Astrophysics Data System (ADS)

Kuai, Xiao-yan; Sun, Hai-xin; Qi, Jie; Cheng, En; Xu, Xiao-ka; Guo, Yu-hui; Chen, You-gan

2014-06-01

In this paper, we investigate the performance of adaptive modulation (AM) orthogonal frequency division multiplexing (OFDM) system in underwater acoustic (UWA) communications. The aim is to solve the problem of large feedback overhead for channel state information (CSI) in every subcarrier. A novel CSI feedback scheme is proposed based on the theory of compressed sensing (CS). We propose a feedback from the receiver that only feedback the sparse channel parameters. Additionally, prediction of the channel state is proposed every several symbols to realize the AM in practice. We describe a linear channel prediction algorithm which is used in adaptive transmission. This system has been tested in the real underwater acoustic channel. The linear channel prediction makes the AM transmission techniques more feasible for acoustic channel communications. The simulation and experiment show that significant improvements can be obtained both in bit error rate (BER) and throughput in the AM scheme compared with the fixed Quadrature Phase Shift Keying (QPSK) modulation scheme. Moreover, the performance with standard CS outperforms the Discrete Cosine Transform (DCT) method.

Synchrony suppression in ensembles of coupled oscillators via adaptive vanishing feedback.

PubMed

Montaseri, Ghazal; Yazdanpanah, Mohammad Javad; Pikovsky, Arkady; Rosenblum, Michael

2013-09-01

Synchronization and emergence of a collective mode is a general phenomenon, frequently observed in ensembles of coupled self-sustained oscillators of various natures. In several circumstances, in particular in cases of neurological pathologies, this state of the active medium is undesirable. Destruction of this state by a specially designed stimulation is a challenge of high clinical relevance. Typically, the precise effect of an external action on the ensemble is unknown, since the microscopic description of the oscillators and their interactions are not available. We show that, desynchronization in case of a large degree of uncertainty about important features of the system is nevertheless possible; it can be achieved by virtue of a feedback loop with an additional adaptation of parameters. The adaptation also ensures desynchronization of ensembles with non-stationary, time-varying parameters. We perform the stability analysis of the feedback-controlled system and demonstrate efficient destruction of synchrony for several models, including those of spiking and bursting neurons.

Synchrony suppression in ensembles of coupled oscillators via adaptive vanishing feedback

NASA Astrophysics Data System (ADS)

Montaseri, Ghazal; Javad Yazdanpanah, Mohammad; Pikovsky, Arkady; Rosenblum, Michael

2013-09-01

Synchronization and emergence of a collective mode is a general phenomenon, frequently observed in ensembles of coupled self-sustained oscillators of various natures. In several circumstances, in particular in cases of neurological pathologies, this state of the active medium is undesirable. Destruction of this state by a specially designed stimulation is a challenge of high clinical relevance. Typically, the precise effect of an external action on the ensemble is unknown, since the microscopic description of the oscillators and their interactions are not available. We show that, desynchronization in case of a large degree of uncertainty about important features of the system is nevertheless possible; it can be achieved by virtue of a feedback loop with an additional adaptation of parameters. The adaptation also ensures desynchronization of ensembles with non-stationary, time-varying parameters. We perform the stability analysis of the feedback-controlled system and demonstrate efficient destruction of synchrony for several models, including those of spiking and bursting neurons.

Adaptive self-organization of Bali's ancient rice terraces.

PubMed

Lansing, J Stephen; Thurner, Stefan; Chung, Ning Ning; Coudurier-Curveur, Aurélie; Karakaş, Çağil; Fesenmyer, Kurt A; Chew, Lock Yue

2017-06-20

Spatial patterning often occurs in ecosystems as a result of a self-organizing process caused by feedback between organisms and the physical environment. Here, we show that the spatial patterns observable in centuries-old Balinese rice terraces are also created by feedback between farmers' decisions and the ecology of the paddies, which triggers a transition from local to global-scale control of water shortages and rice pests. We propose an evolutionary game, based on local farmers' decisions that predicts specific power laws in spatial patterning that are also seen in a multispectral image analysis of Balinese rice terraces. The model shows how feedbacks between human decisions and ecosystem processes can evolve toward an optimal state in which total harvests are maximized and the system approaches Pareto optimality. It helps explain how multiscale cooperation from the community to the watershed scale could persist for centuries, and why the disruption of this self-organizing system by the Green Revolution caused chaos in irrigation and devastating losses from pests. The model shows that adaptation in a coupled human-natural system can trigger self-organized criticality (SOC). In previous exogenously driven SOC models, adaptation plays no role, and no optimization occurs. In contrast, adaptive SOC is a self-organizing process where local adaptations drive the system toward local and global optima.

The Case for Absolute Ligand Discrimination: Modeling Information Processing and Decision by Immune T Cells

NASA Astrophysics Data System (ADS)

François, Paul; Altan-Bonnet, Grégoire

2016-03-01

Some cells have to take decision based on the quality of surroundings ligands, almost irrespective of their quantity, a problem we name "absolute discrimination". An example of absolute discrimination is recognition of not-self by immune T Cells. We show how the problem of absolute discrimination can be solved by a process called "adaptive sorting". We review several implementations of adaptive sorting, as well as its generic properties such as antagonism. We show how kinetic proofreading with negative feedback implement an approximate version of adaptive sorting in the immune context. Finally, we revisit the decision problem at the cell population level, showing how phenotypic variability and feedbacks between population and single cells are crucial for proper decision.

The Adaptability of Career Decision-Making Profiles

ERIC Educational Resources Information Center

Gadassi, Reuma; Gati, Itamar; Dayan, Amira

2012-01-01

The Career Decision-Making Profiles questionnaire (CDMP; Gati, Landman, Davidovitch, Asulin-Peretz, & Gadassi, 2010) uses a new model for characterizing the way individuals make decisions based on the simultaneous use of 11 dimensions. The present study investigated which pole of each dimension is more adaptive. Using the data of 383 young…

Episodic memories predict adaptive value-based decision-making

PubMed Central

Murty, Vishnu; FeldmanHall, Oriel; Hunter, Lindsay E.; Phelps, Elizabeth A; Davachi, Lila

2016-01-01

Prior research illustrates that memory can guide value-based decision-making. For example, previous work has implicated both working memory and procedural memory (i.e., reinforcement learning) in guiding choice. However, other types of memories, such as episodic memory, may also influence decision-making. Here we test the role for episodic memory—specifically item versus associative memory—in supporting value-based choice. Participants completed a task where they first learned the value associated with trial unique lotteries. After a short delay, they completed a decision-making task where they could choose to re-engage with previously encountered lotteries, or new never before seen lotteries. Finally, participants completed a surprise memory test for the lotteries and their associated values. Results indicate that participants chose to re-engage more often with lotteries that resulted in high versus low rewards. Critically, participants not only formed detailed, associative memories for the reward values coupled with individual lotteries, but also exhibited adaptive decision-making only when they had intact associative memory. We further found that the relationship between adaptive choice and associative memory generalized to more complex, ecologically valid choice behavior, such as social decision-making. However, individuals more strongly encode experiences of social violations—such as being treated unfairly, suggesting a bias for how individuals form associative memories within social contexts. Together, these findings provide an important integration of episodic memory and decision-making literatures to better understand key mechanisms supporting adaptive behavior. PMID:26999046

A Feedback Learning and Mental Models Perspective on Strategic Decision Making

ERIC Educational Resources Information Center

Capelo, Carlos; Dias, Joao Ferreira

2009-01-01

This study aims to be a contribution to a theoretical model that explains the effectiveness of the learning and decision-making processes by means of a feedback and mental models perspective. With appropriate mental models, managers should be able to improve their capacity to deal with dynamically complex contexts, in order to achieve long-term…

Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

PubMed

Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

2018-06-01

This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

Improved methods in neural network-based adaptive output feedback control, with applications to flight control

NASA Astrophysics Data System (ADS)

Kim, Nakwan

Utilizing the universal approximation property of neural networks, we develop several novel approaches to neural network-based adaptive output feedback control of nonlinear systems, and illustrate these approaches for several flight control applications. In particular, we address the problem of non-affine systems and eliminate the fixed point assumption present in earlier work. All of the stability proofs are carried out in a form that eliminates an algebraic loop in the neural network implementation. An approximate input/output feedback linearizing controller is augmented with a neural network using input/output sequences of the uncertain system. These approaches permit adaptation to both parametric uncertainty and unmodeled dynamics. All physical systems also have control position and rate limits, which may either deteriorate performance or cause instability for a sufficiently high control bandwidth. Here we apply a method for protecting an adaptive process from the effects of input saturation and time delays, known as "pseudo control hedging". This method was originally developed for the state feedback case, and we provide a stability analysis that extends its domain of applicability to the case of output feedback. The approach is illustrated by the design of a pitch-attitude flight control system for a linearized model of an R-50 experimental helicopter, and by the design of a pitch-rate control system for a 58-state model of a flexible aircraft consisting of rigid body dynamics coupled with actuator and flexible modes. A new approach to augmentation of an existing linear controller is introduced. It is especially useful when there is limited information concerning the plant model, and the existing controller. The approach is applied to the design of an adaptive autopilot for a guided munition. Design of a neural network adaptive control that ensures asymptotically stable tracking performance is also addressed.

Eye-Hand Coordination during Visuomotor Adaptation with Different Rotation Angles: Effects of Terminal Visual Feedback

PubMed Central

Rand, Miya K.; Rentsch, Sebastian

2016-01-01

This study examined adaptive changes of eye-hand coordination during a visuomotor rotation task under the use of terminal visual feedback. Young adults made reaching movements to targets on a digitizer while looking at targets on a monitor where the rotated feedback (a cursor) of hand movements appeared after each movement. Three rotation angles (30°, 75° and 150°) were examined in three groups in order to vary the task difficulty. The results showed that the 30° group gradually reduced direction errors of reaching with practice and adapted well to the visuomotor rotation. The 75° group made large direction errors of reaching, and the 150° group applied a 180° reversal shift from early practice. The 75°and 150° groups, however, overcompensated the respective rotations at the end of practice. Despite these group differences in adaptive changes of reaching, all groups gradually adapted gaze directions prior to reaching from the target area to the areas related to the final positions of reaching during the course of practice. The adaptive changes of both hand and eye movements in all groups mainly reflected adjustments of movement directions based on explicit knowledge of the applied rotation acquired through practice. Only the 30° group showed small implicit adaptation in both effectors. The results suggest that by adapting gaze directions from the target to the final position of reaching based on explicit knowledge of the visuomotor rotation, the oculomotor system supports the limb-motor system to make precise preplanned adjustments of reaching directions during learning of visuomotor rotation under terminal visual feedback. PMID:27812093

Fuzzy Adaptive Output Feedback Control of Uncertain Nonlinear Systems With Prescribed Performance.

PubMed

Zhang, Jin-Xi; Yang, Guang-Hong

2018-05-01

This paper investigates the tracking control problem for a family of strict-feedback systems in the presence of unknown nonlinearities and immeasurable system states. A low-complexity adaptive fuzzy output feedback control scheme is proposed, based on a backstepping method. In the control design, a fuzzy adaptive state observer is first employed to estimate the unmeasured states. Then, a novel error transformation approach together with a new modification mechanism is introduced to guarantee the finite-time convergence of the output error to a predefined region and ensure the closed-loop stability. Compared with the existing methods, the main advantages of our approach are that: 1) without using extra command filters or auxiliary dynamic surface control techniques, the problem of explosion of complexity can still be addressed and 2) the design procedures are independent of the initial conditions. Finally, two practical examples are performed to further illustrate the above theoretic findings.

Effectiveness of a Video-Feedback and Questioning Programme to Develop Cognitive Expertise in Sport

PubMed Central

García-González, Luis; Moreno, M. Perla; Moreno, Alberto; Gil, Alexander; del Villar, Fernando

2013-01-01

The importance within sport expertise of cognitive factors has been emphasised in many research studies. Adaptations that take place in athletes’ long-term memories are going to condition their decision-making and performance, and training programmes must be developed that improve these adaptations. In our study, we provide a tactical-cognitive training programme based on video-feedback and questioning in order to improve tactical knowledge in tennis players and verify its effect when transferred to athletes’ decision-making. 11 intermediate tennis players participated in this study (12.9±0.7 years old), distributed into two groups (experimental, n = 5; control, n = 6). Tactical knowledge was measured by problem representation and strategy planning with a verbal protocol. Decision-making was measured by a systematic observation instrument. Results confirm the effectiveness of a combination of video-feedback and questioning on cognitive expertise, developing adaptations in long-term memory that produce an improvement in the quality of tactical knowledge (content, sophistication and structure). This, in turn, is transferred to the athletes’ decision-making capacity, leading to a higher percentage of successful decisions made during game play. Finally, we emphasise the need to develop effective programmes to develop cognitive expertise and improve athletes' performance, and include it in athletes’ formative stages. PMID:24340012

Social influences on adaptive criterion learning.

PubMed

Cassidy, Brittany S; Dubé, Chad; Gutchess, Angela H

2015-07-01

People adaptively shift decision criteria when given biased feedback encouraging specific types of errors. Given that work on this topic has been conducted in nonsocial contexts, we extended the literature by examining adaptive criterion learning in both social and nonsocial contexts. Specifically, we compared potential differences in criterion shifting given performance feedback from social sources varying in reliability and from a nonsocial source. Participants became lax when given false positive feedback for false alarms, and became conservative when given false positive feedback for misses, replicating prior work. In terms of a social influence on adaptive criterion learning, people became more lax in response style over time if feedback was provided by a nonsocial source or by a social source meant to be perceived as unreliable and low-achieving. In contrast, people adopted a more conservative response style over time if performance feedback came from a high-achieving and reliable source. Awareness that a reliable and high-achieving person had not provided their feedback reduced the tendency to become more conservative, relative to those unaware of the source manipulation. Because teaching and learning often occur in a social context, these findings may have important implications for many scenarios in which people fine-tune their behaviors, given cues from others.

Adaptive output-feedback control for switched stochastic uncertain nonlinear systems with time-varying delay.

PubMed

Song, Zhibao; Zhai, Junyong

2018-04-01

This paper addresses the problem of adaptive output-feedback control for a class of switched stochastic time-delay nonlinear systems with uncertain output function, where both the control coefficients and time-varying delay are unknown. The drift and diffusion terms are subject to unknown homogeneous growth condition. By virtue of adding a power integrator technique, an adaptive output-feedback controller is designed to render that the closed-loop system is bounded in probability, and the state of switched stochastic nonlinear system can be globally regulated to the origin almost surely. A numerical example is provided to demonstrate the validity of the proposed control method. Copyright © 2018 ISA. Published by Elsevier Ltd. All rights reserved.

Nonlinear filter based decision feedback equalizer for optical communication systems.

PubMed

Han, Xiaoqi; Cheng, Chi-Hao

2014-04-07

Nonlinear impairments in optical communication system have become a major concern of optical engineers. In this paper, we demonstrate that utilizing a nonlinear filter based Decision Feedback Equalizer (DFE) with error detection capability can deliver a better performance compared with the conventional linear filter based DFE. The proposed algorithms are tested in simulation using a coherent 100 Gb/sec 16-QAM optical communication system in a legacy optical network setting.

Adaptive Output-Feedback Neural Control of Switched Uncertain Nonlinear Systems With Average Dwell Time.

PubMed

Long, Lijun; Zhao, Jun

2015-07-01

This paper investigates the problem of adaptive neural tracking control via output-feedback for a class of switched uncertain nonlinear systems without the measurements of the system states. The unknown control signals are approximated directly by neural networks. A novel adaptive neural control technique for the problem studied is set up by exploiting the average dwell time method and backstepping. A switched filter and different update laws are designed to reduce the conservativeness caused by adoption of a common observer and a common update law for all subsystems. The proposed controllers of subsystems guarantee that all closed-loop signals remain bounded under a class of switching signals with average dwell time, while the output tracking error converges to a small neighborhood of the origin. As an application of the proposed design method, adaptive output feedback neural tracking controllers for a mass-spring-damper system are constructed.

Integrated Flight/Structural Mode Control for Very Flexible Aircraft Using L1 Adaptive Output Feedback Controller

NASA Technical Reports Server (NTRS)

Che, Jiaxing; Cao, Chengyu; Gregory, Irene M.

2012-01-01

This paper explores application of adaptive control architecture to a light, high-aspect ratio, flexible aircraft configuration that exhibits strong rigid body/flexible mode coupling. Specifically, an L(sub 1) adaptive output feedback controller is developed for a semi-span wind tunnel model capable of motion. The wind tunnel mount allows the semi-span model to translate vertically and pitch at the wing root, resulting in better simulation of an aircraft s rigid body motion. The control objective is to design a pitch control with altitude hold while suppressing body freedom flutter. The controller is an output feedback nominal controller (LQG) augmented by an L(sub 1) adaptive loop. A modification to the L(sub 1) output feedback is proposed to make it more suitable for flexible structures. The new control law relaxes the required bounds on the unmatched uncertainty and allows dependence on the state as well as time, i.e. a more general unmatched nonlinearity. The paper presents controller development and simulated performance responses. Simulation is conducted by using full state flexible wing models derived from test data at 10 different dynamic pressure conditions. An L(sub 1) adaptive output feedback controller is designed for a single test point and is then applied to all the test cases. The simulation results show that the L(sub 1) augmented controller can stabilize and meet the performance requirements for all 10 test conditions ranging from 30 psf to 130 psf dynamic pressure.

Adaptive Neural Networks Decentralized FTC Design for Nonstrict-Feedback Nonlinear Interconnected Large-Scale Systems Against Actuator Faults.

PubMed

Li, Yongming; Tong, Shaocheng

The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small neighborhood of zero. Finally, numerical results of practical examples are presented to further demonstrate the effectiveness of the proposed control strategy.The problem of active fault-tolerant control (FTC) is investigated for the large-scale nonlinear systems in nonstrict-feedback form. The nonstrict-feedback nonlinear systems considered in this paper consist of unstructured uncertainties, unmeasured states, unknown interconnected terms, and actuator faults (e.g., bias fault and gain fault). A state observer is designed to solve the unmeasurable state problem. Neural networks (NNs) are used to identify the unknown lumped nonlinear functions so that the problems of unstructured uncertainties and unknown interconnected terms can be solved. By combining the adaptive backstepping design principle with the combination Nussbaum gain function property, a novel NN adaptive output-feedback FTC approach is developed. The proposed FTC controller can guarantee that all signals in all subsystems are bounded, and the tracking errors for each subsystem converge to a small

Designing Dynamic Adaptive Policy Pathways using Many-Objective Robust Decision Making

NASA Astrophysics Data System (ADS)

Kwakkel, Jan; Haasnoot, Marjolijn

2017-04-01

Dealing with climate risks in water management requires confronting a wide variety of deeply uncertain factors, while navigating a many dimensional space of trade-offs amongst objectives. There is an emerging body of literature on supporting this type of decision problem, under the label of decision making under deep uncertainty. Two approaches within this literature are Many-Objective Robust Decision Making, and Dynamic Adaptive Policy Pathways. In recent work, these approaches have been compared. One of the main conclusions of this comparison was that they are highly complementary. Many-Objective Robust Decision Making is a model based decision support approach, while Dynamic Adaptive Policy Pathways is primarily a conceptual framework for the design of flexible strategies that can be adapted over time in response to how the future is actually unfolding. In this research we explore this complementarity in more detail. Specifically, we demonstrate how Many-Objective Robust Decision Making can be used to design adaptation pathways. We demonstrate this combined approach using a water management problem, in the Netherlands. The water level of Lake IJselmeer, the main fresh water resource of the Netherlands, is currently managed through discharge by gravity. Due to climate change, this won't be possible in the future, unless water levels are changed. Changing the water level has undesirable flood risk and spatial planning consequences. The challenge is to find promising adaptation pathways that balance objectives related to fresh water supply, flood risk, and spatial issues, while accounting for uncertain climatic and land use change. We conclude that the combination of Many-Objective Robust Decision Making and Dynamic Adaptive Policy Pathways is particularly suited for dealing with deeply uncertain climate risks.

Acute effects of alcohol on feedback processing and outcome evaluation during risky decision-making: an ERP study.

PubMed

Euser, Anja S; van Meel, Catharina S; Snelleman, Michelle; Franken, Ingmar H A

2011-09-01

Although risky decision-making is one of the hallmarks of alcohol use disorders, relatively little is known about the acute psychopharmacological effects of alcohol on decision-making processes. The present study investigated the acute effects of alcohol on neural mechanisms underlying feedback processing and outcome evaluation during risky decision-making, using event-related brain potentials (ERPs). ERPs elicited by positive and negative feedback were recorded during performance of a modified version of the Balloon Analogue Risk Task in male participants receiving either a moderate dose of alcohol (0.65 g/kg alcohol; n = 32) or a non-alcoholic placebo beverage (n = 32). Overall, there was no significant difference in the mean number of pumps between the alcohol and the placebo condition. However, when analyzing over time, it was found that the alcohol group made more riskier choices at the beginning of the task than the placebo group. ERPs demonstrated that alcohol consumption did not affect early processing of negative feedback, indexed by the feedback-related negativity. By contrast, alcohol-intoxicated individuals showed significantly reduced P300 amplitudes in response to negative feedback as compared to sober controls, suggesting that more elaborate evaluation to losses was significantly diminished. These results suggest that alcohol consumption does not influence the ability to rapidly evaluate feedback valence, but rather the ability to assign sufficient attention to further process motivationally salient outcomes. Blunted P300 amplitudes may reflect poor integration of feedback across trials, particularly adverse ones. Consequently, alcohol may keep people from effectively predicting the probability of future gains and losses based on their reinforcement history.

An adaptive molecular timer in p53-meidated cell fate decision

NASA Astrophysics Data System (ADS)

Zhang, Xiao-Peng; Wang, Ping; Liu, Feng; Wang, Wei

The tumor suppressor p53 decides cellular outcomes in the DNA damage response. It is intriguing to explore the link between p53 dynamics and cell fates. We developed a theoretical model of p53 signaling network to clarify the mechanism of cell fate decision mediated by its dynamics. We found that the interplay between p53-Mdm2 negative feedback loop and p53-PTEN-Mdm2 positive feedback loop shapes p53 dynamics. Depending on the intensity of DNA damage, p53 shows three modes of dynamics: persistent pulses, two-phase dynamics with pulses followed by sustained high levels and straightforward high levels. Especially, p53 shows two-phase dynamics upon moderated damage and the required number of p53 pulses before apoptosis induction decreases with increasing DNA damage. Our results suggested there exists an adaptive molecular timer that determines whether and when the apoptosis switch should be triggered. We clarified the mechanism behind the switching of p53 dynamical modes by bifurcation analysis. Moreover, we reproduced the experimental results that drug additions alter p53 pulses to sustained p53 activation and leads to senescence. Our work may advance the understanding the significance of p53 dynamics in tumor suppression. This work was supported by National Natural Science Foundation of China (Nos. 11175084, 11204126 and 31361163003).

Finite-Time Adaptive Control for a Class of Nonlinear Systems With Nonstrict Feedback Structure.

PubMed

Sun, Yumei; Chen, Bing; Lin, Chong; Wang, Honghong

2017-09-18

This paper focuses on finite-time adaptive neural tracking control for nonlinear systems in nonstrict feedback form. A semiglobal finite-time practical stability criterion is first proposed. Correspondingly, the finite-time adaptive neural control strategy is given by using this criterion. Unlike the existing results on adaptive neural/fuzzy control, the proposed adaptive neural controller guarantees that the tracking error converges to a sufficiently small domain around the origin in finite time, and other closed-loop signals are bounded. At last, two examples are used to test the validity of our results.

Studying citizen science through adaptive management and learning feedbacks as mechanisms for improving conservation.

PubMed

Jordan, Rebecca; Gray, Steven; Sorensen, Amanda; Newman, Greg; Mellor, David; Newman, Greg; Hmelo-Silver, Cindy; LaDeau, Shannon; Biehler, Dawn; Crall, Alycia

2016-06-01

Citizen science has generated a growing interest among scientists and community groups, and citizen science programs have been created specifically for conservation. We examined collaborative science, a highly interactive form of citizen science, which we developed within a theoretically informed framework. In this essay, we focused on 2 aspects of our framework: social learning and adaptive management. Social learning, in contrast to individual-based learning, stresses collaborative and generative insight making and is well-suited for adaptive management. Adaptive-management integrates feedback loops that are informed by what is learned and is guided by iterative decision making. Participants engaged in citizen science are able to add to what they are learning through primary data collection, which can result in the real-time information that is often necessary for conservation. Our work is particularly timely because research publications consistently report a lack of established frameworks and evaluation plans to address the extent of conservation outcomes in citizen science. To illustrate how our framework supports conservation through citizen science, we examined how 2 programs enacted our collaborative science framework. Further, we inspected preliminary conservation outcomes of our case-study programs. These programs, despite their recent implementation, are demonstrating promise with regard to positive conservation outcomes. To date, they are independently earning funds to support research, earning buy-in from local partners to engage in experimentation, and, in the absence of leading scientists, are collecting data to test ideas. We argue that this success is due to citizen scientists being organized around local issues and engaging in iterative, collaborative, and adaptive learning. © 2016 Society for Conservation Biology.

Monitoring in the context of structured decision-making and adaptive management

USGS Publications Warehouse

Lyons, J.E.; Runge, M.C.; Laskowski, H.P.; Kendall, W.L.

2008-01-01

In a natural resource management setting, monitoring is a crucial component of an informed process for making decisions, and monitoring design should be driven by the decision context and associated uncertainties. Monitoring itself can play >3 roles. First, it is important for state-dependent decision-making, as when managers need to know the system state before deciding on the appropriate course of action during the ensuing management cycle. Second, monitoring is critical for evaluating the effectiveness of management actions relative to objectives. Third, in an adaptive management setting, monitoring provides the feedback loop for learning about the system; learning is sought not for its own sake but primarily to better achieve management objectives. In this case, monitoring should be designed to reduce the critical uncertainties in models of the managed system. The United States Geological Survey and United States Fish and Wildlife Service are conducting a large-scale management experiment on 23 National Wildlife Refuges across the Northeast and Midwest Regions. The primary management objective is to provide habitat for migratory waterbirds, particularly during migration, using water-level manipulations in managed wetlands. Key uncertainties are related to the potential trade-offs created by management for a specific waterbird guild (e.g., migratory shorebirds) and the response of waterbirds, plant communities, and invertebrates to specific experimental hydroperiods. We reviewed the monitoring program associated with this study, and the ways that specific observations fill >1 of the roles identified above. We used observations from our monitoring to improve state-dependent decisions to control undesired plants, to evaluate management performance relative to shallow-water habitat objectives, and to evaluate potential trade-offs between waterfowl and shorebird habitat management. With limited staff and budgets, management agencies need efficient monitoring

Adaptive Fuzzy Tracking Control for a Class of MIMO Nonlinear Systems in Nonstrict-Feedback Form.

PubMed

Chen, Bing; Lin, Chong; Liu, Xiaoping; Liu, Kefu

2015-12-01

This paper focuses on the problem of fuzzy adaptive control for a class of multiinput and multioutput (MIMO) nonlinear systems in nonstrict-feedback form, which contains the strict-feedback form as a special case. By the condition of variable partition, a new fuzzy adaptive backstepping is proposed for such a class of nonlinear MIMO systems. The suggested fuzzy adaptive controller guarantees that the proposed control scheme can guarantee that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded and the tracking errors eventually converge to a small neighborhood around the origin. The main advantage of this paper is that a control approach is systematically derived for nonlinear systems with strong interconnected terms which are the functions of all states of the whole system. Simulation results further illustrate the effectiveness of the suggested approach.

The adaptive decision-making, risky decision, and decision-making style of Internet gaming disorder.

PubMed

Ko, C-H; Wang, P-W; Liu, T-L; Chen, C-S; Yen, C-F; Yen, J-Y

2017-07-01

Persistent gaming, despite acknowledgment of its negative consequences, is a major criterion for individuals with Internet gaming disorder (IGD). This study evaluated the adaptive decision-making, risky decision, and decision-making style of individuals with IGD. We recruited 87 individuals with IGD and 87 without IGD (matched controls). All participants underwent an interview based on the Diagnostic and Statistical Manual of Mental Disorders (5th Edition) diagnostic criteria for IGD and completed an adaptive decision-making task; the Preference for Intuition and Deliberation Scale, Chen Internet Addiction Scale, and Barratt Impulsivity Scale were also assessed on the basis of the information from the diagnostic interviews. The results demonstrated that the participants in both groups tend to make more risky choices in advantage trials where their expected value (EV) was more favorable than those of the riskless choice. The tendency to make a risky choice in advantage trials was stronger among IGD group than that among controls. Participants of both groups made more risky choices in the loss domain, a risky option to loss more versus sure loss option, than they did in the gain domain, a risky option to gain more versus sure gain. Furthermore, the participants with IGD made more risky choices in the gain domain than did the controls. Participants with IGD showed higher and lower preferences for intuitive and deliberative decision-making styles, respectively, than controls and their preferences for intuition and deliberation were positively and negatively associated with IGD severity, respectively. These results suggested that individuals with IGD have elevated EV sensitivity for decision-making. However, they demonstrated risky preferences in the gain domain and preferred an intuitive rather than deliberative decision-making style. This might explain why they continue Internet gaming despite negative consequences. Thus, therapists should focus more on decision

An adaptive approach to invasive plant management on U.S. Fish and Wildlife Service-owned native prairies in the Prairie Pothole Region: decision support under uncertainity

USGS Publications Warehouse

Gannon, Jill J.; Moore, Clinton T.; Shaffer, Terry L.; Flanders-Wanner, Bridgette

2011-01-01

Much of the native prairie managed by the U.S. Fish and Wildlife Service (Service) in the Prairie Pothole Region (PPR) is extensively invaded by the introduced cool-season grasses smooth brome (Bromus inermis) and Kentucky bluegrass (Poa pratensis). The central challenge to managers is selecting appropriate management actions in the face of biological and environmental uncertainties. We describe the technical components of a USGS management project, and explain how the components integrate and inform each other, how data feedback from individual cooperators serves to reduce uncertainty across the whole region, and how a successful adaptive management project is coordinated and maintained on a large scale. In partnership with the Service, the U.S. Geological Survey is developing an adaptive decision support framework to assist managers in selecting management actions under uncertainty and maximizing learning from management outcomes. The framework is built around practical constraints faced by refuge managers and includes identification of the management objective and strategies, analysis of uncertainty and construction of competing decision models, monitoring, and mechanisms for model feedback and decision selection. Nineteen Service field stations, spanning four states of the PPR, are participating in the project. They share a common management objective, available management strategies, and biological uncertainties. While the scope is broad, the project interfaces with individual land managers who provide refuge-specific information and receive updated decision guidance that incorporates understanding gained from the collective experience of all cooperators.

Risky decision making from childhood through adulthood: Contributions of learning and sensitivity to negative feedback.

PubMed

Humphreys, Kathryn L; Telzer, Eva H; Flannery, Jessica; Goff, Bonnie; Gabard-Durnam, Laurel; Gee, Dylan G; Lee, Steve S; Tottenham, Nim

2016-02-01

Decision making in the context of risk is a complex and dynamic process that changes across development. Here, we assessed the influence of sensitivity to negative feedback (e.g., loss) and learning on age-related changes in risky decision making, both of which show unique developmental trajectories. In the present study, we examined risky decision making in 216 individuals, ranging in age from 3-26 years, using the balloon emotional learning task (BELT), a computerized task in which participants pump up a series of virtual balloons to earn points, but risk balloon explosion on each trial, which results in no points. It is important to note that there were 3 balloon conditions, signified by different balloon colors, ranging from quick- to slow-to-explode, and participants could learn the color-condition pairings through task experience. Overall, we found age-related increases in pumps made and points earned. However, in the quick-to-explode condition, there was a nonlinear adolescent peak for points earned. Follow-up analyses indicated that this adolescent phenotype occurred at the developmental intersection of linear age-related increases in learning and decreases in sensitivity to negative feedback. Adolescence was marked by intermediate values on both these processes. These findings show that a combination of linearly changing processes can result in nonlinear changes in risky decision making, the adolescent-specific nature of which is associated with developmental improvements in learning and reduced sensitivity to negative feedback. (c) 2016 APA, all rights reserved).

Characterizing Decision-Analysis Performances of Risk Prediction Models Using ADAPT Curves.

PubMed

Lee, Wen-Chung; Wu, Yun-Chun

2016-01-01

The area under the receiver operating characteristic curve is a widely used index to characterize the performance of diagnostic tests and prediction models. However, the index does not explicitly acknowledge the utilities of risk predictions. Moreover, for most clinical settings, what counts is whether a prediction model can guide therapeutic decisions in a way that improves patient outcomes, rather than to simply update probabilities.Based on decision theory, the authors propose an alternative index, the "average deviation about the probability threshold" (ADAPT).An ADAPT curve (a plot of ADAPT value against the probability threshold) neatly characterizes the decision-analysis performances of a risk prediction model.Several prediction models can be compared for their ADAPT values at a chosen probability threshold, for a range of plausible threshold values, or for the whole ADAPT curves. This should greatly facilitate the selection of diagnostic tests and prediction models.

Adaptive decision rules for the acquisition of nature reserves.

PubMed

Turner, Will R; Wilcove, David S

2006-04-01

Although reserve-design algorithms have shown promise for increasing the efficiency of conservation planning, recent work casts doubt on the usefulness of some of these approaches in practice. Using three data sets that vary widely in size and complexity, we compared various decision rules for acquiring reserve networks over multiyear periods. We explored three factors that are often important in real-world conservation efforts: uncertain availability of sites for acquisition, degradation of sites, and overall budget constraints. We evaluated the relative strengths and weaknesses of existing optimal and heuristic decision rules and developed a new set of adaptive decision rules that combine the strengths of existing optimal and heuristic approaches. All three of the new adaptive rules performed better than the existing rules we tested under virtually all scenarios of site availability, site degradation, and budget constraints. Moreover, the adaptive rules required no additional data beyond what was readily available and were relatively easy to compute.

The adaptive use of recognition in group decision making.

PubMed

Kämmer, Juliane E; Gaissmaier, Wolfgang; Reimer, Torsten; Schermuly, Carsten C

2014-06-01

Applying the framework of ecological rationality, the authors studied the adaptivity of group decision making. In detail, they investigated whether groups apply decision strategies conditional on their composition in terms of task-relevant features. The authors focused on the recognition heuristic, so the task-relevant features were the validity of the group members' recognition and knowledge, which influenced the potential performance of group strategies. Forty-three three-member groups performed an inference task in which they had to infer which of two German companies had the higher market capitalization. Results based on the choice data support the hypothesis that groups adaptively apply the strategy that leads to the highest theoretically achievable performance. Time constraints had no effect on strategy use but did have an effect on the proportions of different types of arguments. Possible mechanisms underlying the adaptive use of recognition in group decision making are discussed. © 2014 Cognitive Science Society, Inc.

Engaging stakeholders for adaptive management using structured decision analysis

USGS Publications Warehouse

Irwin, Elise R.; Kathryn, D.; Kennedy, Mickett

2009-01-01

Adaptive management is different from other types of management in that it includes all stakeholders (versus only policy makers) in the process, uses resource optimization techniques to evaluate competing objectives, and recognizes and attempts to reduce uncertainty inherent in natural resource systems. Management actions are negotiated by stakeholders, monitored results are compared to predictions of how the system should respond, and management strategies are adjusted in a “monitor-compare-adjust” iterative routine. Many adaptive management projects fail because of the lack of stakeholder identification, engagement, and continued involvement. Primary reasons for this vary but are usually related to either stakeholders not having ownership (or representation) in decision processes or disenfranchisement of stakeholders after adaptive management begins. We present an example in which stakeholders participated fully in adaptive management of a southeastern regulated river. Structured decision analysis was used to define management objectives and stakeholder values and to determine initial flow prescriptions. The process was transparent, and the visual nature of the modeling software allowed stakeholders to see how their interests and values were represented in the decision process. The development of a stakeholder governance structure and communication mechanism has been critical to the success of the project.

The Feedback-related Negativity Codes Components of Abstract Inference during Reward-based Decision-making.

PubMed

Reiter, Andrea M F; Koch, Stefan P; Schröger, Erich; Hinrichs, Hermann; Heinze, Hans-Jochen; Deserno, Lorenz; Schlagenhauf, Florian

2016-08-01

Behavioral control is influenced not only by learning from the choices made and the rewards obtained but also by "what might have happened," that is, inference about unchosen options and their fictive outcomes. Substantial progress has been made in understanding the neural signatures of direct learning from choices that are actually made and their associated rewards via reward prediction errors (RPEs). However, electrophysiological correlates of abstract inference in decision-making are less clear. One seminal theory suggests that the so-called feedback-related negativity (FRN), an ERP peaking 200-300 msec after a feedback stimulus at frontocentral sites of the scalp, codes RPEs. Hitherto, the FRN has been predominantly related to a so-called "model-free" RPE: The difference between the observed outcome and what had been expected. Here, by means of computational modeling of choice behavior, we show that individuals employ abstract, "double-update" inference on the task structure by concurrently tracking values of chosen stimuli (associated with observed outcomes) and unchosen stimuli (linked to fictive outcomes). In a parametric analysis, model-free RPEs as well as their modification because of abstract inference were regressed against single-trial FRN amplitudes. We demonstrate that components related to abstract inference uniquely explain variance in the FRN beyond model-free RPEs. These findings advance our understanding of the FRN and its role in behavioral adaptation. This might further the investigation of disturbed abstract inference, as proposed, for example, for psychiatric disorders, and its underlying neural correlates.

Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach

PubMed Central

Cavagnaro, Daniel R.; Gonzalez, Richard; Myung, Jay I.; Pitt, Mark A.

2014-01-01

Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called Adaptive Design Optimization (ADO), adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate Expected Utility, Weighted Expected Utility, Original Prospect Theory, and Cumulative Prospect Theory models. PMID:24532856

Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach.

PubMed

Cavagnaro, Daniel R; Gonzalez, Richard; Myung, Jay I; Pitt, Mark A

2013-02-01

Collecting data to discriminate between models of risky choice requires careful selection of decision stimuli. Models of decision making aim to predict decisions across a wide range of possible stimuli, but practical limitations force experimenters to select only a handful of them for actual testing. Some stimuli are more diagnostic between models than others, so the choice of stimuli is critical. This paper provides the theoretical background and a methodological framework for adaptive selection of optimal stimuli for discriminating among models of risky choice. The approach, called Adaptive Design Optimization (ADO), adapts the stimulus in each experimental trial based on the results of the preceding trials. We demonstrate the validity of the approach with simulation studies aiming to discriminate Expected Utility, Weighted Expected Utility, Original Prospect Theory, and Cumulative Prospect Theory models.

Extracting an evaluative feedback from the brain for adaptation of motor neuroprosthetic decoders.

PubMed

Mahmoudi, Babak; Principe, Jose C; Sanchez, Justin C

2010-01-01

The design of Brain-Machine Interface (BMI) neural decoders that have robust performance in changing environments encountered in daily life activity is a challenging problem. One solution to this problem is the design of neural decoders that are able to assist and adapt to the user by participating in their perception-action-reward cycle (PARC). Using inspiration both from artificial intelligence and neurobiology reinforcement learning theories, we have designed a novel decoding architecture that enables a symbiotic relationship between the user and an Intelligent Assistant (IA). By tapping into the motor and reward centers in the brain, the IA adapts the process of decoding neural motor commands into prosthetic actions based on the user's goals. The focus of this paper is on extraction of goal information directly from the brain and making it accessible to the IA as an evaluative feedback for adaptation. We have recorded the neural activity of the Nucleus Accumbens in behaving rats during a reaching task. The peri-event time histograms demonstrate a rich representation of the reward prediction in this subcortical structure that can be modeled on a single trial basis as a scalar evaluative feedback with high precision.

Psychosocial intervention in at-risk adolescents: using event-related potentials to assess changes in decision making and feedback processing.

PubMed

Pincham, H L; Bryce, D; Fonagy, P; Fearon, R M Pasco

2018-05-25

Decision making and feedback processing are two important cognitive processes that are impacted by social context, particularly during adolescence. The current study examined whether a psychosocial intervention could improve psychological wellbeing in at-risk adolescent boys, thereby improving their decision making and feedback processing skills. Two groups of at-risk adolescents were compared: those who were relatively new to a psychosocial intervention, and those who had engaged over a longer time period. Electroencephalography was recorded while the young people participated in a modified version of the Taylor Aggression Paradigm. The late positive potential (LPP) was measured during the decision phase of the task (where participants selected punishments for their opponents). The feedback-related negativity (FRN) and P3 components were measured during the task's outcome phase (where participants received 'win' or 'lose' feedback). Adolescents who were new to the intervention (the minimal-intervention group) were harsher in their punishment selections than those who had been engaged in the program for much longer. The minimal-intervention group also showed an enhanced LPP during the decision phase of the task, which may be indicative of immature decision making in that group. Analysis of the FRN and P3 amplitudes revealed that the minimal-intervention group was physiologically hypo-sensitive to feedback, compared with the extended-intervention group. Overall, these findings suggest that long-term community-based psychosocial intervention programs are beneficial for at-risk adolescents, and that event-related potentials can be employed as biomarkers of therapeutic change. However, because participants were not randomly allocated to treatment groups, alternative explanations cannot be excluded until further randomized controlled trials are undertaken.

Adaptive Neural Control for a Class of Pure-Feedback Nonlinear Systems via Dynamic Surface Technique.

PubMed

Liu, Zongcheng; Dong, Xinmin; Xue, Jianping; Li, Hongbo; Chen, Yong

2016-09-01

This brief addresses the adaptive control problem for a class of pure-feedback systems with nonaffine functions possibly being nondifferentiable. Without using the mean value theorem, the difficulty of the control design for pure-feedback systems is overcome by modeling the nonaffine functions appropriately. With the help of neural network approximators, an adaptive neural controller is developed by combining the dynamic surface control (DSC) and minimal learning parameter (MLP) techniques. The key features of our approach are that, first, the restrictive assumptions on the partial derivative of nonaffine functions are removed, second, the DSC technique is used to avoid "the explosion of complexity" in the backstepping design, and the number of adaptive parameters is reduced significantly using the MLP technique, third, smooth robust compensators are employed to circumvent the influences of approximation errors and disturbances. Furthermore, it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, the simulation results are provided to demonstrate the effectiveness of the designed method.

Effects of Visual Feedback Distortion on Gait Adaptation: Comparison of Implicit Visual Distortion Versus Conscious Modulation on Retention of Motor Learning.

PubMed

Kim, Seung-Jae; Ogilvie, Mitchell; Shimabukuro, Nathan; Stewart, Trevor; Shin, Joon-Ho

2015-09-01

Visual feedback can be used during gait rehabilitation to improve the efficacy of training. We presented a paradigm called visual feedback distortion; the visual representation of step length was manipulated during treadmill walking. Our prior work demonstrated that an implicit distortion of visual feedback of step length entails an unintentional adaptive process in the subjects' spatial gait pattern. Here, we investigated whether the implicit visual feedback distortion, versus conscious correction, promotes efficient locomotor adaptation that relates to greater retention of a task. Thirteen healthy subjects were studied under two conditions: (1) we implicitly distorted the visual representation of their gait symmetry over 14 min, and (2) with help of visual feedback, subjects were told to walk on the treadmill with the intent of attaining the gait asymmetry observed during the first implicit trial. After adaptation, the visual feedback was removed while subjects continued walking normally. Over this 6-min period, retention of preserved asymmetric pattern was assessed. We found that there was a greater retention rate during the implicit distortion trial than that of the visually guided conscious modulation trial. This study highlights the important role of implicit learning in the context of gait rehabilitation by demonstrating that training with implicit visual feedback distortion may produce longer lasting effects. This suggests that using visual feedback distortion could improve the effectiveness of treadmill rehabilitation processes by influencing the retention of motor skills.

Mechanisms in adaptive feedback control: photoisomerization in a liquid.

PubMed

Hoki, Kunihito; Brumer, Paul

2005-10-14

The underlying mechanism for Adaptive Feedback Control in the experimental photoisomerization of 3,3'-diethyl-2,2'-thiacyanine iodide (NK88) in methanol is exposed theoretically. With given laboratory limitations on laser output, the complicated electric fields are shown to achieve their targets in qualitatively simple ways. Further, control over the cis population without laser limitations reveals an incoherent pump-dump scenario as the optimal isomerization strategy. In neither case are there substantial contributions from quantum multiple-path interference or from nuclear wave packet coherence. Environmentally induced decoherence is shown to justify the use of a simplified theoretical model.

An Iterative Information-Reduced Quadriphase-Shift-Keyed Carrier Synchronization Scheme Using Decision Feedback for Low Signal-to-Noise Ratio Applications

NASA Technical Reports Server (NTRS)

Simon, M.; Tkacenko, A.

2006-01-01

In a previous publication [1], an iterative closed-loop carrier synchronization scheme for binary phase-shift keyed (BPSK) modulation was proposed that was based on feeding back data decisions to the input of the loop, the purpose being to remove the modulation prior to carrier synchronization as opposed to the more conventional decision-feedback schemes that incorporate such feedback inside the loop. The idea there was that, with sufficient independence between the received data and the decisions on it that are fed back (as would occur in an error-correction coding environment with sufficient decoding delay), a pure tone in the presence of noise would ultimately be produced (after sufficient iteration and low enough error probability) and thus could be tracked without any squaring loss. This article demonstrates that, with some modification, the same idea of iterative information reduction through decision feedback can be applied to quadrature phase-shift keyed (QPSK) modulation, something that was mentioned in the previous publication but never pursued.

Do Amnesic Patients with Korsakoff's Syndrome Use Feedback when Making Decisions under Risky Conditions? An Experimental Investigation with the Game of Dice Task with and without Feedback

ERIC Educational Resources Information Center

Brand, Matthias; Pawlikowski, Mirko; Labudda, Kirsten; Laier, Christian; von Rothkirch, Nadine; Markowitsch, Hans J.

2009-01-01

We investigated the role of feedback processing in decision making under risk conditions in 50 patients with amnesia in the course of alcoholic Korsakoff's syndrome (KS). Half of the patients were administered the Game of Dice Task (GDT) and the remaining 25 patients were examined with a modified version of the GDT in which no feedback was…

Sequential decision making in computational sustainability via adaptive submodularity

USGS Publications Warehouse

Krause, Andreas; Golovin, Daniel; Converse, Sarah J.

2015-01-01

Many problems in computational sustainability require making a sequence of decisions in complex, uncertain environments. Such problems are generally notoriously difficult. In this article, we review the recently discovered notion of adaptive submodularity, an intuitive diminishing returns condition that generalizes the classical notion of submodular set functions to sequential decision problems. Problems exhibiting the adaptive submodularity property can be efficiently and provably near-optimally solved using simple myopic policies. We illustrate this concept in several case studies of interest in computational sustainability: First, we demonstrate how it can be used to efficiently plan for resolving uncertainty in adaptive management scenarios. Secondly, we show how it applies to dynamic conservation planning for protecting endangered species, a case study carried out in collaboration with the US Geological Survey and the US Fish and Wildlife Service.

Social provocation modulates decision making and feedback processing: Examining the trajectory of development in adolescent participants.

PubMed

Pincham, Hannah L; Wu, Claire; Killikelly, Clare; Vuillier, Laura; Fearon, R M Pasco

2015-10-01

Increasingly, research is turning to the ways in which social context impacts decision making and feedback processing in adolescents. The current study recorded electroencephalography to examine the trajectory of development across adolescence, with a focus on how social context impacts cognition and behaviour. To that end, younger (10-12 years) and older (14-16 years) adolescents played a modified Taylor Aggression Paradigm against two virtual opponents: a low-provoker and a high-provoker. During the task's decision phase (where participants select punishment for their opponent), we examined two event-related potentials: the N2 and the late positive potential (LPP). During the outcome phase (where participants experience win or loss feedback), we measured the feedback related negativity (FRN). Although N2 amplitudes did not vary with provocation, LPP amplitudes were enhanced under high provocation for the younger group, suggesting that emotional reactivity during the decision phase was heightened for early adolescents. During the outcome phase, the FRN was reduced following win outcomes under high provocation for both groups, suggesting that a highly provocative social opponent may influence the reward response. Collectively, the data argue that social context is an important factor modulating neural responses in adolescent behavioural and brain development. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Decision-feedback detection strategy for nonlinear frequency-division multiplexing

NASA Astrophysics Data System (ADS)

Civelli, Stella; Forestieri, Enrico; Secondini, Marco

2018-04-01

By exploiting a causality property of the nonlinear Fourier transform, a novel decision-feedback detection strategy for nonlinear frequency-division multiplexing (NFDM) systems is introduced. The performance of the proposed strategy is investigated both by simulations and by theoretical bounds and approximations, showing that it achieves a considerable performance improvement compared to previously adopted techniques in terms of Q-factor. The obtained improvement demonstrates that, by tailoring the detection strategy to the peculiar properties of the nonlinear Fourier transform, it is possible to boost the performance of NFDM systems and overcome current limitations imposed by the use of more conventional detection techniques suitable for the linear regime.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines.

PubMed

Khan, Arif Ul Maula; Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts.

A New Feedback-Based Method for Parameter Adaptation in Image Processing Routines

PubMed Central

Mikut, Ralf; Reischl, Markus

2016-01-01

The parametrization of automatic image processing routines is time-consuming if a lot of image processing parameters are involved. An expert can tune parameters sequentially to get desired results. This may not be productive for applications with difficult image analysis tasks, e.g. when high noise and shading levels in an image are present or images vary in their characteristics due to different acquisition conditions. Parameters are required to be tuned simultaneously. We propose a framework to improve standard image segmentation methods by using feedback-based automatic parameter adaptation. Moreover, we compare algorithms by implementing them in a feedforward fashion and then adapting their parameters. This comparison is proposed to be evaluated by a benchmark data set that contains challenging image distortions in an increasing fashion. This promptly enables us to compare different standard image segmentation algorithms in a feedback vs. feedforward implementation by evaluating their segmentation quality and robustness. We also propose an efficient way of performing automatic image analysis when only abstract ground truth is present. Such a framework evaluates robustness of different image processing pipelines using a graded data set. This is useful for both end-users and experts. PMID:27764213

Adaptation to delayed auditory feedback induces the temporal recalibration effect in both speech perception and production.

PubMed

Yamamoto, Kosuke; Kawabata, Hideaki

2014-12-01

We ordinarily speak fluently, even though our perceptions of our own voices are disrupted by various environmental acoustic properties. The underlying mechanism of speech is supposed to monitor the temporal relationship between speech production and the perception of auditory feedback, as suggested by a reduction in speech fluency when the speaker is exposed to delayed auditory feedback (DAF). While many studies have reported that DAF influences speech motor processing, its relationship to the temporal tuning effect on multimodal integration, or temporal recalibration, remains unclear. We investigated whether the temporal aspects of both speech perception and production change due to adaptation to the delay between the motor sensation and the auditory feedback. This is a well-used method of inducing temporal recalibration. Participants continually read texts with specific DAF times in order to adapt to the delay. Then, they judged the simultaneity between the motor sensation and the vocal feedback. We measured the rates of speech with which participants read the texts in both the exposure and re-exposure phases. We found that exposure to DAF changed both the rate of speech and the simultaneity judgment, that is, participants' speech gained fluency. Although we also found that a delay of 200 ms appeared to be most effective in decreasing the rates of speech and shifting the distribution on the simultaneity judgment, there was no correlation between these measurements. These findings suggest that both speech motor production and multimodal perception are adaptive to temporal lag but are processed in distinct ways.

Performance of a modified feedback loop adaptive array with TVRO satellite signals

NASA Technical Reports Server (NTRS)

Steadman, Karl N.; Gupta, Inder J.; Walton, Eric K.

1990-01-01

Performance of an experimental adaptive antenna array system is evaluated using television receive-only (TVRO) satellite signals. The experimental system is a sidelobe canceller with two auxiliary channels. Modified feedback loops are used to enhance the suppression of weak interfering signals. The modified feedback loops used two spatialy separated antennas, each with an individual amplifier for each auxiliary channel. Thus, the experimental system uses five antenna elements. Instead of using five separate antennas, a reflector antenna with multiple feeds is used to receive signals from various TVRO satellites. The details of the earth station are given. It is shown that the experimental system can null up to two signals originating from interfering TVRO satellites while receiving the signals from a desired TVRO satellite.

Performance of a modified feedback loop adaptive array with TVRO satellite signals

NASA Technical Reports Server (NTRS)

Steadman, K.; Gupta, I. J.; Walton, E. K.

1990-01-01

The performance of an experimental adaptive antenna array system is evaluated using television-receive-only (TVRO) satellite signals. The experimental system is a sidelobe canceler with two auxiliary channels. Modified feedback loops are used to enhance the suppression of weak interfering signals. The modified feedback loops use two spatially separate antennas, each with an individual amplifier for each auxiliary channel. Thus, the experimental system uses five antenna elements. Instead of using five separate antennas, a reflector antenna with multiple feeds is used to receive signals from various TVRO satellites. The details of the earth station are given. It is shown that the experimental system can null up to two signals originating from interfering TVRO satellites while receiving the signals from a desired TVRO satellite.

Variance adaptation in navigational decision making

NASA Astrophysics Data System (ADS)

Gershow, Marc; Gepner, Ruben; Wolk, Jason; Wadekar, Digvijay

Drosophila larvae navigate their environments using a biased random walk strategy. A key component of this strategy is the decision to initiate a turn (change direction) in response to declining conditions. We modeled this decision as the output of a Linear-Nonlinear-Poisson cascade and used reverse correlation with visual and fictive olfactory stimuli to find the parameters of this model. Because the larva responds to changes in stimulus intensity, we used stimuli with uncorrelated normally distributed intensity derivatives, i.e. Brownian processes, and took the stimulus derivative as the input to our LNP cascade. In this way, we were able to present stimuli with 0 mean and controlled variance. We found that the nonlinear rate function depended on the variance in the stimulus input, allowing larvae to respond more strongly to small changes in low-noise compared to high-noise environments. We measured the rate at which the larva adapted its behavior following changes in stimulus variance, and found that larvae adapted more quickly to increases in variance than to decreases, consistent with the behavior of an optimal Bayes estimator. Supported by NIH Grant 1DP2EB022359 and NSF Grant PHY-1455015.

Adaptive integral feedback controller for pitch and yaw channels of an AUV with actuator saturations.

PubMed

Sarhadi, Pouria; Noei, Abolfazl Ranjbar; Khosravi, Alireza

2016-11-01

Input saturations and uncertain dynamics are among the practical challenges in control of autonomous vehicles. Adaptive control is known as a proper method to deal with the uncertain dynamics of these systems. Therefore, incorporating the ability to confront with input saturation in adaptive controllers can be valuable. In this paper, an adaptive autopilot is presented for the pitch and yaw channels of an autonomous underwater vehicle (AUV) in the presence of input saturations. This will be performed by combination of a model reference adaptive control (MRAC) with integral state feedback with a modern anti-windup (AW) compensator. MRAC with integral state feedback is commonly used in autonomous vehicles. However, some proper modifications need to be taken into account in order to cope with the saturation problem. To this end, a Riccati-based anti-windup (AW) compensator is employed. The presented technique is applied to the non-linear six degrees of freedom (DOF) model of an AUV and the obtained results are compared with that of its baseline method. Several simulation scenarios are executed in the pitch and yaw channels to evaluate the controller performance. Moreover, effectiveness of proposed adaptive controller is comprehensively investigated by implementing Monte Carlo simulations. The obtained results verify the performance of proposed method. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Toolbox or Adjustable Spanner? A Critical Comparison of Two Metaphors for Adaptive Decision Making

ERIC Educational Resources Information Center

Söllner, Anke; Bröder, Arndt

2016-01-01

For multiattribute decision tasks, different metaphors exist that describe the process of decision making and its adaptation to diverse problems and situations. Multiple strategy models (MSMs) assume that decision makers choose adaptively from a set of different strategies (toolbox metaphor), whereas evidence accumulation models (EAMs) hold that a…

Formation tracker design of multiple mobile robots with wheel perturbations: adaptive output-feedback approach

NASA Astrophysics Data System (ADS)

Yoo, Sung Jin

2016-11-01

This paper presents a theoretical design approach for output-feedback formation tracking of multiple mobile robots under wheel perturbations. It is assumed that these perturbations are unknown and the linear and angular velocities of the robots are unmeasurable. First, adaptive state observers for estimating unmeasurable velocities of the robots are developed under the robots' kinematics and dynamics including wheel perturbation effects. Then, we derive a virtual-structure-based formation tracker scheme according to the observer dynamic surface design procedure. The main difficulty of the output-feedback control design is to manage the coupling problems between unmeasurable velocities and unknown wheel perturbation effects. These problems are avoided by using the adaptive technique and the function approximation property based on fuzzy logic systems. From the Lyapunov stability analysis, it is shown that point tracking errors of each robot and synchronisation errors for the desired formation converge to an adjustable neighbourhood of the origin, while all signals in the controlled closed-loop system are semiglobally uniformly ultimately bounded.

Adaptation of handwriting size under distorted visual feedback in patients with Parkinson's disease and elderly and young controls

PubMed Central

Teulings, H; Contreras-Vidal, J; Stelmach, G; Adler, C

2002-01-01

Objective: The ability to use visual feedback to control handwriting size was compared in patients with Parkinson's disease (PD), elderly people, and young adults to better understand factors playing a part in parkinsonian micrographia. Methods: The participants wrote sequences of eight cursive l loops with visual target sizes of 0.5 and 2 cm on a flat panel display digitiser which both recorded and displayed the pen movements. In the pre-exposure and postexposure conditions, the display digitiser showed the actual pen trace in real time and real size. In the distortion exposure conditions, the gain of the vertical dimension of the visual feedback was either reduced to 70% or enlarged to 140%. Results: The young controls showed a gradual visuomotor adaptation that compensated for the visual feedback distortions during the exposure conditions. They also showed significant after effects during the postexposure conditions. The elderly controls marginally corrected for the size distortions and showed small after effects. The patients with PD, however, showed no trial by trial adaptations or after effects but instead, a progressive amplification of the distortion effect in each individual trial. Conclusion: The young controls used visual feedback to update their visuomotor map. The elderly controls seemed to make little use of visual feedback. The patients with Parkinson's disease rely on the visual feedback of previous or of ongoing strokes to programme subsequent strokes. This recursive feedback may play a part in the progressive reductions in handwriting size found in parkinsonian micrographia. PMID:11861687

Implementation of an Automated Grading System with an Adaptive Learning Component to Affect Student Feedback and Response Time

ERIC Educational Resources Information Center

Matthews, Kevin; Janicki, Thomas; He, Ling; Patterson, Laurie

2012-01-01

This research focuses on the development and implementation of an adaptive learning and grading system with a goal to increase the effectiveness and quality of feedback to students. By utilizing various concepts from established learning theories, the goal of this research is to improve the quantity, quality, and speed of feedback as it pertains…

Frontal cortex electrophysiology in reward- and punishment-related feedback processing during advice-guided decision making: An interleaved EEG-DC stimulation study.

PubMed

Wischnewski, Miles; Bekkering, Harold; Schutter, Dennis J L G

2018-04-01

During decision making, individuals are prone to rely on external cues such as expert advice when the outcome is not known. However, the electrophysiological correlates associated with outcome uncertainty and the use of expert advice are not completely understood. The feedback-related negativity (FRN), P3a, and P3b are event-related brain potentials (ERPs) linked to dissociable stages of feedback and attentional processing during decision making. Even though these ERPs are influenced by both reward- and punishment-related feedback, it remains unclear how extrinsic information during uncertainty modulates these brain potentials. In this study, the effects of advice cues on decision making were investigated in two separate experiments. In the first experiment, electroencephalography (EEG) was recorded in healthy volunteers during a decision-making task in which the participants received reward or punishment feedback preceded by novice, amateur, or expert advice. The results showed that the P3a component was significantly influenced by the subjective predictive value of an advice cue, whereas the FRN and P3b were unaffected by the advice cues. In the second, sham-controlled experiment, cathodal transcranial direct current stimulation (ctDCS) was administered in conjunction with EEG in order to explore the direct contributions of the frontal cortex to these brain potentials. Results showed no significant change in either advice-following behavior or decision times. However, ctDCS did decrease FRN amplitudes as compared to sham, with no effect on the P3a or P3b. Together, these findings suggest that advice information may act primarily on attention allocation during feedback processing, whereas the electrophysiological correlates of the detection and updating of internal prediction models are not affected.

Vibration suppression for large scale adaptive truss structures using direct output feedback control

NASA Technical Reports Server (NTRS)

Lu, Lyan-Ywan; Utku, Senol; Wada, Ben K.

1993-01-01

In this article, the vibration control of adaptive truss structures, where the control actuation is provided by length adjustable active members, is formulated as a direct output feedback control problem. A control method named Model Truncated Output Feedback (MTOF) is presented. The method allows the control feedback gain to be determined in a decoupled and truncated modal space in which only the critical vibration modes are retained. The on-board computation required by MTOF is minimal; thus, the method is favorable for the applications of vibration control of large scale structures. The truncation of the modal space inevitably introduces spillover effect during the control process. In this article, the effect is quantified in terms of active member locations, and it is shown that the optimal placement of active members, which minimizes the spillover effect (and thus, maximizes the control performance) can be sought. The problem of optimally selecting the locations of active members is also treated.

Frequencies of decision making and monitoring in adaptive resource management

PubMed Central

Johnson, Fred A.

2017-01-01

Adaptive management involves learning-oriented decision making in the presence of uncertainty about the responses of a resource system to management. It is implemented through an iterative sequence of decision making, monitoring and assessment of system responses, and incorporating what is learned into future decision making. Decision making at each point is informed by a value or objective function, for example total harvest anticipated over some time frame. The value function expresses the value associated with decisions, and it is influenced by system status as updated through monitoring. Often, decision making follows shortly after a monitoring event. However, it is certainly possible for the cadence of decision making to differ from that of monitoring. In this paper we consider different combinations of annual and biennial decision making, along with annual and biennial monitoring. With biennial decision making decisions are changed only every other year; with biennial monitoring field data are collected only every other year. Different cadences of decision making combine with annual and biennial monitoring to define 4 scenarios. Under each scenario we describe optimal valuations for active and passive adaptive decision making. We highlight patterns in valuation among scenarios, depending on the occurrence of monitoring and decision making events. Differences between years are tied to the fact that every other year a new decision can be made no matter what the scenario, and state information is available to inform that decision. In the subsequent year, however, in 3 of the 4 scenarios either a decision is repeated or monitoring does not occur (or both). There are substantive differences in optimal values among the scenarios, as well as the optimal policies producing those values. Especially noteworthy is the influence of monitoring cadence on valuation in some years. We highlight patterns in policy and valuation among the scenarios, and discuss management

Frequencies of decision making and monitoring in adaptive resource management

USGS Publications Warehouse

Williams, Byron K.; Johnson, Fred A.

2017-01-01

Adaptive management involves learning-oriented decision making in the presence of uncertainty about the responses of a resource system to management. It is implemented through an iterative sequence of decision making, monitoring and assessment of system responses, and incorporating what is learned into future decision making. Decision making at each point is informed by a value or objective function, for example total harvest anticipated over some time frame. The value function expresses the value associated with decisions, and it is influenced by system status as updated through monitoring. Often, decision making follows shortly after a monitoring event. However, it is certainly possible for the cadence of decision making to differ from that of monitoring. In this paper we consider different combinations of annual and biennial decision making, along with annual and biennial monitoring. With biennial decision making decisions are changed only every other year; with biennial monitoring field data are collected only every other year. Different cadences of decision making combine with annual and biennial monitoring to define 4 scenarios. Under each scenario we describe optimal valuations for active and passive adaptive decision making. We highlight patterns in valuation among scenarios, depending on the occurrence of monitoring and decision making events. Differences between years are tied to the fact that every other year a new decision can be made no matter what the scenario, and state information is available to inform that decision. In the subsequent year, however, in 3 of the 4 scenarios either a decision is repeated or monitoring does not occur (or both). There are substantive differences in optimal values among the scenarios, as well as the optimal policies producing those values. Especially noteworthy is the influence of monitoring cadence on valuation in some years. We highlight patterns in policy and valuation among the scenarios, and discuss management

Adaptation pathways: ecoregion and land ownership influences on climate adaptation decision-making in forest management

Treesearch

Todd A. Ontl; Chris Swanston; Leslie A. Brandt; Patricia R. Butler; Anthony W. D’Amato; Stephen D. Handler; Maria K. Janowiak; P. Danielle Shannon

2018-01-01

Climate adaptation planning and implementation are likely to increase rapidly within the forest sector not only as climate continues to change but also as we intentionally learn from real-world examples. We sought to better understand how adaptation is being incorporated in land management decision-making across diverse land ownership types in the Midwest by evaluating...

Adaptive Feedback in Local Coordinates for Real-time Vision-Based Motion Control Over Long Distances

NASA Astrophysics Data System (ADS)

Aref, M. M.; Astola, P.; Vihonen, J.; Tabus, I.; Ghabcheloo, R.; Mattila, J.

2018-03-01

We studied the differences in noise-effects, depth-correlated behavior of sensors, and errors caused by mapping between coordinate systems in robotic applications of machine vision. In particular, the highly range-dependent noise densities for semi-unknown object detection were considered. An equation is proposed to adapt estimation rules to dramatic changes of noise over longer distances. This algorithm also benefits the smooth feedback of wheels to overcome variable latencies of visual perception feedback. Experimental evaluation of the integrated system is presented with/without the algorithm to highlight its effectiveness.

Neural responses to feedback information produced by self-generated or other-generated decision-making and their impairment in schizophrenia.

PubMed

Toyomaki, Atsuhito; Hashimoto, Naoki; Kako, Yuki; Murohashi, Harumitsu; Kusumi, Ichiro

2017-01-01

Several studies of self-monitoring dysfunction in schizophrenia have focused on the sense of agency to motor action using behavioral and psychophysiological techniques. So far, no study has ever tried to investigate whether the sense of agency or causal attribution for external events produced by self-generated decision-making is abnormal in schizophrenia. The purpose of this study was to investigate neural responses to feedback information produced by self-generated or other-generated decision-making in a multiplayer gambling task using even-related potentials and electroencephalogram synchronization. We found that the late positive component and theta/alpha synchronization were increased in response to feedback information in the self-decision condition in normal controls, but that these responses were significantly decreased in patients with schizophrenia. These neural activities thus reflect the self-reference effect that affects the cognitive appraisal of external events following decision-making and their impairment in schizophrenia.

Robust adaptive fuzzy tracking control for pure-feedback stochastic nonlinear systems with input constraints.

PubMed

Wang, Huanqing; Chen, Bing; Liu, Xiaoping; Liu, Kefu; Lin, Chong

2013-12-01

This paper is concerned with the problem of adaptive fuzzy tracking control for a class of pure-feedback stochastic nonlinear systems with input saturation. To overcome the design difficulty from nondifferential saturation nonlinearity, a smooth nonlinear function of the control input signal is first introduced to approximate the saturation function; then, an adaptive fuzzy tracking controller based on the mean-value theorem is constructed by using backstepping technique. The proposed adaptive fuzzy controller guarantees that all signals in the closed-loop system are bounded in probability and the system output eventually converges to a small neighborhood of the desired reference signal in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the proposed control scheme.

Computerized Adaptive Test vs. decision trees: Development of a support decision system to identify suicidal behavior.

PubMed

Delgado-Gomez, D; Baca-Garcia, E; Aguado, D; Courtet, P; Lopez-Castroman, J

2016-12-01

Several Computerized Adaptive Tests (CATs) have been proposed to facilitate assessments in mental health. These tests are built in a standard way, disregarding useful and usually available information not included in the assessment scales that could increase the precision and utility of CATs, such as the history of suicide attempts. Using the items of a previously developed scale for suicidal risk, we compared the performance of a standard CAT and a decision tree in a support decision system to identify suicidal behavior. We included the history of past suicide attempts as a class for the separation of patients in the decision tree. The decision tree needed an average of four items to achieve a similar accuracy than a standard CAT with nine items. The accuracy of the decision tree, obtained after 25 cross-validations, was 81.4%. A shortened test adapted for the separation of suicidal and non-suicidal patients was developed. CATs can be very useful tools for the assessment of suicidal risk. However, standard CATs do not use all the information that is available. A decision tree can improve the precision of the assessment since they are constructed using a priori information. Copyright © 2016 Elsevier B.V. All rights reserved.

A plastic corticostriatal circuit model of adaptation in perceptual decision making

PubMed Central

Hsiao, Pao-Yueh; Lo, Chung-Chuan

2013-01-01

The ability to optimize decisions and adapt them to changing environments is a crucial brain function that increase survivability. Although much has been learned about the neuronal activity in various brain regions that are associated with decision making, and about how the nervous systems may learn to achieve optimization, the underlying neuronal mechanisms of how the nervous systems optimize decision strategies with preference given to speed or accuracy, and how the systems adapt to changes in the environment, remain unclear. Based on extensive empirical observations, we addressed the question by extending a previously described cortico-basal ganglia circuit model of perceptual decisions with the inclusion of a dynamic dopamine (DA) system that modulates spike-timing dependent plasticity (STDP). We found that, once an optimal model setting that maximized the reward rate was selected, the same setting automatically optimized decisions across different task environments through dynamic balancing between the facilitating and depressing components of the DA dynamics. Interestingly, other model parameters were also optimal if we considered the reward rate that was weighted by the subject's preferences for speed or accuracy. Specifically, the circuit model favored speed if we increased the phasic DA response to the reward prediction error, whereas the model favored accuracy if we reduced the tonic DA activity or the phasic DA responses to the estimated reward probability. The proposed model provides insight into the roles of different components of DA responses in decision adaptation and optimization in a changing environment. PMID:24339814

Engaging Students with Feedback through Adaptive Release

ERIC Educational Resources Information Center

Irwin, Brian; Hepplestone, Stuart; Holden, Graham; Parkin, Helen J.; Thorpe, Louise

2013-01-01

Feedback to students has been highlighted in the literature as an area where improvements are needed. Students need high quality, prompt feedback, but they also need guidance and tools to help them engage with and learn from that feedback. This case study explores staff and student perceptions of a tool at Sheffield Hallam University which…

Adaptive neural coding: from biological to behavioral decision-making

PubMed Central

Louie, Kenway; Glimcher, Paul W.; Webb, Ryan

2015-01-01

Empirical decision-making in diverse species deviates from the predictions of normative choice theory, but why such suboptimal behavior occurs is unknown. Here, we propose that deviations from optimality arise from biological decision mechanisms that have evolved to maximize choice performance within intrinsic biophysical constraints. Sensory processing utilizes specific computations such as divisive normalization to maximize information coding in constrained neural circuits, and recent evidence suggests that analogous computations operate in decision-related brain areas. These adaptive computations implement a relative value code that may explain the characteristic context-dependent nature of behavioral violations of classical normative theory. Examining decision-making at the computational level thus provides a crucial link between the architecture of biological decision circuits and the form of empirical choice behavior. PMID:26722666

CREB1 Genotype Modulates Adaptive Reward-Based Decisions in Humans.

PubMed

Wolf, Claudia; Mohr, Holger; Diekhof, Esther K; Vieker, Henning; Goya-Maldonado, Roberto; Trost, Sarah; Krämer, Bernd; Keil, Maria; Binder, Elisabeth B; Gruber, Oliver

2016-07-01

Cyclic AMP response element-binding protein (CREB) contributes to adaptation of mesocorticolimbic networks by modulating activity-regulated transcription and plasticity in neurons. Activity or expression changes of CREB in the nucleus accumbens (NAc) and orbital frontal cortex (OFC) interact with behavioral changes during reward-motivated learning. However, these findings from animal models have not been evaluated in humans. We tested whether CREB1 genotypes affect reward-motivated decisions and related brain activation, using BOLD fMRI in 224 young and healthy participants. More specifically, participants needed to adapt their decision to either pursue or resist immediate rewards to optimize the reward outcome. We found significant CREB1 genotype effects on choices to pursue increases of the reward outcome and on BOLD signal in the NAc, OFC, insula cortex, cingulate gyrus, hippocampus, amygdala, and precuneus during these decisions in comparison with those decisions avoiding total reward loss. Our results suggest that CREB1 genotype effects in these regions could contribute to individual differences in reward- and associative memory-based decision-making. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Decision-making tool for applying adaptive traffic control systems : final report.

DOT National Transportation Integrated Search

2016-03-01

Adaptive traffic signal control technologies have been increasingly deployed in real world situations. The objective of this project was to develop a decision-making tool to guide traffic engineers and decision-makers who must decide whether or not a...

Designing an Alternative Teaching Approach (Feedback Lecture) through the Use of Guided Decision-Making.

ERIC Educational Resources Information Center

Osterman, Dean

This chapter explains how the Guided Design method of teaching can be used to solve problems, and how this method was used in the development of a new method of teaching. Called the Feedback Lecture, this method is illustrated through an example, and research data on its effectiveness is presented. The Guided Decision-Making Process is also…

Adaptive neural control of MIMO nonlinear systems with a block-triangular pure-feedback control structure.

PubMed

Chen, Zhenfeng; Ge, Shuzhi Sam; Zhang, Yun; Li, Yanan

2014-11-01

This paper presents adaptive neural tracking control for a class of uncertain multiinput-multioutput (MIMO) nonlinear systems in block-triangular form. All subsystems within these MIMO nonlinear systems are of completely nonaffine pure-feedback form and allowed to have different orders. To deal with the nonaffine appearance of the control variables, the mean value theorem is employed to transform the systems into a block-triangular strict-feedback form with control coefficients being couplings among various inputs and outputs. A systematic procedure is proposed for the design of a new singularity-free adaptive neural tracking control strategy. Such a design procedure can remove the couplings among subsystems and hence avoids the possible circular control construction problem. As a consequence, all the signals in the closed-loop system are guaranteed to be semiglobally uniformly ultimately bounded. Moreover, the outputs of the systems are ensured to converge to a small neighborhood of the desired trajectories. Simulation studies verify the theoretical findings revealed in this paper.

Adaptive neural network output feedback control for stochastic nonlinear systems with unknown dead-zone and unmodeled dynamics.

PubMed

Tong, Shaocheng; Wang, Tong; Li, Yongming; Zhang, Huaguang

2014-06-01

This paper discusses the problem of adaptive neural network output feedback control for a class of stochastic nonlinear strict-feedback systems. The concerned systems have certain characteristics, such as unknown nonlinear uncertainties, unknown dead-zones, unmodeled dynamics and without the direct measurements of state variables. In this paper, the neural networks (NNs) are employed to approximate the unknown nonlinear uncertainties, and then by representing the dead-zone as a time-varying system with a bounded disturbance. An NN state observer is designed to estimate the unmeasured states. Based on both backstepping design technique and a stochastic small-gain theorem, a robust adaptive NN output feedback control scheme is developed. It is proved that all the variables involved in the closed-loop system are input-state-practically stable in probability, and also have robustness to the unmodeled dynamics. Meanwhile, the observer errors and the output of the system can be regulated to a small neighborhood of the origin by selecting appropriate design parameters. Simulation examples are also provided to illustrate the effectiveness of the proposed approach.

What is coded into memory in the absence of outcome feedback?

PubMed

Henriksson, Maria P; Elwin, Ebba; Juslin, Peter

2010-01-01

Although people often have to learn from environments with scarce and highly selective outcome feedback, the question of how nonfeedback trials are represented in memory and affect later performance has received little attention in models of learning and decision making. In this article, the authors use the generalized context model (Nosofsky, 1986) as a vehicle to test contrasting hypotheses about the coding of nonfeedback trials. Data across 3 experiments with selective decision-contingent and selective outcome-contingent feedback provide support for the hypothesis of constructivist coding (Elwin, Juslin, Olsson, & Enkvist, 2007), according to which the outcomes on nonfeedback trials are coded with the most likely outcome, as inferred by the individual. The relation to sampling-based approaches to judgment, and the adaptive significance of constructivist coding, are discussed. (PsycINFO Database Record (c) 2009 APA, all rights reserved).

When 'solutions of yesterday become problems of today': crisis-ridden decision making in a complex adaptive system (CAS)--the Additional Duty Hours Allowance in Ghana.

PubMed

Agyepong, Irene Akua; Kodua, Augustina; Adjei, Sam; Adam, Taghreed

2012-10-01

Implementation of policies (decisions) in the health sector is sometimes defeated by the system's response to the policy itself. This can lead to counter-intuitive, unanticipated, or more modest effects than expected by those who designed the policy. The health sector fits the characteristics of complex adaptive systems (CAS) and complexity is at the heart of this phenomenon. Anticipating both positive and negative effects of policy decisions, understanding the interests, power and interaction between multiple actors; and planning for the delayed and distal impact of policy decisions are essential for effective decision making in CAS. Failure to appreciate these elements often leads to a series of reductionist approach interventions or 'fixes'. This in turn can initiate a series of negative feedback loops that further complicates the situation over time. In this paper we use a case study of the Additional Duty Hours Allowance (ADHA) policy in Ghana to illustrate these points. Using causal loop diagrams, we unpack the intended and unintended effects of the policy and how these effects evolved over time. The overall goal is to advance our understanding of decision making in complex adaptive systems; and through this process identify some essential elements in formulating, updating and implementing health policy that can help to improve attainment of desired outcomes and minimize negative unintended effects.

Chaotic Feedback Loops within Decision Making Groups: Towards an Integration of Chaos Theory and Cybernetics.

ERIC Educational Resources Information Center

Keaten, James A.

This paper offers a model that integrates chaos theory and cybernetics, which can be used to describe the structure of decision making within small groups. The paper begins with an overview of cybernetics and chaos. Definitional characteristics of cybernetics are reviewed along with salient constructs, such as goal-seeking, feedback, feedback…

Parental Rearing Behavior Prospectively Predicts Adolescents' Risky Decision-Making and Feedback-Related Electrical Brain Activity

ERIC Educational Resources Information Center

Euser, Anja S.; Evans, Brittany E.; Greaves-Lord, Kirstin; Huizink, Anja C.; Franken, Ingmar H. A.

2013-01-01

The present study examined the role of parental rearing behavior in adolescents' risky decision-making and the brain's feedback processing mechanisms. Healthy adolescent participants ("n" = 110) completed the EMBU-C, a self-report questionnaire on perceived parental rearing behaviors between 2006 and 2008 (T1). Subsequently, after an…

A decision analysis approach to climate adaptation: comparing multiple pathways for multi-decadal decision making

NASA Astrophysics Data System (ADS)

Lin, B. B.; Little, L.

2013-12-01

Policy planners around the world are required to consider the implications of adapting to climatic change across spatial contexts and decadal timeframes. However, local level information for planning is often poorly defined, even though climate adaptation decision-making is made at this scale. This is especially true when considering sea level rise and coastal impacts of climate change. We present a simple approach using sea level rise simulations paired with adaptation scenarios to assess a range of adaptation options available to local councils dealing with issues of beach recession under present and future sea level rise and storm surge. Erosion and beach recession pose a large socioeconomic risk to coastal communities because of the loss of key coastal infrastructure. We examine the well-known adaptation technique of beach nourishment and assess various timings and amounts of beach nourishment at decadal time spans in relation to beach recession impacts. The objective was to identify an adaptation strategy that would allow for a low frequency of management interventions, the maintenance of beach width, and the ability to minimize variation in beach width over the 2010 to 2100 simulation period. 1000 replications of each adaptation option were produced against the 90 year simulation in order to model the ability each adaptation option to achieve the three key objectives. Three sets of adaptation scenarios were identified. Within each scenario, a number of adaptation options were tested. The three scenarios were: 1) Fixed periodic beach replenishment of specific amounts at 20 and 50 year intervals, 2) Beach replenishment to the initial beach width based on trigger levels of recession (5m, 10m, 20m), and 3) Fixed period beach replenishment of a variable amount at decadal intervals (every 10, 20, 30, 40, 50 years). For each adaptation option, we show the effectiveness of each beach replenishment scenario to maintain beach width and consider the implications of more

Dynamic adaptive learning for decision-making supporting systems

NASA Astrophysics Data System (ADS)

He, Haibo; Cao, Yuan; Chen, Sheng; Desai, Sachi; Hohil, Myron E.

2008-03-01

This paper proposes a novel adaptive learning method for data mining in support of decision-making systems. Due to the inherent characteristics of information ambiguity/uncertainty, high dimensionality and noisy in many homeland security and defense applications, such as surveillances, monitoring, net-centric battlefield, and others, it is critical to develop autonomous learning methods to efficiently learn useful information from raw data to help the decision making process. The proposed method is based on a dynamic learning principle in the feature spaces. Generally speaking, conventional approaches of learning from high dimensional data sets include various feature extraction (principal component analysis, wavelet transform, and others) and feature selection (embedded approach, wrapper approach, filter approach, and others) methods. However, very limited understandings of adaptive learning from different feature spaces have been achieved. We propose an integrative approach that takes advantages of feature selection and hypothesis ensemble techniques to achieve our goal. Based on the training data distributions, a feature score function is used to provide a measurement of the importance of different features for learning purpose. Then multiple hypotheses are iteratively developed in different feature spaces according to their learning capabilities. Unlike the pre-set iteration steps in many of the existing ensemble learning approaches, such as adaptive boosting (AdaBoost) method, the iterative learning process will automatically stop when the intelligent system can not provide a better understanding than a random guess in that particular subset of feature spaces. Finally, a voting algorithm is used to combine all the decisions from different hypotheses to provide the final prediction results. Simulation analyses of the proposed method on classification of different US military aircraft databases show the effectiveness of this method.

Revisiting the generation and interpretation of climate models experiments for adaptation decision-making (Invited)

NASA Astrophysics Data System (ADS)

Ranger, N.; Millner, A.; Niehoerster, F.

2010-12-01

Traditionally, climate change risk assessments have taken a roughly four-stage linear ‘chain’ of moving from socioeconomic projections, to climate projections, to primary impacts and then finally onto economic and social impact assessment. Adaptation decisions are then made on the basis of these outputs. The escalation of uncertainty through this chain is well known; resulting in an ‘explosion’ of uncertainties in the final risk and adaptation assessment. The space of plausible future risk scenarios is growing ever wider with the application of new techniques which aim to explore uncertainty ever more deeply; such as those used in the recent ‘probabilistic’ UK Climate Projections 2009, and the stochastic integrated assessment models, for example PAGE2002. This explosion of uncertainty can make decision-making problematic, particularly given that the uncertainty information communicated can not be treated as strictly probabilistic and therefore, is not an easy fit with standard decision-making under uncertainty approaches. Additional problems can arise from the fact that the uncertainty estimated for different components of the ‘chain’ is rarely directly comparable or combinable. Here, we explore the challenges and limitations of using current projections for adaptation decision-making. We report the findings of a recent report completed for the UK Adaptation Sub-Committee on approaches to deal with these challenges and make robust adaptation decisions today. To illustrate these approaches, we take a number of illustrative case studies, including a case of adaptation to hurricane risk on the US Gulf Coast. This is a particularly interesting case as it involves urgent adaptation of long-lived infrastructure but requires interpreting highly uncertain climate change science and modelling; i.e. projections of Atlantic basin hurricane activity. An approach we outline is reversing the linear chain of assessments to put the economics and decision

Adaptive positive position feedback control with a feedforward compensator of a magnetostrictive beam for vibration suppression

NASA Astrophysics Data System (ADS)

Bian, Leixiang; Zhu, Wei

2018-07-01

In this paper, a Fe–Ga alloy magnetostrictive beam is designed as an actuator to restrain the vibration of a supported mass. Dynamic modeling of the system based on the transfer matrix method of multibody system is first shown, and then a hybrid controller is developed to achieve vibration control. The proposed vibration controller combines a multi-mode adaptive positive position feedback (APPF) with a feedforward compensator. In the APPF control, an adaptive natural frequency estimator based on the recursive least-square method is developed to be used. In the feedforward compensator, the hysteresis of the magnetostrictive beam is linearized based on a Bouc–Wen model. The further remarkable vibration suppression capability of the proposed hybrid controller is demonstrated experimentally and compared with the positive position feedback controller. Experiment results show that the proposed controller is applicable to the magnetostrictive beam for improving vibration control effectiveness.

Good decision-making is associated with an adaptive cardiovascular response to social competitive stress.

PubMed

Alacreu-Crespo, Adrián; Costa, Raquel; Abad-Tortosa, Diana; Salvador, Alicia; Serrano, Miguel Ángel

2018-06-22

Competition elicits different psychological and cardiovascular responses depending on a person's skills. Decision-making has been considered a distal factor that influences competition, but there are no studies analyzing this relationship. Our objective was to analyze whether decision-making affects the response to competition. Specifically, we aimed to test whether good performers on a decision-making test, the Iowa Gambling Task (IGT), showed an adaptive cardiovascular response to competition. In all, 116 participants (44 women) performed the IGT and were classified into Good or Poor decision-makers. Subsequently, they were exposed to a stress task in two different conditions: a face-to-face competition (winners/losers) or a control condition, while an electrocardiogram was recorded. In the competition group, good decision-makers increased their high-frequency respect to the total heart rate variability (HF/HRV) levels during the task, compared to Poor decision-makers. Again, competition group good decision-makers, showed lower LF and higher HF/HRV reactivity than the control group, which represents lower HRV stress pattern. Moreover, in the group of losers, good decision-makers had a decline in low frequency (LF) during the task and faster recovery than poor decision-makers. In conclusion, good decision-makers have a more adaptive stress response and higher levels of mental effort, based on total HRV interpretation. Decision-making skills could be a factor in a more adaptive cardiovascular response to competition.

Evaluating a multispecies adaptive management framework: Must uncertainty impede effective decision-making?

USGS Publications Warehouse

Smith, David R.; McGowan, Conor P.; Daily, Jonathan P.; Nichols, James D.; Sweka, John A.; Lyons, James E.

2013-01-01

Application of adaptive management to complex natural resource systems requires careful evaluation to ensure that the process leads to improved decision-making. As part of that evaluation, adaptive policies can be compared with alternative nonadaptive management scenarios. Also, the value of reducing structural (ecological) uncertainty to achieving management objectives can be quantified.A multispecies adaptive management framework was recently adopted by the Atlantic States Marine Fisheries Commission for sustainable harvest of Delaware Bay horseshoe crabs Limulus polyphemus, while maintaining adequate stopover habitat for migrating red knots Calidris canutus rufa, the focal shorebird species. The predictive model set encompassed the structural uncertainty in the relationships between horseshoe crab spawning, red knot weight gain and red knot vital rates. Stochastic dynamic programming was used to generate a state-dependent strategy for harvest decisions given that uncertainty. In this paper, we employed a management strategy evaluation approach to evaluate the performance of this adaptive management framework. Active adaptive management was used by including model weights as state variables in the optimization and reducing structural uncertainty by model weight updating.We found that the value of information for reducing structural uncertainty is expected to be low, because the uncertainty does not appear to impede effective management. Harvest policy responded to abundance levels of both species regardless of uncertainty in the specific relationship that generated those abundances. Thus, the expected horseshoe crab harvest and red knot abundance were similar when the population generating model was uncertain or known, and harvest policy was robust to structural uncertainty as specified.Synthesis and applications. The combination of management strategy evaluation with state-dependent strategies from stochastic dynamic programming was an informative approach to

Usability evaluation and adaptation of the e-health Personal Patient Profile-Prostate decision aid for Spanish-speaking Latino men.

PubMed

Berry, Donna L; Halpenny, Barbara; Bosco, Jaclyn L F; Bruyere, John; Sanda, Martin G

2015-07-24

The Personal Patient Profile-Prostate (P3P), a web-based decision aid, was demonstrated to reduce decisional conflict in English-speaking men with localized prostate cancer early after initial diagnosis. The purpose of this study was to explore and enhance usability and cultural appropriateness of a Spanish P3P by Latino men with a diagnosis of prostate cancer. P3P was translated to Spanish and back-translated by three native Spanish-speaking translators working independently. Spanish-speaking Latino men with a diagnosis of localized prostate cancer, who had made treatment decisions in the past 24 months, were recruited from two urban clinical care sites. Individual cognitive interviews were conducted by two bilingual research assistants as each participant used the Spanish P3P. Notes of user behavior, feedback, and answers to direct questions about comprehension, usability and perceived usefulness were analyzed and categorized. Seven participants with a range of education levels identified 25 unique usability issues in navigation, content comprehension and completeness, sociocultural appropriateness, and methodology. Revisions were prioritized to refine the usability and cultural and linguistic appropriateness of the decision aid. Usability issues were discovered that are potential barriers to effective decision support. Successful use of decision aids requires adaptation and testing beyond translation. Our findings led to revisions further refining the usability and linguistic and cultural appropriateness of Spanish P3P.

Bio-inspired adaptive feedback error learning architecture for motor control.

PubMed

Tolu, Silvia; Vanegas, Mauricio; Luque, Niceto R; Garrido, Jesús A; Ros, Eduardo

2012-10-01

This study proposes an adaptive control architecture based on an accurate regression method called Locally Weighted Projection Regression (LWPR) and on a bio-inspired module, such as a cerebellar-like engine. This hybrid architecture takes full advantage of the machine learning module (LWPR kernel) to abstract an optimized representation of the sensorimotor space while the cerebellar component integrates this to generate corrective terms in the framework of a control task. Furthermore, we illustrate how the use of a simple adaptive error feedback term allows to use the proposed architecture even in the absence of an accurate analytic reference model. The presented approach achieves an accurate control with low gain corrective terms (for compliant control schemes). We evaluate the contribution of the different components of the proposed scheme comparing the obtained performance with alternative approaches. Then, we show that the presented architecture can be used for accurate manipulation of different objects when their physical properties are not directly known by the controller. We evaluate how the scheme scales for simulated plants of high Degrees of Freedom (7-DOFs).

Adaptable history biases in human perceptual decisions.

PubMed

Abrahamyan, Arman; Silva, Laura Luz; Dakin, Steven C; Carandini, Matteo; Gardner, Justin L

2016-06-21

When making choices under conditions of perceptual uncertainty, past experience can play a vital role. However, it can also lead to biases that worsen decisions. Consistent with previous observations, we found that human choices are influenced by the success or failure of past choices even in a standard two-alternative detection task, where choice history is irrelevant. The typical bias was one that made the subject switch choices after a failure. These choice history biases led to poorer performance and were similar for observers in different countries. They were well captured by a simple logistic regression model that had been previously applied to describe psychophysical performance in mice. Such irrational biases seem at odds with the principles of reinforcement learning, which would predict exquisite adaptability to choice history. We therefore asked whether subjects could adapt their irrational biases following changes in trial order statistics. Adaptability was strong in the direction that confirmed a subject's default biases, but weaker in the opposite direction, so that existing biases could not be eradicated. We conclude that humans can adapt choice history biases, but cannot easily overcome existing biases even if irrational in the current context: adaptation is more sensitive to confirmatory than contradictory statistics.

Spatially Common Sparsity Based Adaptive Channel Estimation and Feedback for FDD Massive MIMO

NASA Astrophysics Data System (ADS)

Gao, Zhen; Dai, Linglong; Wang, Zhaocheng; Chen, Sheng

2015-12-01

This paper proposes a spatially common sparsity based adaptive channel estimation and feedback scheme for frequency division duplex based massive multi-input multi-output (MIMO) systems, which adapts training overhead and pilot design to reliably estimate and feed back the downlink channel state information (CSI) with significantly reduced overhead. Specifically, a non-orthogonal downlink pilot design is first proposed, which is very different from standard orthogonal pilots. By exploiting the spatially common sparsity of massive MIMO channels, a compressive sensing (CS) based adaptive CSI acquisition scheme is proposed, where the consumed time slot overhead only adaptively depends on the sparsity level of the channels. Additionally, a distributed sparsity adaptive matching pursuit algorithm is proposed to jointly estimate the channels of multiple subcarriers. Furthermore, by exploiting the temporal channel correlation, a closed-loop channel tracking scheme is provided, which adaptively designs the non-orthogonal pilot according to the previous channel estimation to achieve an enhanced CSI acquisition. Finally, we generalize the results of the multiple-measurement-vectors case in CS and derive the Cramer-Rao lower bound of the proposed scheme, which enlightens us to design the non-orthogonal pilot signals for the improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterparts, and it is capable of approaching the performance bound.

Rolling bearing fault diagnosis based on time-delayed feedback monostable stochastic resonance and adaptive minimum entropy deconvolution

NASA Astrophysics Data System (ADS)

Li, Jimeng; Li, Ming; Zhang, Jinfeng

2017-08-01

Rolling bearings are the key components in the modern machinery, and tough operation environments often make them prone to failure. However, due to the influence of the transmission path and background noise, the useful feature information relevant to the bearing fault contained in the vibration signals is weak, which makes it difficult to identify the fault symptom of rolling bearings in time. Therefore, the paper proposes a novel weak signal detection method based on time-delayed feedback monostable stochastic resonance (TFMSR) system and adaptive minimum entropy deconvolution (MED) to realize the fault diagnosis of rolling bearings. The MED method is employed to preprocess the vibration signals, which can deconvolve the effect of transmission path and clarify the defect-induced impulses. And a modified power spectrum kurtosis (MPSK) index is constructed to realize the adaptive selection of filter length in the MED algorithm. By introducing the time-delayed feedback item in to an over-damped monostable system, the TFMSR method can effectively utilize the historical information of input signal to enhance the periodicity of SR output, which is beneficial to the detection of periodic signal. Furthermore, the influence of time delay and feedback intensity on the SR phenomenon is analyzed, and by selecting appropriate time delay, feedback intensity and re-scaling ratio with genetic algorithm, the SR can be produced to realize the resonance detection of weak signal. The combination of the adaptive MED (AMED) method and TFMSR method is conducive to extracting the feature information from strong background noise and realizing the fault diagnosis of rolling bearings. Finally, some experiments and engineering application are performed to evaluate the effectiveness of the proposed AMED-TFMSR method in comparison with a traditional bistable SR method.

Application of Adaptive Decision Aiding Systems to Computer-Assisted Instruction. Final Report, January-December 1974.

ERIC Educational Resources Information Center

May, Donald M.; And Others

The minicomputer-based Computerized Diagnostic and Decision Training (CDDT) system described combines the principles of artificial intelligence, decision theory, and adaptive computer assisted instruction for training in electronic troubleshooting. The system incorporates an adaptive computer program which learns the student's diagnostic and…

Self Adapted Testing as Formative Assessment: Effects of Feedback and Scoring on Engagement and Performance

ERIC Educational Resources Information Center

Arieli-Attali, Meirav

2016-01-01

This dissertation investigated the feasibility of self-adapted testing (SAT) as a formative assessment tool with the focus on learning. Under two different orientation goals--to excel on a test (performance goal) or to learn from the test (learning goal)--I examined the effect of different scoring rules provided as interactive feedback, on test…

Effects of subordinate feedback to the supervisor and participation in decision-making in the prediction of organizational support.

DOT National Transportation Integrated Search

1992-03-01

The present study tested the hypothesis that participation in decision-making (PDM) and perceived effectiveness of subordinate feedback to the supervisor would contribute unique variance in the prediction of perceptions of organizational support. In ...

A Decision Analysis Tool for Climate Impacts, Adaptations, and Vulnerabilities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Omitaomu, Olufemi A; Parish, Esther S; Nugent, Philip J

Climate change related extreme events (such as flooding, storms, and drought) are already impacting millions of people globally at a cost of billions of dollars annually. Hence, there are urgent needs for urban areas to develop adaptation strategies that will alleviate the impacts of these extreme events. However, lack of appropriate decision support tools that match local applications is limiting local planning efforts. In this paper, we present a quantitative analysis and optimization system with customized decision support modules built on geographic information system (GIS) platform to bridge this gap. This platform is called Urban Climate Adaptation Tool (Urban-CAT). Formore » all Urban-CAT models, we divide a city into a grid with tens of thousands of cells; then compute a list of metrics for each cell from the GIS data. These metrics are used as independent variables to predict climate impacts, compute vulnerability score, and evaluate adaptation options. Overall, the Urban-CAT system has three layers: data layer (that contains spatial data, socio-economic and environmental data, and analytic data), middle layer (that handles data processing, model management, and GIS operation), and application layer (that provides climate impacts forecast, adaptation optimization, and site evaluation). The Urban-CAT platform can guide city and county governments in identifying and planning for effective climate change adaptation strategies.« less

Collective irrationality and positive feedback.

PubMed

Nicolis, Stamatios C; Zabzina, Natalia; Latty, Tanya; Sumpter, David J T

2011-04-26

Recent experiments on ants and slime moulds have assessed the degree to which they make rational decisions when presented with a number of alternative food sources or shelter. Ants and slime moulds are just two examples of a wide range of species and biological processes that use positive feedback mechanisms to reach decisions. Here we use a generic, experimentally validated model of positive feedback between group members to show that the probability of taking the best of options depends crucially on the strength of feedback. We show how the probability of choosing the best option can be maximized by applying an optimal feedback strength. Importantly, this optimal value depends on the number of options, so that when we change the number of options the preference of the group changes, producing apparent "irrationalities". We thus reinterpret the idea that collectives show "rational" or "irrational" preferences as being a necessary consequence of the use of positive feedback. We argue that positive feedback is a heuristic which often produces fast and accurate group decision-making, but is always susceptible to apparent irrationality when studied under particular experimental conditions.

Design of robust adaptive controller and feedback error learning for rehabilitation in Parkinson's disease: a simulation study.

PubMed

Rouhollahi, Korosh; Emadi Andani, Mehran; Karbassi, Seyed Mahdi; Izadi, Iman

2017-02-01

Deep brain stimulation (DBS) is an efficient therapy to control movement disorders of Parkinson's tremor. Stimulation of one area of basal ganglia (BG) by DBS with no feedback is the prevalent opinion. Reduction of additional stimulatory signal delivered to the brain is the advantage of using feedback. This results in reduction of side effects caused by the excessive stimulation intensity. In fact, the stimulatory intensity of controllers is decreased proportional to reduction of hand tremor. The objective of this study is to design a new controller structure to decrease three indicators: (i) the hand tremor; (ii) the level of delivered stimulation in disease condition; and (iii) the ratio of the level of delivered stimulation in health condition to disease condition. For this purpose, the authors offer a new closed-loop control structure to stimulate two areas of BG simultaneously. One area (STN: subthalamic nucleus) is stimulated by an adaptive controller with feedback error learning. The other area (GPi: globus pallidus internal) is stimulated by a partial state feedback (PSF) controller. Considering the three indicators, the results show that, stimulating two areas simultaneously leads to better performance compared with stimulating one area only. It is shown that both PSF and adaptive controllers are robust regarding system parameter uncertainties. In addition, a method is proposed to update the parameters of the BG model in real time. As a result, the parameters of the controllers can be updated based on the new parameters of the BG model.

The fundamental role of ecological feedback mechanisms for the adaptive management of seagrass ecosystems - a review.

PubMed

Maxwell, Paul S; Eklöf, Johan S; van Katwijk, Marieke M; O'Brien, Katherine R; de la Torre-Castro, Maricela; Boström, Christoffer; Bouma, Tjeerd J; Krause-Jensen, Dorte; Unsworth, Richard K F; van Tussenbroek, Brigitta I; van der Heide, Tjisse

2017-08-01

Seagrass meadows are vital ecosystems in coastal zones worldwide, but are also under global threat. One of the major hurdles restricting the success of seagrass conservation and restoration is our limited understanding of ecological feedback mechanisms. In these ecosystems, multiple, self-reinforcing feedbacks can undermine conservation efforts by masking environmental impacts until the decline is precipitous, or alternatively they can inhibit seagrass recovery in spite of restoration efforts. However, no clear framework yet exists for identifying or dealing with feedbacks to improve the management of seagrass ecosystems. Here we review the causes and consequences of multiple feedbacks between seagrass and biotic and/or abiotic processes. We demonstrate how feedbacks have the potential to impose or reinforce regimes of either seagrass dominance or unvegetated substrate, and how the strength and importance of these feedbacks vary across environmental gradients. Although a myriad of feedbacks have now been identified, the co-occurrence and likely interaction among feedbacks has largely been overlooked to date due to difficulties in analysis and detection. Here we take a fundamental step forward by modelling the interactions among two distinct above- and belowground feedbacks to demonstrate that interacting feedbacks are likely to be important for ecosystem resilience. On this basis, we propose a five-step adaptive management plan to address feedback dynamics for effective conservation and restoration strategies. The management plan provides guidance to aid in the identification and prioritisation of likely feedbacks in different seagrass ecosystems. © 2016 Cambridge Philosophical Society.

Understanding and applying principles of social cognition and decision making in adaptive environmental governance.

PubMed

DeCaro, Daniel A; Arnol, Craig Anthony Tony; Boama, Emmanuel Frimpong; Garmestani, Ahjond S

2017-03-01

Environmental governance systems are under greater pressure to adapt and to cope with increased social and ecological uncertainty from stressors like climate change. We review principles of social cognition and decision making that shape and constrain how environmental governance systems adapt. We focus primarily on the interplay between key decision makers in society and legal systems. We argue that adaptive governance must overcome three cooperative dilemmas to facilitate adaptation: (1) encouraging collaborative problem solving, (2) garnering social acceptance and commitment, and (3) cultivating a culture of trust and tolerance for change and uncertainty. However, to do so governance systems must cope with biases in people's decision making that cloud their judgment and create conflict. These systems must also satisfy people's fundamental needs for self-determination, fairness, and security, ensuring that changes to environmental governance are perceived as legitimate, trustworthy, and acceptable. We discuss the implications of these principles for common governance solutions (e.g., public participation, enforcement) and conclude with methodological recommendations. We outline how scholars can investigate the social cognitive principles involved in cases of adaptive governance.

Understanding and applying principles of social cognition and decision making in adaptive environmental governance

PubMed Central

DeCaro, Daniel A.; Arnol, Craig Anthony (Tony); Boama, Emmanuel Frimpong; Garmestani, Ahjond S.

2018-01-01

Environmental governance systems are under greater pressure to adapt and to cope with increased social and ecological uncertainty from stressors like climate change. We review principles of social cognition and decision making that shape and constrain how environmental governance systems adapt. We focus primarily on the interplay between key decision makers in society and legal systems. We argue that adaptive governance must overcome three cooperative dilemmas to facilitate adaptation: (1) encouraging collaborative problem solving, (2) garnering social acceptance and commitment, and (3) cultivating a culture of trust and tolerance for change and uncertainty. However, to do so governance systems must cope with biases in people’s decision making that cloud their judgment and create conflict. These systems must also satisfy people’s fundamental needs for self-determination, fairness, and security, ensuring that changes to environmental governance are perceived as legitimate, trustworthy, and acceptable. We discuss the implications of these principles for common governance solutions (e.g., public participation, enforcement) and conclude with methodological recommendations. We outline how scholars can investigate the social cognitive principles involved in cases of adaptive governance. PMID:29780425

Dopamine D3 Receptor Availability Is Associated with Inflexible Decision Making.

PubMed

Groman, Stephanie M; Smith, Nathaniel J; Petrullli, J Ryan; Massi, Bart; Chen, Lihui; Ropchan, Jim; Huang, Yiyun; Lee, Daeyeol; Morris, Evan D; Taylor, Jane R

2016-06-22

Dopamine D2/3 receptor signaling is critical for flexible adaptive behavior; however, it is unclear whether D2, D3, or both receptor subtypes modulate precise signals of feedback and reward history that underlie optimal decision making. Here, PET with the radioligand [(11)C]-(+)-PHNO was used to quantify individual differences in putative D3 receptor availability in rodents trained on a novel three-choice spatial acquisition and reversal-learning task with probabilistic reinforcement. Binding of [(11)C]-(+)-PHNO in the midbrain was negatively related to the ability of rats to adapt to changes in rewarded locations, but not to the initial learning. Computational modeling of choice behavior in the reversal phase indicated that [(11)C]-(+)-PHNO binding in the midbrain was related to the learning rate and sensitivity to positive, but not negative, feedback. Administration of a D3-preferring agonist likewise impaired reversal performance by reducing the learning rate and sensitivity to positive feedback. These results demonstrate a previously unrecognized role for D3 receptors in select aspects of reinforcement learning and suggest that individual variation in midbrain D3 receptors influences flexible behavior. Our combined neuroimaging, behavioral, pharmacological, and computational approach implicates the dopamine D3 receptor in decision-making processes that are altered in psychiatric disorders. Flexible decision-making behavior is dependent upon dopamine D2/3 signaling in corticostriatal brain regions. However, the role of D3 receptors in adaptive, goal-directed behavior has not been thoroughly investigated. By combining PET imaging with the D3-preferring radioligand [(11)C]-(+)-PHNO, pharmacology, a novel three-choice probabilistic discrimination and reversal task and computational modeling of behavior in rats, we report that naturally occurring variation in [(11)C]-(+)-PHNO receptor availability relates to specific aspects of flexible decision making. We confirm

Danish translation, cultural adaption and initial psychometric evaluation of the patient feedback form.

PubMed

Tolstrup, Lærke K; Pappot, Helle; Zangger, Graziella; Bastholt, Lars; Zwisler, Ann-Dorthe; Dieperink, Karin B

2018-04-27

No suitable Danish questionnaire exists to evaluate patient satisfaction with various patient reported outcome measures. Thus, the aim of this research project was to conduct a study on the translation and cultural adaption of an American patient reported experience measures questionnaire, "Patient Feedback Form", among Danish patients, and to examine selected psychometric properties within reliability. In the first phase of the study, the Patient Feedback Form was forward and backward translated following the methodology of existing guidelines. Subsequently, cognitive interviewing was performed with seven cancer patients and seven healthy persons (19-86 years old/6 men and 8 women) to ensure that questions were easy to understand and made sense to Danish interviewees. In the second phase, phone interviews were carried out with 95 prostate cancer patients after they had responded to the same Patient Feedback Form. Missing data was imputed using the Expectation-Maximization technique. To examine the structure of the questionnaire, an exploratory factor analysis was conducted. Cronbach's alpha was calculated to investigate internal consistency. There were only minor disagreements in the translation process, and the reconciliation went smoothly (phase 1). With regard to one item, however, it was difficult to reach a consensus. Through the qualitative validation process, the right solution was found. The results from the psychometric testing (phase 2) showed that four factors had an Eigen value > 1, but only one factor was extracted as the Scree plot had a clear "elbow", showing a one factor structure that explained 46.1% of the variance. The internal consistency was high as Cronbach's alpha was 0.89. The translated, culturally adapted, and validated version of the Patient Feedback Form seems to be suitable for measuring satisfaction with patient reported outcome measures in a Danish setting. While the results should be treated with caution due to the small sample

Feedback control methods for drug dosage optimisation. Concepts, classification and clinical application.

PubMed

Vozeh, S; Steimer, J L

1985-01-01

The concept of feedback control methods for drug dosage optimisation is described from the viewpoint of control theory. The control system consists of 5 parts: (a) patient (the controlled process); (b) response (the measured feedback); (c) model (the mathematical description of the process); (d) adaptor (to update the parameters); and (e) controller (to determine optimum dosing strategy). In addition to the conventional distinction between open-loop and closed-loop control systems, a classification is proposed for dosage optimisation techniques which distinguishes between tight-loop and loose-loop methods depending on whether physician's interaction is absent or included as part of the control step. Unlike engineering problems where the process can usually be controlled by fully automated devices, therapeutic situations often require that the physician be included in the decision-making process to determine the 'optimal' dosing strategy. Tight-loop and loose-loop methods can be further divided into adaptive and non-adaptive, depending on the presence of the adaptor. The main application areas of tight-loop feedback control methods are general anaesthesia, control of blood pressure, and insulin delivery devices. Loose-loop feedback methods have been used for oral anticoagulation and in therapeutic drug monitoring. The methodology, advantages and limitations of the different approaches are reviewed. A general feature common to all application areas could be observed: to perform well under routine clinical conditions, which are characterised by large interpatient variability and sometimes also intrapatient changes, control systems should be adaptive. Apart from application in routine drug treatment, feedback control methods represent an important research tool. They can be applied for the investigation of pathophysiological and pharmacodynamic processes. A most promising application is the evaluation of the relationship between an intermediate response (e.g. drug

Modafinil alters decision making based on feedback history - a randomized placebo-controlled double blind study in humans.

PubMed

Bellebaum, Christian; Kuchinke, Lars; Roser, Patrik

2017-02-01

Modafinil is becoming increasingly popular as a cognitive enhancer. Research on the effects of modafinil on cognitive function have yielded mixed results, with negative findings for simple memory and attention tasks and enhancing effects for more complex tasks. In the present study we examined whether modafinil, due to its known effect on the dopamine level in the striatum, alters feedback-related choice behaviour. We applied a task that separately tests the choice of previously rewarded behaviours (approach) and avoidance of previously punished behaviours. 18 participants received a single dose of 200 mg modafinil. Their performance was compared to a group of 22 participants who received placebo in a double-blind design. Modafinil but not placebo induced a significant bias towards approach behaviour as compared to the frequency of avoidance behaviour. General attention, overall feedback-based acquisition of choice behaviour and reaction times in high vs low conflict choices were not significantly affected by modafinil. This finding suggests that modafinil has a specific effect on dopamine-mediated choice behaviour based on the history of feedback, while a contribution of noradrenaline is also conceivable. The described change in decision making cannot be considered as cognitive enhancement, but might rather have detrimental effects on decisions in everyday life.

Social closeness and feedback modulate susceptibility to the framing effect

PubMed Central

Sip, Kamila E.; Smith, David V.; Porcelli, Anthony J.; Kar, Kohitij; Delgado, Mauricio R.

2014-01-01

Although, we often seek social feedback from others to help us make decisions, little is known about how social feedback affects decisions under risk, particularly from a close peer. We conducted two experiments using an established framing task to probe how decision making is modulated by social feedback valence (positive, negative) and the level of closeness with feedback provider (friend, confederate). Participants faced mathematically equivalent decisions framed as either an opportunity to keep (gain frame) or lose (loss frame) part of an initial endowment. Periodically, participants were provided with positive (e.g., “Nice!”) or negative (e.g., “Lame!”) feedback about their choices. Such feedback was provided by either a confederate (Experiment 1), or a gender-matched close friend (Experiment 2). As expected, the framing effect was observed in both experiments. Critically, an individual’s susceptibility to the framing effect was modulated by the valence of the social feedback, but only when the feedback provider was a close friend. This effect was reflected in the activation patterns of ventromedial prefrontal cortex and posterior cingulate cortex, regions involved in complex decision making. Taken together, these results highlight social closeness as an important factor in understanding the impact of social feedback on neural mechanisms of decision making. PMID:25074501

Online adaptive decision trees: pattern classification and function approximation.

PubMed

Basak, Jayanta

2006-09-01

Recently we have shown that decision trees can be trained in the online adaptive (OADT) mode (Basak, 2004), leading to better generalization score. OADTs were bottlenecked by the fact that they are able to handle only two-class classification tasks with a given structure. In this article, we provide an architecture based on OADT, ExOADT, which can handle multiclass classification tasks and is able to perform function approximation. ExOADT is structurally similar to OADT extended with a regression layer. We also show that ExOADT is capable not only of adapting the local decision hyperplanes in the nonterminal nodes but also has the potential of smoothly changing the structure of the tree depending on the data samples. We provide the learning rules based on steepest gradient descent for the new model ExOADT. Experimentally we demonstrate the effectiveness of ExOADT in the pattern classification and function approximation tasks. Finally, we briefly discuss the relationship of ExOADT with other classification models.

Adaptive leadership: a novel approach for family decision making.

PubMed

Adams, Judith; Bailey, Donald E; Anderson, Ruth A; Galanos, Anthony N

2013-03-01

Family members of intensive care unit (ICU) patients want to be involved in decision making, but they may not be best served by being placed in the position of having to solve problems for which they lack knowledge and skills. This case report presents an exemplar family meeting in the ICU led by a palliative care specialist, with discussion about the strategies used to improve the capacity of the family to make a decision consistent with the patient's goals. These strategies are presented through the lens of Adaptive Leadership.

Hybrid Model Predictive Control for Sequential Decision Policies in Adaptive Behavioral Interventions.

PubMed

Dong, Yuwen; Deshpande, Sunil; Rivera, Daniel E; Downs, Danielle S; Savage, Jennifer S

2014-06-01

Control engineering offers a systematic and efficient method to optimize the effectiveness of individually tailored treatment and prevention policies known as adaptive or "just-in-time" behavioral interventions. The nature of these interventions requires assigning dosages at categorical levels, which has been addressed in prior work using Mixed Logical Dynamical (MLD)-based hybrid model predictive control (HMPC) schemes. However, certain requirements of adaptive behavioral interventions that involve sequential decision making have not been comprehensively explored in the literature. This paper presents an extension of the traditional MLD framework for HMPC by representing the requirements of sequential decision policies as mixed-integer linear constraints. This is accomplished with user-specified dosage sequence tables, manipulation of one input at a time, and a switching time strategy for assigning dosages at time intervals less frequent than the measurement sampling interval. A model developed for a gestational weight gain (GWG) intervention is used to illustrate the generation of these sequential decision policies and their effectiveness for implementing adaptive behavioral interventions involving multiple components.

The Sustained Influence of an Error on Future Decision-Making.

PubMed

Schiffler, Björn C; Bengtsson, Sara L; Lundqvist, Daniel

2017-01-01

Post-error slowing (PES) is consistently observed in decision-making tasks after negative feedback. Yet, findings are inconclusive as to whether PES supports performance accuracy. We addressed the role of PES by employing drift diffusion modeling which enabled us to investigate latent processes of reaction times and accuracy on a large-scale dataset (>5,800 participants) of a visual search experiment with emotional face stimuli. In our experiment, post-error trials were characterized by both adaptive and non-adaptive decision processes. An adaptive increase in participants' response threshold was sustained over several trials post-error. Contrarily, an initial decrease in evidence accumulation rate, followed by an increase on the subsequent trials, indicates a momentary distraction of task-relevant attention and resulted in an initial accuracy drop. Higher values of decision threshold and evidence accumulation on the post-error trial were associated with higher accuracy on subsequent trials which further gives credence to these parameters' role in post-error adaptation. Finally, the evidence accumulation rate post-error decreased when the error trial presented angry faces, a finding suggesting that the post-error decision can be influenced by the error context. In conclusion, we demonstrate that error-related response adaptations are multi-component processes that change dynamically over several trials post-error.

Beyond Prediction: the Many Ways in which Climate Science can Inform Adaptation Decisions

NASA Astrophysics Data System (ADS)

Lempert, R. J.

2017-12-01

Climate science provides an increasingly rich understanding of current and future climate, but this understanding is often not fully incorporated into climate adaptation decisions. In particular, the provision of climate information is still trapped in a narrow prediction-based framework, which envisions a sequential process that begins with model-based forecasts of future climate and decision makers then acting on those forecasts. Among its challenges, this framework can discourage action when climate predictions are deemed too uncertain, encourage overconfidence when climate scientists and decision makers fail to focus on decision-relevant but poorly understood extreme events, and offers a too-narrow communication path among climate scientists and decision makers. This talk will describe how robust decision approaches, organized around the idea of stress testing proposed adaptation decisions over a wide range of futures, can enable a richer flow information among climate scientists and decision makers. The talk illustrates these themes with two examples: 1) conservation management that explores the tradeoffs among alternative climate information products with different combinations of ensemble size and spatial resolution and 2) water quality implementation planning that focuses on the handling of extremes.

Age differences in neural correlates of feedback processing after economic decisions under risk.

PubMed

Fernandes, Carina; Pasion, Rita; Gonçalves, Ana R; Ferreira-Santos, Fernando; Barbosa, Fernando; Martins, Isabel P; Marques-Teixeira, João

2018-05-01

This study examines age-related differences in behavioral responses to risk and in the neurophysiological correlates of feedback processing. Our sample was composed of younger, middle-aged, and older adults, who were asked to decide between 2 risky options, in the gain and loss domains, during an EEG recording. Results evidenced group-related differences in early and later stages of feedback processing, indexed by differences in the feedback-related negativity (FRN) and P3 amplitudes. Specifically, in the loss domain, younger adults showed higher FRN amplitudes after non-losses than after losses, whereas middle-aged and older adults had similar FRN amplitudes after both. In the gain domain, younger and middle-aged adults had higher P3 amplitudes after gains than after non-gains, whereas older adults had similar P3 amplitudes after both. Behaviorally, older adults had higher rates of risky decisions than younger adults in the loss domain, a result that was correlated with poorer performance in memory and executive functions. Our results suggest age-related differences in the outcome-related expectations, as well as in the affective relevance attributed to the outcomes, which may underlie the group differences found in risk-aversion. Copyright © 2018 Elsevier Inc. All rights reserved.

Structural learning in feedforward and feedback control.

PubMed

Yousif, Nada; Diedrichsen, Jörn

2012-11-01

For smooth and efficient motor control, the brain needs to make fast corrections during the movement to resist possible perturbations. It also needs to adapt subsequent movements to improve future performance. It is important that both feedback corrections and feedforward adaptation need to be made based on noisy and often ambiguous sensory data. Therefore, the initial response of the motor system, both for online corrections and adaptive responses, is guided by prior assumptions about the likely structure of perturbations. In the context of correcting and adapting movements perturbed by a force field, we asked whether these priors are hard wired or whether they can be modified through repeated exposure to differently shaped force fields. We found that both feedback corrections to unexpected perturbations and feedforward adaptation to a new force field changed, such that they were appropriate to counteract the type of force field that participants had experienced previously. We then investigated whether these changes were driven by a common mechanism or by two separate mechanisms. Participants experienced force fields that were either temporally consistent, causing sustained adaptation, or temporally inconsistent, causing little overall adaptation. We found that the consistent force fields modified both feedback and feedforward responses. In contrast, the inconsistent force field modified the temporal shape of feedback corrections but not of the feedforward adaptive response. These results indicate that responses to force perturbations can be modified in a structural manner and that these modifications are at least partly dissociable for feedback and feedforward control.

Evaluation of the Display of Cognitive State Feedback to Drive Adaptive Task Sharing.

PubMed

Dorneich, Michael C; Passinger, Břetislav; Hamblin, Christopher; Keinrath, Claudia; Vašek, Jiři; Whitlow, Stephen D; Beekhuyzen, Martijn

2017-01-01

This paper presents an adaptive system intended to address workload imbalances between pilots in future flight decks. Team performance can be maximized when task demands are balanced within crew capabilities and resources. Good communication skills enable teams to adapt to changes in workload, and include the balancing of workload between team members This work addresses human factors priorities in the aviation domain with the goal to develop concepts that balance operator workload, support future operator roles and responsibilities, and support new task requirements, while allowing operators to focus on the most safety critical tasks. A traditional closed-loop adaptive system includes the decision logic to turn automated adaptations on and off. This work takes a novel approach of replacing the decision logic, normally performed by the automation, with human decisions. The Crew Workload Manager (CWLM) was developed to objectively display the workload between pilots and recommend task sharing; it is then the pilots who "close the loop" by deciding how to best mitigate unbalanced workload. The workload was manipulated by the Shared Aviation Task Battery (SAT-B), which was developed to provide opportunities for pilots to mitigate imbalances in workload between crew members. Participants were put in situations of high and low workload (i.e., workload was manipulated as opposed to being measured), the workload was then displayed to pilots, and pilots were allowed to decide how to mitigate the situation. An evaluation was performed that utilized the SAT-B to manipulate workload and create workload imbalances. Overall, the CWLM reduced the time spent in unbalanced workload and improved the crew coordination in task sharing while not negatively impacting concurrent task performance. Balancing workload has the potential to improve crew resource management and task performance over time, and reduce errors and fatigue. Paired with a real-time workload measurement system, the

Adaptation of a Knowledge-Based Decision-Support System in the Tactical Environment.

DTIC Science & Technology

1981-12-01

002-04-6411S1CURITY CL All PICATION OF 1,416 PAGE (00HIR Onto ea0aOW .L10 *GU9WVC 4bGSI.CAYON S. Voss 10466lVka t... OftesoE ’ making decisons . The...noe..aaw Ad tdlalttt’ IV 680011 MMib) Artificial Intelligence; Decision-Support Systems; Tactical Decision- making ; Knowledge-based Decision-support...tactical information to assist tactical commanders in making decisions. The system, TAC*, for "Tactical Adaptable Consultant," incorporates a database

Fuzzy Multicriteria Decision Analysis for Adaptive Watershed Management

NASA Astrophysics Data System (ADS)

Chang, N.

2006-12-01

The dramatic changes of societal complexity due to intensive interactions among agricultural, industrial, and municipal sectors have resulted in acute issues of water resources redistribution and water quality management in many river basins. Given the fact that integrated watershed management is more a political and societal than a technical challenge, there is a need for developing a compelling method leading to justify a water-based land use program in some critical regions. Adaptive watershed management is viewed as an indispensable tool nowadays for providing step-wise constructive decision support that is concerned with all related aspects of the water consumption cycle and those facilities affecting water quality and quantity temporally and spatially. Yet the greatest challenge that decision makers face today is to consider how to leverage ambiguity, paradox, and uncertainty to their competitive advantage of management policy quantitatively. This paper explores a fuzzy multicriteria evaluation method for water resources redistribution and subsequent water quality management with respect to a multipurpose channel-reservoir system--the Tseng- Wen River Basin, South Taiwan. Four fuzzy operators tailored for this fuzzy multicriteria decision analysis depict greater flexibility in representing the complexity of various possible trade-offs among management alternatives constrained by physical, economic, and technical factors essential for adaptive watershed management. The management strategies derived may enable decision makers to integrate a vast number of internal weirs, water intakes, reservoirs, drainage ditches, transfer pipelines, and wastewater treatment facilities within the basin and bring up the permitting issue for transboundary diversion from a neighboring river basin. Experience gained indicates that the use of different types of fuzzy operators is highly instructive, which also provide unique guidance collectively for achieving the overarching goals

Adapting Scott and Bruce's General Decision-Making Style Inventory to Patient Decision Making in Provider Choice.

PubMed

Fischer, Sophia; Soyez, Katja; Gurtner, Sebastian

2015-05-01

Research testing the concept of decision-making styles in specific contexts such as health care-related choices is missing. Therefore, we examine the contextuality of Scott and Bruce's (1995) General Decision-Making Style Inventory with respect to patient choice situations. Scott and Bruce's scale was adapted for use as a patient decision-making style inventory. In total, 388 German patients who underwent elective joint surgery responded to a questionnaire about their provider choice. Confirmatory factor analyses within 2 independent samples assessed factorial structure, reliability, and validity of the scale. The final 4-dimensional, 13-item patient decision-making style inventory showed satisfactory psychometric properties. Data analyses supported reliability and construct validity. Besides the intuitive, dependent, and avoidant style, a new subdimension, called "comparative" decision-making style, emerged that originated from the rational dimension of the general model. This research provides evidence for the contextuality of decision-making style to specific choice situations. Using a limited set of indicators, this report proposes the patient decision-making style inventory as valid and feasible tool to assess patients' decision propensities. © The Author(s) 2015.

Adaptive neural output-feedback control for nonstrict-feedback time-delay fractional-order systems with output constraints and actuator nonlinearities.

PubMed

Zouari, Farouk; Ibeas, Asier; Boulkroune, Abdesselem; Cao, Jinde; Mehdi Arefi, Mohammad

2018-06-01

This study addresses the issue of the adaptive output tracking control for a category of uncertain nonstrict-feedback delayed incommensurate fractional-order systems in the presence of nonaffine structures, unmeasured pseudo-states, unknown control directions, unknown actuator nonlinearities and output constraints. Firstly, the mean value theorem and the Gaussian error function are introduced to eliminate the difficulties that arise from the nonaffine structures and the unknown actuator nonlinearities, respectively. Secondly, the immeasurable tracking error variables are suitably estimated by constructing a fractional-order linear observer. Thirdly, the neural network, the Razumikhin Lemma, the variable separation approach, and the smooth Nussbaum-type function are used to deal with the uncertain nonlinear dynamics, the unknown time-varying delays, the nonstrict feedback and the unknown control directions, respectively. Fourthly, asymmetric barrier Lyapunov functions are employed to overcome the violation of the output constraints and to tune online the parameters of the adaptive neural controller. Through rigorous analysis, it is proved that the boundedness of all variables in the closed-loop system and the semi global asymptotic tracking are ensured without transgression of the constraints. The principal contributions of this study can be summarized as follows: (1) based on Caputo's definitions and new lemmas, methods concerning the controllability, observability and stability analysis of integer-order systems are extended to fractional-order ones, (2) the output tracking objective for a relatively large class of uncertain systems is achieved with a simple controller and less tuning parameters. Finally, computer-simulation studies from the robotic field are given to demonstrate the effectiveness of the proposed controller. Copyright © 2018 Elsevier Ltd. All rights reserved.

Interior Noise Reduction by Adaptive Feedback Vibration Control

NASA Technical Reports Server (NTRS)

Lim, Tae W.

1998-01-01

The objective of this project is to investigate the possible use of adaptive digital filtering techniques in simultaneous, multiple-mode identification of the modal parameters of a vibrating structure in real-time. It is intended that the results obtained from this project will be used for state estimation needed in adaptive structural acoustics control. The work done in this project is basically an extension of the work on real-time single mode identification, which was performed successfully using a digital signal processor (DSP) at NASA, Langley. Initially, in this investigation the single mode identification work was duplicated on a different processor, namely the Texas Instruments TMS32OC40 DSP. The system identification results for the single mode case were very good. Then an algorithm for simultaneous two mode identification was developed and tested using analytical simulation. When it successfully performed the expected tasks, it was implemented in real-time on the DSP system to identify the first two modes of vibration of a cantilever aluminum beam. The results of the simultaneous two mode case were good but some problems were identified related to frequency warping and spurious mode identification. The frequency warping problem was found to be due to the bilinear transformation used in the algorithm to convert the system transfer function from the continuous-time domain to the discrete-time domain. An alternative approach was developed to rectify the problem. The spurious mode identification problem was found to be associated with high sampling rates. Noise in the signal is suspected to be the cause of this problem but further investigation will be needed to clarify the cause. For simultaneous identification of more than two modes, it was found that theoretically an adaptive digital filter can be designed to identify the required number of modes, but the algebra became very complex which made it impossible to implement in the DSP system used in this study

Decision making in recurrent neuronal circuits.

PubMed

Wang, Xiao-Jing

2008-10-23

Decision making has recently emerged as a central theme in neurophysiological studies of cognition, and experimental and computational work has led to the proposal of a cortical circuit mechanism of elemental decision computations. This mechanism depends on slow recurrent synaptic excitation balanced by fast feedback inhibition, which not only instantiates attractor states for forming categorical choices but also long transients for gradually accumulating evidence in favor of or against alternative options. Such a circuit endowed with reward-dependent synaptic plasticity is able to produce adaptive choice behavior. While decision threshold is a core concept for reaction time tasks, it can be dissociated from a general decision rule. Moreover, perceptual decisions and value-based economic choices are described within a unified framework in which probabilistic choices result from irregular neuronal activity as well as iterative interactions of a decision maker with an uncertain environment or other unpredictable decision makers in a social group.

Adaptive Neural Output-Feedback Control for a Class of Nonlower Triangular Nonlinear Systems With Unmodeled Dynamics.

PubMed

Wang, Huanqing; Liu, Peter Xiaoping; Li, Shuai; Wang, Ding

2017-08-29

This paper presents the development of an adaptive neural controller for a class of nonlinear systems with unmodeled dynamics and immeasurable states. An observer is designed to estimate system states. The structure consistency of virtual control signals and the variable partition technique are combined to overcome the difficulties appearing in a nonlower triangular form. An adaptive neural output-feedback controller is developed based on the backstepping technique and the universal approximation property of the radial basis function (RBF) neural networks. By using the Lyapunov stability analysis, the semiglobally and uniformly ultimate boundedness of all signals within the closed-loop system is guaranteed. The simulation results show that the controlled system converges quickly, and all the signals are bounded. This paper is novel at least in the two aspects: 1) an output-feedback control strategy is developed for a class of nonlower triangular nonlinear systems with unmodeled dynamics and 2) the nonlinear disturbances and their bounds are the functions of all states, which is in a more general form than existing results.

Integrated Decision Support for Global Environmental Change Adaptation

NASA Astrophysics Data System (ADS)

Kumar, S.; Cantrell, S.; Higgins, G. J.; Marshall, J.; VanWijngaarden, F.

2011-12-01

Environmental changes are happening now that has caused concern in many parts of the world; particularly vulnerable are the countries and communities with limited resources and with natural environments that are more susceptible to climate change impacts. Global leaders are concerned about the observed phenomena and events such as Amazon deforestation, shifting monsoon patterns affecting agriculture in the mountain slopes of Peru, floods in Pakistan, water shortages in Middle East, droughts impacting water supplies and wildlife migration in Africa, and sea level rise impacts on low lying coastal communities in Bangladesh. These environmental changes are likely to get exacerbated as the temperatures rise, the weather and climate patterns change, and sea level rise continues. Large populations and billions of dollars of infrastructure could be affected. At Northrop Grumman, we have developed an integrated decision support framework for providing necessary information to stakeholders and planners to adapt to the impacts of climate variability and change at the regional and local levels. This integrated approach takes into account assimilation and exploitation of large and disparate weather and climate data sets, regional downscaling (dynamic and statistical), uncertainty quantification and reduction, and a synthesis of scientific data with demographic and economic data to generate actionable information for the stakeholders and decision makers. Utilizing a flexible service oriented architecture and state-of-the-art visualization techniques, this information can be delivered via tailored GIS portals to meet diverse set of user needs and expectations. This integrated approach can be applied to regional and local risk assessments, predictions and decadal projections, and proactive adaptation planning for vulnerable communities. In this paper we will describe this comprehensive decision support approach with selected applications and case studies to illustrate how this

Adaptation to Coriolis force perturbation of movement trajectory; role of proprioceptive and cutaneous somatosensory feedback

NASA Technical Reports Server (NTRS)

Lackner, James R.; DiZio, Paul

2002-01-01

Subjects exposed to constant velocity rotation in a large fully-enclosed room that rotates initially make large reaching errors in pointing to targets. The paths and endpoints of their reaches are deviated in the direction of the transient lateral Coriolis forces generated by the forward velocity of their reaches. With additional reaches, subjects soon reach in straighter paths and become more accurate at landing on target even in the absence of visual feedback about their movements. Two factors contribute to this adaptation: first, muscle spindle and golgi tendon organ feedback interpreted in relation to efferent commands provide information about movement trajectory, and second, somatosensory stimulation of the fingertip at the completion of a reach provides information about the location of the fingertip relative to the torso.

2D/3D video content adaptation decision engine based on content classification and user assessment

NASA Astrophysics Data System (ADS)

Fernandes, Rui; Andrade, M. T.

2017-07-01

Multimedia adaptation depends on several factors, such as the content itself, the consumption device and its characteristics, the transport and access networks and the user. An adaptation decision engine, in order to provide the best possible Quality of Experience to a user, needs to have information about all variables that may influence its decision. For the aforementioned factors, we implement content classification, define device classes, consider limited bandwidth scenarios and categorize user preferences based on a subjective quality evaluation test. The results of these actions generate vital information to pass to the adaptation decision engine so that its operation may provide the indication of the most suitable adaptation to perform that delivers the best possible outcome for the user under the existing constraints.

The Selection of Test Items for Decision Making with a Computer Adaptive Test.

ERIC Educational Resources Information Center

Spray, Judith A.; Reckase, Mark D.

The issue of test-item selection in support of decision making in adaptive testing is considered. The number of items needed to make a decision is compared for two approaches: selecting items from an item pool that are most informative at the decision point or selecting items that are most informative at the examinee's ability level. The first…

Neural network based adaptive output feedback control: Applications and improvements

NASA Astrophysics Data System (ADS)

Kutay, Ali Turker

Application of recently developed neural network based adaptive output feedback controllers to a diverse range of problems both in simulations and experiments is investigated in this thesis. The purpose is to evaluate the theory behind the development of these controllers numerically and experimentally, identify the needs for further development in practical applications, and to conduct further research in directions that are identified to ultimately enhance applicability of adaptive controllers to real world problems. We mainly focus our attention on adaptive controllers that augment existing fixed gain controllers. A recently developed approach holds great potential for successful implementations on real world applications due to its applicability to systems with minimal information concerning the plant model and the existing controller. In this thesis the formulation is extended to the multi-input multi-output case for distributed control of interconnected systems and successfully tested on a formation flight wind tunnel experiment. The command hedging method is formulated for the approach to further broaden the class of systems it can address by including systems with input nonlinearities. Also a formulation is adopted that allows the approach to be applied to non-minimum phase systems for which non-minimum phase characteristics are modeled with sufficient accuracy and treated properly in the design of the existing controller. It is shown that the approach can also be applied to augment nonlinear controllers under certain conditions and an example is presented where the nonlinear guidance law of a spinning projectile is augmented. Simulation results on a high fidelity 6 degrees-of-freedom nonlinear simulation code are presented. The thesis also presents a preliminary adaptive controller design for closed loop flight control with active flow actuators. Behavior of such actuators in dynamic flight conditions is not known. To test the adaptive controller design in

Structural learning in feedforward and feedback control

PubMed Central

Diedrichsen, Jörn

2012-01-01

For smooth and efficient motor control, the brain needs to make fast corrections during the movement to resist possible perturbations. It also needs to adapt subsequent movements to improve future performance. It is important that both feedback corrections and feedforward adaptation need to be made based on noisy and often ambiguous sensory data. Therefore, the initial response of the motor system, both for online corrections and adaptive responses, is guided by prior assumptions about the likely structure of perturbations. In the context of correcting and adapting movements perturbed by a force field, we asked whether these priors are hard wired or whether they can be modified through repeated exposure to differently shaped force fields. We found that both feedback corrections to unexpected perturbations and feedforward adaptation to a new force field changed, such that they were appropriate to counteract the type of force field that participants had experienced previously. We then investigated whether these changes were driven by a common mechanism or by two separate mechanisms. Participants experienced force fields that were either temporally consistent, causing sustained adaptation, or temporally inconsistent, causing little overall adaptation. We found that the consistent force fields modified both feedback and feedforward responses. In contrast, the inconsistent force field modified the temporal shape of feedback corrections but not of the feedforward adaptive response. These results indicate that responses to force perturbations can be modified in a structural manner and that these modifications are at least partly dissociable for feedback and feedforward control. PMID:22896725

Randomised prior feedback modulates neural signals of outcome monitoring.

PubMed

Mushtaq, Faisal; Wilkie, Richard M; Mon-Williams, Mark A; Schaefer, Alexandre

2016-01-15

Substantial evidence indicates that decision outcomes are typically evaluated relative to expectations learned from relatively long sequences of previous outcomes. This mechanism is thought to play a key role in general learning and adaptation processes but relatively little is known about the determinants of outcome evaluation when the capacity to learn from series of prior events is difficult or impossible. To investigate this issue, we examined how the feedback-related negativity (FRN) is modulated by information briefly presented before outcome evaluation. The FRN is a brain potential time-locked to the delivery of decision feedback and it is widely thought to be sensitive to prior expectations. We conducted a multi-trial gambling task in which outcomes at each trial were fully randomised to minimise the capacity to learn from long sequences of prior outcomes. Event-related potentials for outcomes (Win/Loss) in the current trial (Outcomet) were separated according to the type of outcomes that occurred in the preceding two trials (Outcomet-1 and Outcomet-2). We found that FRN voltage was more positive during the processing of win feedback when it was preceded by wins at Outcomet-1 compared to win feedback preceded by losses at Outcomet-1. However, no influence of preceding outcomes was found on FRN activity relative to the processing of loss feedback. We also found no effects of Outcomet-2 on FRN amplitude relative to current feedback. Additional analyses indicated that this effect was largest for trials in which participants selected a decision different to the gamble chosen in the previous trial. These findings are inconsistent with models that solely relate the FRN to prediction error computation. Instead, our results suggest that if stable predictions about future events are weak or non-existent, then outcome processing can be determined by affective systems. More specifically, our results indicate that the FRN is likely to reflect the activity of positive

Randomised prior feedback modulates neural signals of outcome monitoring

PubMed Central

Mushtaq, Faisal; Wilkie, Richard M.; Mon-Williams, Mark A.; Schaefer, Alexandre

2016-01-01

Substantial evidence indicates that decision outcomes are typically evaluated relative to expectations learned from relatively long sequences of previous outcomes. This mechanism is thought to play a key role in general learning and adaptation processes but relatively little is known about the determinants of outcome evaluation when the capacity to learn from series of prior events is difficult or impossible. To investigate this issue, we examined how the feedback-related negativity (FRN) is modulated by information briefly presented before outcome evaluation. The FRN is a brain potential time-locked to the delivery of decision feedback and it is widely thought to be sensitive to prior expectations. We conducted a multi-trial gambling task in which outcomes at each trial were fully randomised to minimise the capacity to learn from long sequences of prior outcomes. Event-related potentials for outcomes (Win/Loss) in the current trial (Outcomet) were separated according to the type of outcomes that occurred in the preceding two trials (Outcomet-1 and Outcomet-2). We found that FRN voltage was more positive during the processing of win feedback when it was preceded by wins at Outcomet-1 compared to win feedback preceded by losses at Outcomet-1. However, no influence of preceding outcomes was found on FRN activity relative to the processing of loss feedback. We also found no effects of Outcomet-2 on FRN amplitude relative to current feedback. Additional analyses indicated that this effect was largest for trials in which participants selected a decision different to the gamble chosen in the previous trial. These findings are inconsistent with models that solely relate the FRN to prediction error computation. Instead, our results suggest that if stable predictions about future events are weak or non-existent, then outcome processing can be determined by affective systems. More specifically, our results indicate that the FRN is likely to reflect the activity of positive

Correlations in state space can cause sub-optimal adaptation of optimal feedback control models.

PubMed

Aprasoff, Jonathan; Donchin, Opher

2012-04-01

Control of our movements is apparently facilitated by an adaptive internal model in the cerebellum. It was long thought that this internal model implemented an adaptive inverse model and generated motor commands, but recently many reject that idea in favor of a forward model hypothesis. In theory, the forward model predicts upcoming state during reaching movements so the motor cortex can generate appropriate motor commands. Recent computational models of this process rely on the optimal feedback control (OFC) framework of control theory. OFC is a powerful tool for describing motor control, it does not describe adaptation. Some assume that adaptation of the forward model alone could explain motor adaptation, but this is widely understood to be overly simplistic. However, an adaptive optimal controller is difficult to implement. A reasonable alternative is to allow forward model adaptation to 're-tune' the controller. Our simulations show that, as expected, forward model adaptation alone does not produce optimal trajectories during reaching movements perturbed by force fields. However, they also show that re-optimizing the controller from the forward model can be sub-optimal. This is because, in a system with state correlations or redundancies, accurate prediction requires different information than optimal control. We find that adding noise to the movements that matches noise found in human data is enough to overcome this problem. However, since the state space for control of real movements is far more complex than in our simple simulations, the effects of correlations on re-adaptation of the controller from the forward model cannot be overlooked.

Hybrid feedback feedforward: An efficient design of adaptive neural network control.

PubMed

Pan, Yongping; Liu, Yiqi; Xu, Bin; Yu, Haoyong

2016-04-01

This paper presents an efficient hybrid feedback feedforward (HFF) adaptive approximation-based control (AAC) strategy for a class of uncertain Euler-Lagrange systems. The control structure includes a proportional-derivative (PD) control term in the feedback loop and a radial-basis-function (RBF) neural network (NN) in the feedforward loop, which mimics the human motor learning control mechanism. At the presence of discontinuous friction, a sigmoid-jump-function NN is incorporated to improve control performance. The major difference of the proposed HFF-AAC design from the traditional feedback AAC (FB-AAC) design is that only desired outputs, rather than both tracking errors and desired outputs, are applied as RBF-NN inputs. Yet, such a slight modification leads to several attractive properties of HFF-AAC, including the convenient choice of an approximation domain, the decrease of the number of RBF-NN inputs, and semiglobal practical asymptotic stability dominated by control gains. Compared with previous HFF-AAC approaches, the proposed approach possesses the following two distinctive features: (i) all above attractive properties are achieved by a much simpler control scheme; (ii) the bounds of plant uncertainties are not required to be known. Consequently, the proposed approach guarantees a minimum configuration of the control structure and a minimum requirement of plant knowledge for the AAC design, which leads to a sharp decrease of implementation cost in terms of hardware selection, algorithm realization and system debugging. Simulation results have demonstrated that the proposed HFF-AAC can perform as good as or even better than the traditional FB-AAC under much simpler control synthesis and much lower computational cost. Copyright © 2015 Elsevier Ltd. All rights reserved.

Spacecraft Formation Flying near Sun-Earth L2 Lagrange Point: Trajectory Generation and Adaptive Full-State Feedback Control

NASA Technical Reports Server (NTRS)

Wong, Hong; Kapila, Vikram

2004-01-01

In this paper, we present a method for trajectory generation and adaptive full-state feedback control to facilitate spacecraft formation flying near the Sun-Earth L2 Lagrange point. Specifically, the dynamics of a spacecraft in the neighborhood of a Halo orbit reveals that there exist quasi-periodic orbits surrounding the Halo orbit. Thus, a spacecraft formation is created by placing a leader spacecraft on a desired Halo orbit and placing follower spacecraft on desired quasi-periodic orbits. To produce a formation maintenance controller, we first develop the nonlinear dynamics of a follower spacecraft relative to the leader spacecraft. We assume that the leader spacecraft is on a desired Halo orbit trajectory and the follower spacecraft is to track a desired quasi-periodic orbit surrounding the Halo orbit. Then, we design an adaptive, full-state feedback position tracking controller for the follower spacecraft providing an adaptive compensation for the unknown mass of the follower spacecraft. The proposed control law is simulated for the case of the leader and follower spacecraft pair and is shown to yield global, asymptotic convergence of the relative position tracking errors.

Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation.

PubMed

Peternel, Luka; Noda, Tomoyuki; Petrič, Tadej; Ude, Aleš; Morimoto, Jun; Babič, Jan

2016-01-01

In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion.

Adaptive Control of Exoskeleton Robots for Periodic Assistive Behaviours Based on EMG Feedback Minimisation

PubMed Central

Peternel, Luka; Noda, Tomoyuki; Petrič, Tadej; Ude, Aleš; Morimoto, Jun; Babič, Jan

2016-01-01

In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle activity as feedback to adapt the assistive joint torque behaviour in a way that the muscle activity is minimised. The user can then relax while the exoskeleton takes over the task execution. If the task is altered and the existing assistive behaviour becomes inadequate, the exoskeleton gradually adapts to the new task execution so that the increased muscle activity caused by the new desired task can be reduced. The advantage of the proposed method is that it does not require biomechanical or dynamical models. Our proposed learning system uses Dynamical Movement Primitives (DMPs) as a trajectory generator and parameters of DMPs are modulated using Locally Weighted Regression. Then, the learning system is combined with adaptive oscillators that determine the phase and frequency of motion according to measured Electromyography (EMG) signals. We tested the method with real robot experiments where subjects wearing an elbow exoskeleton had to move an object of an unknown mass according to a predefined reference motion. We further evaluated the proposed approach on a whole-arm exoskeleton to show that it is able to adaptively derive assistive torques even for multiple-joint motion. PMID:26881743

A framework to investigate drivers of adaptation decisions in marine fishing: Evidence from urban, semi-urban and rural communities.

PubMed

Malakar, Krishna; Mishra, Trupti; Patwardhan, Anand

2018-05-11

Traditional fishing livelihoods need to adapt to changing fish catch/populations, led by numerous anthropogenic, environmental and climatic stressors. The decision to adapt can be influenced by a variety of socio-economic and perceptual factors. However, adaptation decision-making in fishing communities has rarely been studied. Based on previous literature and focus group discussions with community, this study identifies few prominent adaptation responses in marine fishing and proposes credible factors driving decisions to adopt them. Further, a household survey is conducted, and the association of these drivers with various adaptation strategies is examined among fisherfolk of Maharashtra (India). This statistical analysis is based on 601 responses collected across three regional fishing groups: urban, semi-urban and rural. Regional segregation is done to understand variability in decision-making among groups which might be having different socio-economic and perceptual attributes. The survey reveals that only few urban fishing households have been able to diversify into other livelihoods. While having economic capital increases the likelihood of adaptation among urban and semi-urban communities, rural fishermen are significantly driven by social capital. Perception of climate change affecting fish catch drives adoption of mechanized boats solely in urban region. But increasing number of extreme events affects decisions of semi-urban and rural fishermen. Further, rising pollution and trade competition is associated with adaptation responses in the urban and semi-urban community. Higher education might help fishermen choose convenient forms of adaptation. Also, cooperative membership and subsidies are critical in adaptation decisions. The framework and insights of the study suggest the importance of acknowledging differential decision-making of individuals and communities, for designing effective adaptation and capacity-building policies. Copyright © 2018 Elsevier

Cooperative drought adaptation: Integrating infrastructure development, conservation, and water transfers into adaptive policy pathways

NASA Astrophysics Data System (ADS)

Zeff, Harrison B.; Herman, Jonathan D.; Reed, Patrick M.; Characklis, Gregory W.

2016-09-01

A considerable fraction of urban water supply capacity serves primarily as a hedge against drought. Water utilities can reduce their dependence on firm capacity and forestall the development of new supplies using short-term drought management actions, such as conservation and transfers. Nevertheless, new supplies will often be needed, especially as demands rise due to population growth and economic development. Planning decisions regarding when and how to integrate new supply projects are fundamentally shaped by the way in which short-term adaptive drought management strategies are employed. To date, the challenges posed by long-term infrastructure sequencing and adaptive short-term drought management are treated independently, neglecting important feedbacks between planning and management actions. This work contributes a risk-based framework that uses continuously updating risk-of-failure (ROF) triggers to capture the feedbacks between short-term drought management actions (e.g., conservation and water transfers) and the selection and sequencing of a set of regional supply infrastructure options over the long term. Probabilistic regional water supply pathways are discovered for four water utilities in the "Research Triangle" region of North Carolina. Furthermore, this study distinguishes the status-quo planning path of independent action (encompassing utility-specific conservation and new supply infrastructure only) from two cooperative formulations: "weak" cooperation, which combines utility-specific conservation and infrastructure development with regional transfers, and "strong" cooperation, which also includes jointly developed regional infrastructure to support transfers. Results suggest that strong cooperation aids utilities in meeting their individual objectives at substantially lower costs and with less overall development. These benefits demonstrate how an adaptive, rule-based decision framework can coordinate integrated solutions that would not be

Evaluation of the Display of Cognitive State Feedback to Drive Adaptive Task Sharing

PubMed Central

Dorneich, Michael C.; Passinger, Břetislav; Hamblin, Christopher; Keinrath, Claudia; Vašek, Jiři; Whitlow, Stephen D.; Beekhuyzen, Martijn

2017-01-01

This paper presents an adaptive system intended to address workload imbalances between pilots in future flight decks. Team performance can be maximized when task demands are balanced within crew capabilities and resources. Good communication skills enable teams to adapt to changes in workload, and include the balancing of workload between team members This work addresses human factors priorities in the aviation domain with the goal to develop concepts that balance operator workload, support future operator roles and responsibilities, and support new task requirements, while allowing operators to focus on the most safety critical tasks. A traditional closed-loop adaptive system includes the decision logic to turn automated adaptations on and off. This work takes a novel approach of replacing the decision logic, normally performed by the automation, with human decisions. The Crew Workload Manager (CWLM) was developed to objectively display the workload between pilots and recommend task sharing; it is then the pilots who “close the loop” by deciding how to best mitigate unbalanced workload. The workload was manipulated by the Shared Aviation Task Battery (SAT-B), which was developed to provide opportunities for pilots to mitigate imbalances in workload between crew members. Participants were put in situations of high and low workload (i.e., workload was manipulated as opposed to being measured), the workload was then displayed to pilots, and pilots were allowed to decide how to mitigate the situation. An evaluation was performed that utilized the SAT-B to manipulate workload and create workload imbalances. Overall, the CWLM reduced the time spent in unbalanced workload and improved the crew coordination in task sharing while not negatively impacting concurrent task performance. Balancing workload has the potential to improve crew resource management and task performance over time, and reduce errors and fatigue. Paired with a real-time workload measurement system

Performance feedback, self-esteem, and cardiovascular adaptation to recurring stressors.

PubMed

Brown, Eoin G; Creaven, Ann-Marie

2017-05-01

This study sought to examine the effects of performance feedback and individual differences in self-esteem on cardiovascular habituation to repeat stress exposure. Sixty-six university students (n = 39 female) completed a self-esteem measure and completed a cardiovascular stress-testing protocol involving repeated exposure to a mental arithmetic task. Cardiovascular functioning was sampled across four phases: resting baseline, initial stress exposure, a recovery period, and repeated stress exposure. Participants were randomly assigned to receive fictional positive feedback, negative feedback, or no feedback following the recovery period. Negative feedback was associated with a sensitized blood pressure response to a second exposure of the stress task. Positive feedback was associated with decreased cardiovascular and psychological responses to a second exposure. Self-esteem was also found to predict reactivity and this interacted with the type of feedback received. These findings suggest that negative performance feedback sensitizes cardiovascular reactivity to stress, whereas positive performance feedback increases both cardiovascular and psychological habituation to repeat exposure to stressors. Furthermore, an individual's self-esteem also appears to influence this process.

Adaptive Approximation-Based Regulation Control for a Class of Uncertain Nonlinear Systems Without Feedback Linearizability.

PubMed

Wang, Ning; Sun, Jing-Chao; Han, Min; Zheng, Zhongjiu; Er, Meng Joo

2017-09-06

In this paper, for a general class of uncertain nonlinear (cascade) systems, including unknown dynamics, which are not feedback linearizable and cannot be solved by existing approaches, an innovative adaptive approximation-based regulation control (AARC) scheme is developed. Within the framework of adding a power integrator (API), by deriving adaptive laws for output weights and prediction error compensation pertaining to single-hidden-layer feedforward network (SLFN) from the Lyapunov synthesis, a series of SLFN-based approximators are explicitly constructed to exactly dominate completely unknown dynamics. By the virtue of significant advancements on the API technique, an adaptive API methodology is eventually established in combination with SLFN-based adaptive approximators, and it contributes to a recursive mechanism for the AARC scheme. As a consequence, the output regulation error can asymptotically converge to the origin, and all other signals of the closed-loop system are uniformly ultimately bounded. Simulation studies and comprehensive comparisons with backstepping- and API-based approaches demonstrate that the proposed AARC scheme achieves remarkable performance and superiority in dealing with unknown dynamics.

LMI-based adaptive reliable H∞ static output feedback control against switched actuator failures

NASA Astrophysics Data System (ADS)

An, Liwei; Zhai, Ding; Dong, Jiuxiang; Zhang, Qingling

2017-08-01

This paper investigates the H∞ static output feedback (SOF) control problem for switched linear system under arbitrary switching, where the actuator failure models are considered to depend on switching signal. An active reliable control scheme is developed by combination of linear matrix inequality (LMI) method and adaptive mechanism. First, by exploiting variable substitution and Finsler's lemma, new LMI conditions are given for designing the SOF controller. Compared to the existing results, the proposed design conditions are more relaxed and can be applied to a wider class of no-fault linear systems. Then a novel adaptive mechanism is established, where the inverses of switched failure scaling factors are estimated online to accommodate the effects of actuator failure on systems. Two main difficulties arise: first is how to design the switched adaptive laws to prevent the missing of estimating information due to switching; second is how to construct a common Lyapunov function based on a switched estimate error term. It is shown that the new method can give less conservative results than that for the traditional control design with fixed gain matrices. Finally, simulation results on the HiMAT aircraft are given to show the effectiveness of the proposed approaches.

Adaptive Disturbance Tracking Theory with State Estimation and State Feedback for Region II Control of Large Wind Turbines

NASA Technical Reports Server (NTRS)

Balas, Mark J.; Thapa Magar, Kaman S.; Frost, Susan A.

2013-01-01

A theory called Adaptive Disturbance Tracking Control (ADTC) is introduced and used to track the Tip Speed Ratio (TSR) of 5 MW Horizontal Axis Wind Turbine (HAWT). Since ADTC theory requires wind speed information, a wind disturbance generator model is combined with lower order plant model to estimate the wind speed as well as partial states of the wind turbine. In this paper, we present a proof of stability and convergence of ADTC theory with lower order estimator and show that the state feedback can be adaptive.

Top-Down Modulation on Perceptual Decision with Balanced Inhibition through Feedforward and Feedback Inhibitory Neurons

PubMed Central

Wang, Cheng-Te; Lee, Chung-Ting; Wang, Xiao-Jing; Lo, Chung-Chuan

2013-01-01

Recent physiological studies have shown that neurons in various regions of the central nervous systems continuously receive noisy excitatory and inhibitory synaptic inputs in a balanced and covaried fashion. While this balanced synaptic input (BSI) is typically described in terms of maintaining the stability of neural circuits, a number of experimental and theoretical studies have suggested that BSI plays a proactive role in brain functions such as top-down modulation for executive control. Two issues have remained unclear in this picture. First, given the noisy nature of neuronal activities in neural circuits, how do the modulatory effects change if the top-down control implements BSI with different ratios between inhibition and excitation? Second, how is a top-down BSI realized via only excitatory long-range projections in the neocortex? To address the first issue, we systematically tested how the inhibition/excitation ratio affects the accuracy and reaction times of a spiking neural circuit model of perceptual decision. We defined an energy function to characterize the network dynamics, and found that different ratios modulate the energy function of the circuit differently and form two distinct functional modes. To address the second issue, we tested BSI with long-distance projection to inhibitory neurons that are either feedforward or feedback, depending on whether these inhibitory neurons do or do not receive inputs from local excitatory cells, respectively. We found that BSI occurs in both cases. Furthermore, when relying on feedback inhibitory neurons, through the recurrent interactions inside the circuit, BSI dynamically and automatically speeds up the decision by gradually reducing its inhibitory component in the course of a trial when a decision process takes too long. PMID:23626812

Top-down modulation on perceptual decision with balanced inhibition through feedforward and feedback inhibitory neurons.

PubMed

Wang, Cheng-Te; Lee, Chung-Ting; Wang, Xiao-Jing; Lo, Chung-Chuan

2013-01-01

Recent physiological studies have shown that neurons in various regions of the central nervous systems continuously receive noisy excitatory and inhibitory synaptic inputs in a balanced and covaried fashion. While this balanced synaptic input (BSI) is typically described in terms of maintaining the stability of neural circuits, a number of experimental and theoretical studies have suggested that BSI plays a proactive role in brain functions such as top-down modulation for executive control. Two issues have remained unclear in this picture. First, given the noisy nature of neuronal activities in neural circuits, how do the modulatory effects change if the top-down control implements BSI with different ratios between inhibition and excitation? Second, how is a top-down BSI realized via only excitatory long-range projections in the neocortex? To address the first issue, we systematically tested how the inhibition/excitation ratio affects the accuracy and reaction times of a spiking neural circuit model of perceptual decision. We defined an energy function to characterize the network dynamics, and found that different ratios modulate the energy function of the circuit differently and form two distinct functional modes. To address the second issue, we tested BSI with long-distance projection to inhibitory neurons that are either feedforward or feedback, depending on whether these inhibitory neurons do or do not receive inputs from local excitatory cells, respectively. We found that BSI occurs in both cases. Furthermore, when relying on feedback inhibitory neurons, through the recurrent interactions inside the circuit, BSI dynamically and automatically speeds up the decision by gradually reducing its inhibitory component in the course of a trial when a decision process takes too long.

Using social network analysis to evaluate health-related adaptation decision-making in Cambodia.

PubMed

Bowen, Kathryn J; Alexander, Damon; Miller, Fiona; Dany, Va

2014-01-30

Climate change adaptation in the health sector requires decisions across sectors, levels of government, and organisations. The networks that link these different institutions, and the relationships among people within these networks, are therefore critical influences on the nature of adaptive responses to climate change in the health sector. This study uses social network research to identify key organisational players engaged in developing health-related adaptation activities in Cambodia. It finds that strong partnerships are reported as developing across sectors and different types of organisations in relation to the health risks from climate change. Government ministries are influential organisations, whereas donors, development banks and non-government organisations do not appear to be as influential in the development of adaptation policy in the health sector. Finally, the study highlights the importance of informal partnerships (or 'shadow networks') in the context of climate change adaptation policy and activities. The health governance 'map' in relation to health and climate change adaptation that is developed in this paper is a novel way of identifying organisations that are perceived as key agents in the decision-making process, and it holds substantial benefits for both understanding and intervening in a broad range of climate change-related policy problems where collaboration is paramount for successful outcomes.

Using Social Network Analysis to Evaluate Health-Related Adaptation Decision-Making in Cambodia

PubMed Central

Bowen, Kathryn J.; Alexander, Damon; Miller, Fiona; Dany, Va

2014-01-01

Climate change adaptation in the health sector requires decisions across sectors, levels of government, and organisations. The networks that link these different institutions, and the relationships among people within these networks, are therefore critical influences on the nature of adaptive responses to climate change in the health sector. This study uses social network research to identify key organisational players engaged in developing health-related adaptation activities in Cambodia. It finds that strong partnerships are reported as developing across sectors and different types of organisations in relation to the health risks from climate change. Government ministries are influential organisations, whereas donors, development banks and non-government organisations do not appear to be as influential in the development of adaptation policy in the health sector. Finally, the study highlights the importance of informal partnerships (or ‘shadow networks’) in the context of climate change adaptation policy and activities. The health governance ‘map’ in relation to health and climate change adaptation that is developed in this paper is a novel way of identifying organisations that are perceived as key agents in the decision-making process, and it holds substantial benefits for both understanding and intervening in a broad range of climate change-related policy problems where collaboration is paramount for successful outcomes. PMID:24487452

A-Book: A Feedback-Based Adaptive System to Enhance Meta-Cognitive Skills during Reading.

PubMed

Guerra, Ernesto; Mellado, Guido

2017-01-01

In the digital era, tech devices (hardware and software) are increasingly within hand's reach. Yet, implementing information and communication technologies for educational contexts that have robust and long-lasting effects on student learning outcomes is still a challenge. We propose that any such system must a) be theoretically motivated and designed to tackle specific cognitive skills (e.g., inference making) supporting a given cognitive task (e.g., reading comprehension) and b) must be able to identify and adapt to the user's profile. In the present study, we implemented a feedback-based adaptive system called A-book (assisted-reading book) and tested it in a sample of 4th, 5th, and 6th graders. To assess our hypotheses, we contrasted three experimental assisted-reading conditions; one that supported meta-cognitive skills and adapted to the user profile (adaptive condition), one that supported meta-cognitive skills but did not adapt to the user profile (training condition) and a control condition. The results provide initial support for our proposal; participants in the adaptive condition improved their accuracy scores on inference making questions over time, outperforming both the training and control groups. There was no evidence, however, of significant improvements on other tested meta-cognitive skills (i.e., text structure knowledge, comprehension monitoring). We discussed the practical implications of using the A-book for the enhancement of meta-cognitive skills in school contexts, as well as its current limitations and future developments that could improve the system.

Adaptive Fuzzy Output Constrained Control Design for Multi-Input Multioutput Stochastic Nonstrict-Feedback Nonlinear Systems.

PubMed

Li, Yongming; Tong, Shaocheng

2017-12-01

In this paper, an adaptive fuzzy output constrained control design approach is addressed for multi-input multioutput uncertain stochastic nonlinear systems in nonstrict-feedback form. The nonlinear systems addressed in this paper possess unstructured uncertainties, unknown gain functions and unknown stochastic disturbances. Fuzzy logic systems are utilized to tackle the problem of unknown nonlinear uncertainties. The barrier Lyapunov function technique is employed to solve the output constrained problem. In the framework of backstepping design, an adaptive fuzzy control design scheme is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example.

Design and test of a Microsoft Kinect-based system for delivering adaptive visual feedback to stroke patients during training of upper limb movement.

PubMed

Simonsen, Daniel; Popovic, Mirjana B; Spaich, Erika G; Andersen, Ole Kæseler

2017-11-01

The present paper describes the design and test of a low-cost Microsoft Kinect-based system for delivering adaptive visual feedback to stroke patients during the execution of an upper limb exercise. Eleven sub-acute stroke patients with varying degrees of upper limb function were recruited. Each subject participated in a control session (repeated twice) and a feedback session (repeated twice). In each session, the subjects were presented with a rectangular pattern displayed on a vertical mounted monitor embedded in the table in front of the patient. The subjects were asked to move a marker inside the rectangular pattern by using their most affected hand. During the feedback session, the thickness of the rectangular pattern was changed according to the performance of the subject, and the color of the marker changed according to its position, thereby guiding the subject's movements. In the control session, the thickness of the rectangular pattern and the color of the marker did not change. The results showed that the movement similarity and smoothness was higher in the feedback session than in the control session while the duration of the movement was longer. The present study showed that adaptive visual feedback delivered by use of the Kinect sensor can increase the similarity and smoothness of upper limb movement in stroke patients.

Locally adaptive decision in detection of clustered microcalcifications in mammograms.

PubMed

Sainz de Cea, María V; Nishikawa, Robert M; Yang, Yongyi

2018-02-15

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  <10 -4 ). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

Locally adaptive decision in detection of clustered microcalcifications in mammograms

NASA Astrophysics Data System (ADS)

Sainz de Cea, María V.; Nishikawa, Robert M.; Yang, Yongyi

2018-02-01

In computer-aided detection or diagnosis of clustered microcalcifications (MCs) in mammograms, the performance often suffers from not only the presence of false positives (FPs) among the detected individual MCs but also large variability in detection accuracy among different cases. To address this issue, we investigate a locally adaptive decision scheme in MC detection by exploiting the noise characteristics in a lesion area. Instead of developing a new MC detector, we propose a decision scheme on how to best decide whether a detected object is an MC or not in the detector output. We formulate the individual MCs as statistical outliers compared to the many noisy detections in a lesion area so as to account for the local image characteristics. To identify the MCs, we first consider a parametric method for outlier detection, the Mahalanobis distance detector, which is based on a multi-dimensional Gaussian distribution on the noisy detections. We also consider a non-parametric method which is based on a stochastic neighbor graph model of the detected objects. We demonstrated the proposed decision approach with two existing MC detectors on a set of 188 full-field digital mammograms (95 cases). The results, evaluated using free response operating characteristic (FROC) analysis, showed a significant improvement in detection accuracy by the proposed outlier decision approach over traditional thresholding (the partial area under the FROC curve increased from 3.95 to 4.25, p-value  <10-4). There was also a reduction in case-to-case variability in detected FPs at a given sensitivity level. The proposed adaptive decision approach could not only reduce the number of FPs in detected MCs but also improve case-to-case consistency in detection.

Examining Challenges Related to the Production of Actionable Climate Knowledge for Adaptation Decision-Making: A Focus on Climate Knowledge System Producers

NASA Astrophysics Data System (ADS)

Ernst, K.; Preston, B. L.; Tenggren, S.; Klein, R.; Gerger-Swartling, Å.

2017-12-01

Many challenges to adaptation decision-making and action have been identified across peer-reviewed and gray literature. These challenges have primarily focused on the use of climate knowledge for adaptation decision-making, the process of adaptation decision-making, and the needs of the decision-maker. Studies on climate change knowledge systems often discuss the imperative role of climate knowledge producers in adaptation decision-making processes and stress the need for producers to engage in knowledge co-production activities and to more effectively meet decision-maker needs. While the influence of climate knowledge producers on the co-production of science for adaptation decision-making is well-recognized, hardly any research has taken a direct approach to analyzing the challenges that climate knowledge producers face when undertaking science co-production. Those challenges can influence the process of knowledge production and may hinder the creation, utilization, and dissemination of actionable knowledge for adaptation decision-making. This study involves semi-structured interviews, focus groups, and participant observations to analyze, identify, and contextualize the challenges that climate knowledge producers in Sweden face as they endeavor to create effective climate knowledge systems for multiple contexts, scales, and levels across the European Union. Preliminary findings identify complex challenges related to education, training, and support; motivation, willingness, and culture; varying levels of prioritization; professional roles and responsibilities; the type and amount of resources available; and professional incentive structures. These challenges exist at varying scales and levels across individuals, organizations, networks, institutions, and disciplines. This study suggests that the creation of actionable knowledge for adaptation decision-making is not supported across scales and levels in the climate knowledge production landscape. Additionally

Nonlinear dynamics of team performance and adaptability in emergency response.

PubMed

Guastello, Stephen J

2010-04-01

The impact of team size and performance feedback on adaptation levels and performance of emergency response (ER) teams was examined to introduce a metric for quantifying adaptation levels based on nonlinear dynamical systems (NDS) theory. NDS principles appear in reports surrounding Hurricane Katrina, earthquakes, floods, a disease epidemic, and the Southeast Asian tsunami. They are also intrinsic to coordination within teams, adaptation levels, and performance in dynamic decision processes. Performance was measured in a dynamic decision task in which ER teams of different sizes worked against an attacker who was trying to destroy a city (total N = 225 undergraduates). The complexity of teams' and attackers' adaptation strategies and the role of the opponents' performance were assessed by nonlinear regression analysis. An optimal group size for team performance was identified. Teams were more readily influenced by the attackers' performance than vice versa. The adaptive capabilities of attackers and teams were impaired by their opponents in some conditions. ER teams should be large enough to contribute a critical mass of ideas but not so large that coordination would be compromised. ER teams used self-organized strategies that could have been more adaptive, whereas attackers used chaotic strategies. The model and results are applicable to ER processes or training maneuvers involving dynamic decisions but could be limited to nonhierarchical groups.

The Business of Co-Production: Assessing Efforts to Bridge Science and Decision-Making for Adaptation in California

NASA Astrophysics Data System (ADS)

Webber, S.; MacDonald, G. M.

2016-12-01

The last decades have seen scholars argue for a greater integration of science and decision-making in order to more effectively respond to climate change. It has been suggested that overcoming the gap between science, on the one hand, and policy-making and management, on the other, requires building bridges through methods of co-production, creating actionable science, or through boundary organizations. In this paper, we review attempts at co-production for policy-making and management in the context of climate change adaptation in California. Building on field research, including numerous interviews conducted with scientists and decision-makers who are co-producers of adaptation projects, we make three arguments. First, we show that an emphasis on co-production and science-informed climate change adaptation decision-making has bolstered a contract-oriented, and decentralized network-based model of producing climate science. Second, reviewing successes and failures in co-production - as reported in interviews - indicates that it is principally in cases of neatly defined, and spatially and temporarily narrow decision-making contexts, and with highly motivated decision-makers, that climate science is used. Finally, we suggest that the ideas of co-production and actionable science may have increased the institutional and organizational burden at the science-decision interface, lengthening the boundary-organization-chain rather than necessarily facilitating adaptive policy-making and management.

Adaptive restoration of a partially coherent blurred image using an all-optical feedback interferometer with a liquid-crystal device.

PubMed

Shirai, Tomohiro; Barnes, Thomas H

2002-02-01

A liquid-crystal adaptive optics system using all-optical feedback interferometry is applied to partially coherent imaging through a phase disturbance. A theoretical analysis based on the propagation of the cross-spectral density shows that the blurred image due to the phase disturbance can be restored, in principle, irrespective of the state of coherence of the light illuminating the object. Experimental verification of the theory has been performed for two cases when the object to be imaged is illuminated by spatially coherent light originating from a He-Ne laser and by spatially incoherent white light from a halogen lamp. We observed in both cases that images blurred by the phase disturbance were successfully restored, in agreement with the theory, immediately after the adaptive optics system was activated. The origin of the deviation of the experimental results from the theory, together with the effect of the feedback misalignment inherent in our optical arrangement, is also discussed.

Adaptive Control Model Reveals Systematic Feedback and Key Molecules in Metabolic Pathway Regulation

PubMed Central

Moffitt, Richard A.; Merrill, Alfred H.; Wang, May D.

2011-01-01

Abstract Robust behavior in metabolic pathways resembles stabilized performance in systems under autonomous control. This suggests we can apply control theory to study existing regulation in these cellular networks. Here, we use model-reference adaptive control (MRAC) to investigate the dynamics of de novo sphingolipid synthesis regulation in a combined theoretical and experimental case study. The effects of serine palmitoyltransferase over-expression on this pathway are studied in vitro using human embryonic kidney cells. We report two key results from comparing numerical simulations with observed data. First, MRAC simulations of pathway dynamics are comparable to simulations from a standard model using mass action kinetics. The root-sum-square (RSS) between data and simulations in both cases differ by less than 5%. Second, MRAC simulations suggest systematic pathway regulation in terms of adaptive feedback from individual molecules. In response to increased metabolite levels available for de novo sphingolipid synthesis, feedback from molecules along the main artery of the pathway is regulated more frequently and with greater amplitude than from other molecules along the branches. These biological insights are consistent with current knowledge while being new that they may guide future research in sphingolipid biology. In summary, we report a novel approach to study regulation in cellular networks by applying control theory in the context of robust metabolic pathways. We do this to uncover potential insight into the dynamics of regulation and the reverse engineering of cellular networks for systems biology. This new modeling approach and the implementation routines designed for this case study may be extended to other systems. Supplementary Material is available at www.liebertonline.com/cmb. PMID:21314456

Self-reinnervated muscles lose autogenic length feedback, but intermuscular feedback can recover functional connectivity

PubMed Central

Prilutsky, Boris I.; Gregor, Robert J.; Abelew, Thomas A.; Nichols, T. Richard

2016-01-01

In this study, we sought to identify sensory circuitry responsible for motor deficits or compensatory adaptations after peripheral nerve cut and repair. Self-reinnervation of the ankle extensor muscles abolishes the stretch reflex and increases ankle yielding during downslope walking, but it remains unknown whether this finding generalizes to other muscle groups and whether muscles become completely deafferented. In decerebrate cats at least 19 wk after nerve cut and repair, we examined the influence of quadriceps (Q) muscles' self-reinnervation on autogenic length feedback, as well as intermuscular length and force feedback, among the primary extensor muscles in the cat hindlimb. Effects of gastrocnemius and soleus self-reinnervation on intermuscular circuitry were also evaluated. We found that autogenic length feedback was lost after Q self-reinnervation, indicating that loss of the stretch reflex appears to be a generalizable consequence of muscle self-reinnervation. However, intermuscular force and length feedback, evoked from self-reinnervated muscles, was preserved in most of the interactions evaluated with similar relative inhibitory or excitatory magnitudes. These data indicate that intermuscular spinal reflex circuitry has the ability to regain functional connectivity, but the restoration is not absolute. Explanations for the recovery of intermuscular feedback are discussed, based on identified mechanisms responsible for lost autogenic length feedback. Functional implications, due to permanent loss of autogenic length feedback and potential for compensatory adaptations from preserved intermuscular feedback, are discussed. PMID:27306676

Adaptive limited feedback for interference alignment in MIMO interference channels.

PubMed

Zhang, Yang; Zhao, Chenglin; Meng, Juan; Li, Shibao; Li, Li

2016-01-01

It is very important that the radar sensor network has autonomous capabilities such as self-managing, etc. Quite often, MIMO interference channels are applied to radar sensor networks, and for self-managing purpose, interference management in MIMO interference channels is critical. Interference alignment (IA) has the potential to dramatically improve system throughput by effectively mitigating interference in multi-user networks at high signal-to-noise (SNR). However, the implementation of IA predominantly relays on perfect and global channel state information (CSI) at all transceivers. A large amount of CSI has to be fed back to all transmitters, resulting in a proliferation of feedback bits. Thus, IA with limited feedback has been introduced to reduce the sum feedback overhead. In this paper, by exploiting the advantage of heterogeneous path loss, we first investigate the throughput of IA with limited feedback in interference channels while each user transmits multi-streams simultaneously, then we get the upper bound of sum rate in terms of the transmit power and feedback bits. Moreover, we propose a dynamic feedback scheme via bit allocation to reduce the throughput loss due to limited feedback. Simulation results demonstrate that the dynamic feedback scheme achieves better performance in terms of sum rate.

Keys to success for data-driven decision making: Lessons from participatory monitoring and collaborative adaptive management

USDA-ARS?s Scientific Manuscript database

Recent years have witnessed a call for evidence-based decisions in conservation and natural resource management, including data-driven decision-making. Adaptive management (AM) is one prevalent model for integrating scientific data into decision-making, yet AM has faced numerous challenges and limit...

Approximation-based adaptive tracking control of pure-feedback nonlinear systems with multiple unknown time-varying delays.

PubMed

Wang, Min; Ge, Shuzhi Sam; Hong, Keum-Shik

2010-11-01

This paper presents adaptive neural tracking control for a class of non-affine pure-feedback systems with multiple unknown state time-varying delays. To overcome the design difficulty from non-affine structure of pure-feedback system, mean value theorem is exploited to deduce affine appearance of state variables x(i) as virtual controls α(i), and of the actual control u. The separation technique is introduced to decompose unknown functions of all time-varying delayed states into a series of continuous functions of each delayed state. The novel Lyapunov-Krasovskii functionals are employed to compensate for the unknown functions of current delayed state, which is effectively free from any restriction on unknown time-delay functions and overcomes the circular construction of controller caused by the neural approximation of a function of u and [Formula: see text] . Novel continuous functions are introduced to overcome the design difficulty deduced from the use of one adaptive parameter. To achieve uniformly ultimate boundedness of all the signals in the closed-loop system and tracking performance, control gains are effectively modified as a dynamic form with a class of even function, which makes stability analysis be carried out at the present of multiple time-varying delays. Simulation studies are provided to demonstrate the effectiveness of the proposed scheme.

Cooperative Solutions in Multi-Person Quadratic Decision Problems: Finite-Horizon and State-Feedback Cost-Cumulant Control Paradigm

DTIC Science & Technology

2007-01-01

CONTRACT NUMBER Problems: Finite -Horizon and State-Feedback Cost-Cumulant Control Paradigm (PREPRINT) 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER...cooperative cost-cumulant control regime for the class of multi-person single-objective decision problems characterized by quadratic random costs and... finite -horizon integral quadratic cost associated with a linear stochastic system . Since this problem formation is parameterized by the number of cost

Real-time tracking control of electro-hydraulic force servo systems using offline feedback control and adaptive control.

PubMed

Shen, Gang; Zhu, Zhencai; Zhao, Jinsong; Zhu, Weidong; Tang, Yu; Li, Xiang

2017-03-01

This paper focuses on an application of an electro-hydraulic force tracking controller combined with an offline designed feedback controller (ODFC) and an online adaptive compensator in order to improve force tracking performance of an electro-hydraulic force servo system (EHFS). A proportional-integral controller has been employed and a parameter-based force closed-loop transfer function of the EHFS is identified by a continuous system identification algorithm. By taking the identified system model as a nominal plant model, an H ∞ offline design method is employed to establish an optimized feedback controller with consideration of the performance, control efforts, and robustness of the EHFS. In order to overcome the disadvantage of the offline designed controller and cope with the varying dynamics of the EHFS, an online adaptive compensator with a normalized least-mean-square algorithm is cascaded to the force closed-loop system of the EHFS compensated by the ODFC. Some comparative experiments are carried out on a real-time EHFS using an xPC rapid prototype technology, and the proposed controller yields a better force tracking performance improvement. Copyright © 2016. Published by Elsevier Ltd.

Impact of web searching and social feedback on consumer decision making: a prospective online experiment.

PubMed

Lau, Annie Y S; Coiera, Enrico W

2008-01-22

The World Wide Web has increasingly become an important source of information in health care consumer decision making. However, little is known about whether searching online resources actually improves consumers' understanding of health issues. The aim was to study whether searching on the World Wide Web improves consumers' accuracy in answering health questions and whether consumers' understanding of health issues is subject to further change under social feedback. This was a pre/post prospective online study. A convenience sample of 227 undergraduate students was recruited from the population of the University of New South Wales. Subjects used a search engine that retrieved online documents from PubMed, MedlinePlus, and HealthInsite and answered a set of six questions (before and after use of the search engine) designed for health care consumers. They were then presented with feedback consisting of a summary of the post-search answers provided by previous subjects for the same questions and were asked to answer the questions again. There was an improvement in the percentage of correct answers after searching (pre-search 61.2% vs post-search 82.0%, P <.001) and after feedback with other subjects' answers (pre-feedback 82.0% vs post-feedback 85.3%, P =.051). The proportion of subjects with highly confident correct answers (ie, confident or very confident) and the proportion with highly confident incorrect answers significantly increased after searching (correct pre-search 61.6% vs correct post-search 95.5%, P <.001; incorrect pre-search 55.3% vs incorrect post-search 82.0%, P <.001). Subjects who were not as confident in their post-search answers were 28.5% more likely than those who were confident or very confident to change their answer after feedback with other subjects' post-search answers (chi(2) (1)= 66.65, P <.001). Searching across quality health information sources on the Web can improve consumers' accuracy in answering health questions. However, a

Optimizing Decision Preparedness by Adapting Scenario Complexity and Automating Scenario Generation

NASA Technical Reports Server (NTRS)

Dunne, Rob; Schatz, Sae; Flore, Stephen M.; Nicholson, Denise

2011-01-01

Klein's recognition-primed decision (RPD) framework proposes that experts make decisions by recognizing similarities between current decision situations and previous decision experiences. Unfortunately, military personnel arQ often presented with situations that they have not experienced before. Scenario-based training (S8T) can help mitigate this gap. However, SBT remains a challenging and inefficient training approach. To address these limitations, the authors present an innovative formulation of scenario complexity that contributes to the larger research goal of developing an automated scenario generation system. This system will enable trainees to effectively advance through a variety of increasingly complex decision situations and experiences. By adapting scenario complexities and automating generation, trainees will be provided with a greater variety of appropriately calibrated training events, thus broadening their repositories of experience. Preliminary results from empirical testing (N=24) of the proof-of-concept formula are presented, and future avenues of scenario complexity research are also discussed.

Feedback produces divergence from prospect theory in descriptive choice.

PubMed

Jessup, Ryan K; Bishara, Anthony J; Busemeyer, Jerome R

2008-10-01

A recent study demonstrated that individuals making experience-based choices underweight small probabilities, in contrast to the overweighting observed in a typical descriptive paradigm. We tested whether trial-by-trial feedback in a repeated descriptive paradigm would engender choices more correspondent with experiential or descriptive paradigms. The results of a repeated gambling task indicated that individuals receiving feedback underweighted small probabilities, relative to their no-feedback counterparts. These results implicate feedback as a critical component during the decision-making process, even in the presence of fully specified descriptive information. A model comparison at the individual-subject level suggested that feedback drove individuals' decision weights toward objective probability weighting.

Indirect adaptive output feedback control of a biorobotic AUV using pectoral-like mechanical fins.

PubMed

Naik, Mugdha S; Singh, Sahjendra N; Mittal, Rajat

2009-06-01

This paper treats the question of servoregulation of autonomous underwater vehicles (AUVs) in the yaw plane using pectoral-like mechanical fins. The fins attached to the vehicle have oscillatory swaying and yawing motion. The bias angle of the angular motion of the fin is used for the purpose of control. Of course, the design approach considered here is applicable to AUVs for other choices of oscillation patterns of the fins, which produce periodic forces and moments. It is assumed that the vehicle parameters, hydrodynamic coefficients, as well the fin forces and moments are unknown. For the trajectory control of the yaw angle, a sampled-data indirect adaptive control system using output (yaw angle) feedback is derived. The control system has a modular structure, which includes a parameter identifier and a stabilizer. For the control law derivation, an internal model of the exosignals (reference signal (constant or ramp) and constant disturbance) is included. Unlike the direct adaptive control scheme, the derived control law is applicable to minimum as well as nonminimum phase biorobotic AUVs (BAUVs). This is important, because for most of the fin locations on the vehicle, the model is a nonminimum phase. In the closed-loop system, the yaw angle trajectory tracking error converges to zero and the remaining state variables remain bounded. Simulation results are presented which show that the derived modular control system accomplishes precise set point yaw angle control and turning maneuvers in spite of the uncertainties in the system parameters using only yaw angle feedback.

Feedback-related brain activity predicts learning from feedback in multiple-choice testing.

PubMed

Ernst, Benjamin; Steinhauser, Marco

2012-06-01

Different event-related potentials (ERPs) have been shown to correlate with learning from feedback in decision-making tasks and with learning in explicit memory tasks. In the present study, we investigated which ERPs predict learning from corrective feedback in a multiple-choice test, which combines elements from both paradigms. Participants worked through sets of multiple-choice items of a Swahili-German vocabulary task. Whereas the initial presentation of an item required the participants to guess the answer, corrective feedback could be used to learn the correct response. Initial analyses revealed that corrective feedback elicited components related to reinforcement learning (FRN), as well as to explicit memory processing (P300) and attention (early frontal positivity). However, only the P300 and early frontal positivity were positively correlated with successful learning from corrective feedback, whereas the FRN was even larger when learning failed. These results suggest that learning from corrective feedback crucially relies on explicit memory processing and attentional orienting to corrective feedback, rather than on reinforcement learning.

Decentralized Adaptive Neural Output-Feedback DSC for Switched Large-Scale Nonlinear Systems.

PubMed

Lijun Long; Jun Zhao

2017-04-01

In this paper, for a class of switched large-scale uncertain nonlinear systems with unknown control coefficients and unmeasurable states, a switched-dynamic-surface-based decentralized adaptive neural output-feedback control approach is developed. The approach proposed extends the classical dynamic surface control (DSC) technique for nonswitched version to switched version by designing switched first-order filters, which overcomes the problem of multiple "explosion of complexity." Also, a dual common coordinates transformation of all subsystems is exploited to avoid individual coordinate transformations for subsystems that are required when applying the backstepping recursive design scheme. Nussbaum-type functions are utilized to handle the unknown control coefficients, and a switched neural network observer is constructed to estimate the unmeasurable states. Combining with the average dwell time method and backstepping and the DSC technique, decentralized adaptive neural controllers of subsystems are explicitly designed. It is proved that the approach provided can guarantee the semiglobal uniformly ultimately boundedness for all the signals in the closed-loop system under a class of switching signals with average dwell time, and the tracking errors to a small neighborhood of the origin. A two inverted pendulums system is provided to demonstrate the effectiveness of the method proposed.

Adaptive NN control for discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints.

PubMed

Chen, Weisheng

2009-07-01

This paper focuses on the problem of adaptive neural network tracking control for a class of discrete-time pure-feedback systems with unknown control direction under amplitude and rate actuator constraints. Two novel state-feedback and output-feedback dynamic control laws are established where the function tanh(.) is employed to solve the saturation constraint problem. Implicit function theorem and mean value theorem are exploited to deal with non-affine variables that are used as actual control. Radial basis function neural networks are used to approximate the desired input function. Discrete Nussbaum gain is used to estimate the unknown sign of control gain. The uniform boundedness of all closed-loop signals is guaranteed. The tracking error is proved to converge to a small residual set around the origin. A simulation example is provided to illustrate the effectiveness of control schemes proposed in this paper.

Social closeness and feedback modulate susceptibility to the framing effect.

PubMed

Sip, Kamila E; Smith, David V; Porcelli, Anthony J; Kar, Kohitij; Delgado, Mauricio R

2015-01-01

Although we often seek social feedback (SFB) from others to help us make decisions, little is known about how SFB affects decisions under risk, particularly from a close peer. We conducted two experiments using an established framing task to probe how decision-making is modulated by SFB valence (positive, negative) and the level of closeness with feedback provider (friend, confederate). Participants faced mathematically equivalent decisions framed as either an opportunity to keep (gain frame) or lose (loss frame) part of an initial endowment. Periodically, participants were provided with positive (e.g., "Nice!") or negative (e.g., "Lame!") feedback about their choices. Such feedback was provided by either a confederate (Experiment 1) or a gender-matched close friend (Experiment 2). As expected, the framing effect was observed in both experiments. Critically, an individual's susceptibility to the framing effect was modulated by the valence of the SFB, but only when the feedback provider was a close friend. This effect was reflected in the activation patterns of ventromedial prefrontal cortex and posterior cingulate cortex, regions involved in complex decision-making. Taken together, these results highlight social closeness as an important factor in understanding the impact of SFB on neural mechanisms of decision-making.

Feedback in Videogame-Based Adaptive Training

ERIC Educational Resources Information Center

Rivera, Iris Daliz

2010-01-01

The field of training has been changing rapidly due to advances in technology such as videogame-based adaptive training. Videogame-based adaptive training has provided flexibility and adaptability for training in cost-effective ways. Although this method of training may have many benefits for the trainee, current research has not kept up to pace…

Experimental Evaluation of Adaptive Modulation and Coding in MIMO WiMAX with Limited Feedback

NASA Astrophysics Data System (ADS)

Mehlführer, Christian; Caban, Sebastian; Rupp, Markus

2007-12-01

We evaluate the throughput performance of an OFDM WiMAX (IEEE 802.16-2004, Section 8.3) transmission system with adaptive modulation and coding (AMC) by outdoor measurements. The standard compliant AMC utilizes a 3-bit feedback for SISO and Alamouti coded MIMO transmissions. By applying a 6-bit feedback and spatial multiplexing with individual AMC on the two transmit antennas, the data throughput can be increased significantly for large SNR values. Our measurements show that at small SNR values, a single antenna transmission often outperforms an Alamouti transmission. We found that this effect is caused by the asymmetric behavior of the wireless channel and by poor channel knowledge in the two-transmit-antenna case. Our performance evaluation is based on a measurement campaign employing the Vienna MIMO testbed. The measurement scenarios include typical outdoor-to-indoor NLOS, outdoor-to-outdoor NLOS, as well as outdoor-to-indoor LOS connections. We found that in all these scenarios, the measured throughput is far from its achievable maximum; the loss is mainly caused by a too simple convolutional coding.

How Does the Brain Implement Adaptive Decision Making to Eat?

PubMed Central

Walsh, B. Timothy; Kaye, Walter; Geliebter, Allan

2015-01-01

Adaptive decision making to eat is crucial for survival, but in anorexia nervosa, the brain persistently supports reduced food intake despite a growing need for energy. How the brain persists in reducing food intake, sometimes even to the point of death and despite the evolution of multiple mechanisms to ensure survival by governing adaptive eating behaviors, remains mysterious. Neural substrates belong to the reward-habit system, which could differ among the eating disorders. The present review provides an overview of neural circuitry of restrictive food choice, binge eating, and the contribution of specific serotonin receptors. One possibility is that restrictive food intake critically engages goal-directed (decision making) systems and “habit,” supporting the view that persistent caloric restriction mimics some aspects of addiction to drugs of abuse. SIGNIFICANCE STATEMENT An improved understanding of the neural basis of eating disorders is a timely challenge because these disorders can be deadly. Up to 70 million of people in the world suffer from eating disorders. Anorexia nervosa affects 1–4% of women in United States and is the first cause of death among adolescents in Europe. Studies relying on animal models suggest that decision making to eat (or not) can prevail over actual energy requirements due to emotional disturbances resulting in abnormal habitual behavior, mimicking dependence. These recent studies provide a foundation for developing more specific and effective interventions for these disorders. PMID:26468187

Coastal Adaptation Planning for Sea Level Rise and Extremes: A Global Model for Adaptation Decision-making at the Local Level Given Uncertain Climate Projections

NASA Astrophysics Data System (ADS)

Turner, D.

2014-12-01

Understanding the potential economic and physical impacts of climate change on coastal resources involves evaluating a number of distinct adaptive responses. This paper presents a tool for such analysis, a spatially-disaggregated optimization model for adaptation to sea level rise (SLR) and storm surge, the Coastal Impact and Adaptation Model (CIAM). This decision-making framework fills a gap between very detailed studies of specific locations and overly aggregate global analyses. While CIAM is global in scope, the optimal adaptation strategy is determined at the local level, evaluating over 12,000 coastal segments as described in the DIVA database (Vafeidis et al. 2006). The decision to pursue a given adaptation measure depends on local socioeconomic factors like income, population, and land values and how they develop over time, relative to the magnitude of potential coastal impacts, based on geophysical attributes like inundation zones and storm surge. For example, the model's decision to protect or retreat considers the costs of constructing and maintaining coastal defenses versus those of relocating people and capital to minimize damages from land inundation and coastal storms. Uncertain storm surge events are modeled with a generalized extreme value distribution calibrated to data on local surge extremes. Adaptation is optimized for the near-term outlook, in an "act then learn then act" framework that is repeated over the model time horizon. This framework allows the adaptation strategy to be flexibly updated, reflecting the process of iterative risk management. CIAM provides new estimates of the economic costs of SLR; moreover, these detailed results can be compactly represented in a set of adaptation and damage functions for use in integrated assessment models. Alongside the optimal result, CIAM evaluates suboptimal cases and finds that global costs could increase by an order of magnitude, illustrating the importance of adaptive capacity and coastal policy.

Book review: Decision making in natural resource management: A structured adaptive approach

USGS Publications Warehouse

Fuller, Angela K.

2014-01-01

No abstract available.Book information: Decision Making in Natural Resource Management: A Structured Adaptive Approach. Michael J. Conroy and James T. Peterson, 2013. Wiley-Blackwell, Oxford, UK. 456 pp. $99.95 paperback. ISBN: 978-0-470-67174-0.

Effect of visuomotor-map uncertainty on visuomotor adaptation.

PubMed

Saijo, Naoki; Gomi, Hiroaki

2012-03-01

Vision and proprioception contribute to generating hand movement. If a conflict between the visual and proprioceptive feedback of hand position is given, reaching movement is disturbed initially but recovers after training. Although previous studies have predominantly investigated the adaptive change in the motor output, it is unclear whether the contributions of visual and proprioceptive feedback controls to the reaching movement are modified by visuomotor adaptation. To investigate this, we focused on the change in proprioceptive feedback control associated with visuomotor adaptation. After the adaptation to gradually introduce visuomotor rotation, the hand reached the shifted position of the visual target to move the cursor to the visual target correctly. When the cursor feedback was occasionally eliminated (probe trial), the end point of the hand movement was biased in the visual-target direction, while the movement was initiated in the adapted direction, suggesting the incomplete adaptation of proprioceptive feedback control. Moreover, after the learning of uncertain visuomotor rotation, in which the rotation angle was randomly fluctuated on a trial-by-trial basis, the end-point bias in the probe trial increased, but the initial movement direction was not affected, suggesting a reduction in the adaptation level of proprioceptive feedback control. These results suggest that the change in the relative contribution of visual and proprioceptive feedback controls to the reaching movement in response to the visuomotor-map uncertainty is involved in visuomotor adaptation, whereas feedforward control might adapt in a manner different from that of the feedback control.

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making.

PubMed

Prezenski, Sabine; Brechmann, André; Wolff, Susann; Russwinkel, Nele

2017-01-01

Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from

A Cognitive Modeling Approach to Strategy Formation in Dynamic Decision Making

PubMed Central

Prezenski, Sabine; Brechmann, André; Wolff, Susann; Russwinkel, Nele

2017-01-01

Decision-making is a high-level cognitive process based on cognitive processes like perception, attention, and memory. Real-life situations require series of decisions to be made, with each decision depending on previous feedback from a potentially changing environment. To gain a better understanding of the underlying processes of dynamic decision-making, we applied the method of cognitive modeling on a complex rule-based category learning task. Here, participants first needed to identify the conjunction of two rules that defined a target category and later adapt to a reversal of feedback contingencies. We developed an ACT-R model for the core aspects of this dynamic decision-making task. An important aim of our model was that it provides a general account of how such tasks are solved and, with minor changes, is applicable to other stimulus materials. The model was implemented as a mixture of an exemplar-based and a rule-based approach which incorporates perceptual-motor and metacognitive aspects as well. The model solves the categorization task by first trying out one-feature strategies and then, as a result of repeated negative feedback, switching to two-feature strategies. Overall, this model solves the task in a similar way as participants do, including generally successful initial learning as well as reversal learning after the change of feedback contingencies. Moreover, the fact that not all participants were successful in the two learning phases is also reflected in the modeling data. However, we found a larger variance and a lower overall performance of the modeling data as compared to the human data which may relate to perceptual preferences or additional knowledge and rules applied by the participants. In a next step, these aspects could be implemented in the model for a better overall fit. In view of the large interindividual differences in decision performance between participants, additional information about the underlying cognitive processes from

Learning from ISS-modular adaptive NN control of nonlinear strict-feedback systems.

PubMed

Wang, Cong; Wang, Min; Liu, Tengfei; Hill, David J

2012-10-01

This paper studies learning from adaptive neural control (ANC) for a class of nonlinear strict-feedback systems with unknown affine terms. To achieve the purpose of learning, a simple input-to-state stability (ISS) modular ANC method is first presented to ensure the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in finite time. Subsequently, it is proven that learning with the proposed stable ISS-modular ANC can be achieved. The cascade structure and unknown affine terms of the considered systems make it very difficult to achieve learning using existing methods. To overcome these difficulties, the stable closed-loop system in the control process is decomposed into a series of linear time-varying (LTV) perturbed subsystems with the appropriate state transformation. Using a recursive design, the partial persistent excitation condition for the radial basis function neural network (NN) is established, which guarantees exponential stability of LTV perturbed subsystems. Consequently, accurate approximation of the closed-loop system dynamics is achieved in a local region along recurrent orbits of closed-loop signals, and learning is implemented during a closed-loop feedback control process. The learned knowledge is reused to achieve stability and an improved performance, thereby avoiding the tremendous repeated training process of NNs. Simulation studies are given to demonstrate the effectiveness of the proposed method.

Polya's bees: A model of decentralized decision-making.

PubMed

Golman, Russell; Hagmann, David; Miller, John H

2015-09-01

How do social systems make decisions with no single individual in control? We observe that a variety of natural systems, including colonies of ants and bees and perhaps even neurons in the human brain, make decentralized decisions using common processes involving information search with positive feedback and consensus choice through quorum sensing. We model this process with an urn scheme that runs until hitting a threshold, and we characterize an inherent tradeoff between the speed and the accuracy of a decision. The proposed common mechanism provides a robust and effective means by which a decentralized system can navigate the speed-accuracy tradeoff and make reasonably good, quick decisions in a variety of environments. Additionally, consensus choice exhibits systemic risk aversion even while individuals are idiosyncratically risk-neutral. This too is adaptive. The model illustrates how natural systems make decentralized decisions, illuminating a mechanism that engineers of social and artificial systems could imitate.

Developmental and Gender Related Differences in Response Switches after Nonrepresentative Negative Feedback

ERIC Educational Resources Information Center

Jansen, Brenda R. J.; van Duijvenvoorde, Anna C. K.; Huizenga, Hilde M.

2014-01-01

In many decision making tasks negative feedback is probabilistic and, as a consequence, may be given when the decision is actually correct. This feedback can be referred to as nonrepresentative negative feedback. In the current study, we investigated developmental and gender related differences in such switching after nonrepresentative negative…

Evidence use in decision-making on introducing innovations: a systematic scoping review with stakeholder feedback.

PubMed

Turner, Simon; D'Lima, Danielle; Hudson, Emma; Morris, Stephen; Sheringham, Jessica; Swart, Nick; Fulop, Naomi J

2017-12-04

A range of evidence informs decision-making on innovation in health care, including formal research findings, local data and professional opinion. However, cultural and organisational factors often prevent the translation of evidence for innovations into practice. In addition to the characteristics of evidence, it is known that processes at the individual level influence its impact on decision-making. Less is known about the ways in which processes at the professional, organisational and local system level shape evidence use and its role in decisions to adopt innovations. A systematic scoping review was used to review the health literature on innovations within acute and primary care and map processes at the professional, organisational and local system levels which influence how evidence informs decision-making on innovation. Stakeholder feedback on the themes identified was collected via focus groups to test and develop the findings. Following database and manual searches, 31 studies reporting primary qualitative data met the inclusion criteria: 24 were of sufficient methodological quality to be included in the thematic analysis. Evidence use in decision-making on innovation is influenced by multi-level processes (professional, organisational, local system) and interactions across these levels. Preferences for evidence vary by professional group and health service setting. Organisations can shape professional behaviour by requiring particular forms of evidence to inform decision-making. Pan-regional organisations shape innovation decision-making at lower levels. Political processes at all levels shape the selection and use of evidence in decision-making. The synthesis of results from primary qualitative studies found that evidence use in decision-making on innovation is influenced by processes at multiple levels. Interactions between different levels shape evidence use in decision-making (e.g. professional groups and organisations can use local systems to

Adaptive management of urban watersheds

NASA Astrophysics Data System (ADS)

Garmestani, A.; Shuster, W.; Green, O. O.

2013-12-01

Consent decree settlements for violations of the Clean Water Act (1972) increasingly include provisions for redress of combined sewer overflow activity through hybrid approaches that incorporate the best of both gray (e.g., storage tunnels) and green infrastructure (e.g., rain gardens). Adaptive management is an environmental management strategy that uses an iterative process of decision-making to improve environmental management via system monitoring. A central tenet of adaptive management is that management involves a learning process that can help regulated communities achieve environmental quality objectives. We are using an adaptive management approach to guide a green infrastructure retrofit of a neighborhood in the Slavic Village Development Corporation area (Cleveland, Ohio). We are in the process of gathering hydrologic and ecosystem services data and will use this data as a basis for collaboration with area citizens on a plan to use green infrastructure to contain stormflows. Monitoring data provides researchers with feedback on the impact of green infrastructure implementation and suggest where improvements can be made.

Adaptive Robust Output Feedback Control for a Marine Dynamic Positioning System Based on a High-Gain Observer.

PubMed

Du, Jialu; Hu, Xin; Liu, Hongbo; Chen, C L Philip

2015-11-01

This paper develops an adaptive robust output feedback control scheme for dynamically positioned ships with unavailable velocities and unknown dynamic parameters in an unknown time-variant disturbance environment. The controller is designed by incorporating the high-gain observer and radial basis function (RBF) neural networks in vectorial backstepping method. The high-gain observer provides the estimations of the ship position and heading as well as velocities. The RBF neural networks are employed to compensate for the uncertainties of ship dynamics. The adaptive laws incorporating a leakage term are designed to estimate the weights of RBF neural networks and the bounds of unknown time-variant environmental disturbances. In contrast to the existing results of dynamic positioning (DP) controllers, the proposed control scheme relies only on the ship position and heading measurements and does not require a priori knowledge of the ship dynamics and external disturbances. By means of Lyapunov functions, it is theoretically proved that our output feedback controller can control a ship's position and heading to the arbitrarily small neighborhood of the desired target values while guaranteeing that all signals in the closed-loop DP control system are uniformly ultimately bounded. Finally, simulations involving two ships are carried out, and simulation results demonstrate the effectiveness of the proposed control scheme.

Event-based knowledge elicitation of operating room management decision-making using scenarios adapted from information systems data

PubMed Central

2011-01-01

Background No systematic process has previously been described for a needs assessment that identifies the operating room (OR) management decisions made by the anesthesiologists and nurse managers at a facility that do not maximize the efficiency of use of OR time. We evaluated whether event-based knowledge elicitation can be used practically for rapid assessment of OR management decision-making at facilities, whether scenarios can be adapted automatically from information systems data, and the usefulness of the approach. Methods A process of event-based knowledge elicitation was developed to assess OR management decision-making that may reduce the efficiency of use of OR time. Hypothetical scenarios addressing every OR management decision influencing OR efficiency were created from published examples. Scenarios are adapted, so that cues about conditions are accurate and appropriate for each facility (e.g., if OR 1 is used as an example in a scenario, the listed procedure is a type of procedure performed at the facility in OR 1). Adaptation is performed automatically using the facility's OR information system or anesthesia information management system (AIMS) data for most scenarios (43 of 45). Performing the needs assessment takes approximately 1 hour of local managers' time while they decide if their decisions are consistent with the described scenarios. A table of contents of the indexed scenarios is created automatically, providing a simple version of problem solving using case-based reasoning. For example, a new OR manager wanting to know the best way to decide whether to move a case can look in the chapter on "Moving Cases on the Day of Surgery" to find a scenario that describes the situation being encountered. Results Scenarios have been adapted and used at 22 hospitals. Few changes in decisions were needed to increase the efficiency of use of OR time. The few changes were heterogeneous among hospitals, showing the usefulness of individualized assessments

Event-based knowledge elicitation of operating room management decision-making using scenarios adapted from information systems data.

PubMed

Dexter, Franklin; Wachtel, Ruth E; Epstein, Richard H

2011-01-07

No systematic process has previously been described for a needs assessment that identifies the operating room (OR) management decisions made by the anesthesiologists and nurse managers at a facility that do not maximize the efficiency of use of OR time. We evaluated whether event-based knowledge elicitation can be used practically for rapid assessment of OR management decision-making at facilities, whether scenarios can be adapted automatically from information systems data, and the usefulness of the approach. A process of event-based knowledge elicitation was developed to assess OR management decision-making that may reduce the efficiency of use of OR time. Hypothetical scenarios addressing every OR management decision influencing OR efficiency were created from published examples. Scenarios are adapted, so that cues about conditions are accurate and appropriate for each facility (e.g., if OR 1 is used as an example in a scenario, the listed procedure is a type of procedure performed at the facility in OR 1). Adaptation is performed automatically using the facility's OR information system or anesthesia information management system (AIMS) data for most scenarios (43 of 45). Performing the needs assessment takes approximately 1 hour of local managers' time while they decide if their decisions are consistent with the described scenarios. A table of contents of the indexed scenarios is created automatically, providing a simple version of problem solving using case-based reasoning. For example, a new OR manager wanting to know the best way to decide whether to move a case can look in the chapter on "Moving Cases on the Day of Surgery" to find a scenario that describes the situation being encountered. Scenarios have been adapted and used at 22 hospitals. Few changes in decisions were needed to increase the efficiency of use of OR time. The few changes were heterogeneous among hospitals, showing the usefulness of individualized assessments. Our technical advance is the

Online participation in climate change adaptation: A case study of agricultural adaptation measures in Northern Italy.

PubMed

Bojovic, Dragana; Bonzanigo, Laura; Giupponi, Carlo; Maziotis, Alexandros

2015-07-01

The new EU strategy on adaptation to climate change suggests flexible and participatory approaches. Face-to-face contact, although it involves time-consuming procedures with a limited audience, has often been considered the most effective participatory approach. In recent years, however, there has been an increase in the visibility of different citizens' initiatives in the online world, which strengthens the possibility of greater citizen agency. This paper investigates whether the Internet can ensure efficient public participation with meaningful engagement in climate change adaptation. In elucidating issues regarding climate change adaptation, we developed an eParticipation framework to explore adaptation capacity of agriculture to climate change in Northern Italy. Farmers were mobilised using a pre-existing online network. First they took part in an online questionnaire for revealing their perceptions of and reactions to the impacts of ongoing changes in agriculture. We used these results to suggest a portfolio of policy measures and to set evaluation criteria. Farmers then evaluated these policy options, using a multi criteria analysis tool with a simple user-friendly interface. Our results showed that eParticipation is efficient: it supports a rapid data collection, while involving high number of participants. Moreover, we demonstrated that the digital divide is decreasingly an obstacle for using online spaces for public engagement. This research does not present eParticipation as a panacea. Rather, eParticipation was implemented with well-established participatory approaches to both validate the results and, consequently, communicate meaningful messages on local agricultural adaptation practices to regional decision-makers. Feedbacks from the regional decision-makers showed their interest in using eParticipation to improve communication with farmers in the future. We expect that, with further Internet proliferation, eParticipation may allow the inclusion of

Error Argumentation Enhance Adaptability in Adults With Low Motor Ability.

PubMed

Lee, Chi-Mei; Bo, Jin

2016-01-01

The authors focused on young adults with varying degrees of motor difficulties and examined their adaptability in a visuomotor adaptation task where the visual feedback of participants' movement error was presented with either 1:1 ratio (i.e., regular feedback schedule) or 1:2 ratio (i.e., enhanced feedback schedule). Within-subject design was used with two feedback schedules counter-balanced and separated for 10 days. Results revealed that participants with greater motor difficulties showed less adaptability than those with normal motor abilities in the regular feedback schedule; however, all participants demonstrated similar level of adaptability in the enhanced feedback schedule. The results suggest that error argumentation enhances adaptability in adults with low motor ability.

Ambulatory Feedback System

NASA Technical Reports Server (NTRS)

Finger, Herbert; Weeks, Bill

1985-01-01

This presentation discusses instrumentation that will be used for a specific event, which we hope will carry on to future events within the Space Shuttle program. The experiment is the Autogenic Feedback Training Experiment (AFTE) scheduled for Spacelab 3, currently scheduled to be launched in November, 1984. The objectives of the AFTE are to determine the effectiveness of autogenic feedback in preventing or reducing space adaptation syndrome (SAS), to monitor and record in-flight data from the crew, to determine if prediction criteria for SAS can be established, and, finally, to develop an ambulatory instrument package to mount the crew throughout the mission. The purpose of the Ambulatory Feedback System (AFS) is to record the responses of the subject during a provocative event in space and provide a real-time feedback display to reinforce the training.

Learning from adaptive neural dynamic surface control of strict-feedback systems.

PubMed

Wang, Min; Wang, Cong

2015-06-01

Learning plays an essential role in autonomous control systems. However, how to achieve learning in the nonstationary environment for nonlinear systems is a challenging problem. In this paper, we present learning method for a class of n th-order strict-feedback systems by adaptive dynamic surface control (DSC) technology, which achieves the human-like ability of learning by doing and doing with learned knowledge. To achieve the learning, this paper first proposes stable adaptive DSC with auxiliary first-order filters, which ensures the boundedness of all the signals in the closed-loop system and the convergence of tracking errors in a finite time. With the help of DSC, the derivative of the filter output variable is used as the neural network (NN) input instead of traditional intermediate variables. As a result, the proposed adaptive DSC method reduces greatly the dimension of NN inputs, especially for high-order systems. After the stable DSC design, we decompose the stable closed-loop system into a series of linear time-varying perturbed subsystems. Using a recursive design, the recurrent property of NN input variables is easily verified since the complexity is overcome using DSC. Subsequently, the partial persistent excitation condition of the radial basis function NN is satisfied. By combining a state transformation, accurate approximations of the closed-loop system dynamics are recursively achieved in a local region along recurrent orbits. Then, the learning control method using the learned knowledge is proposed to achieve the closed-loop stability and the improved control performance. Simulation studies are performed to demonstrate the proposed scheme can not only reuse the learned knowledge to achieve the better control performance with the faster tracking convergence rate and the smaller tracking error but also greatly alleviate the computational burden because of reducing the number and complexity of NN input variables.

Evaluating Adaptation of a Cancer Clinical Trial Decision Aid for Rural Cancer Patients: A Mixed-Methods Approach.

PubMed

Pathak, Swati; George, Nerissa; Monti, Denise; Robinson, Kathy; Politi, Mary C

2018-06-03

Rural-residing cancer patients often do not participate in clinical trials. Many patients misunderstand cancer clinical trials and their rights as participant. The purpose of this study is to modify a previously developed cancer clinical trials decision aid (DA), incorporating the unique needs of rural populations, and test its impact on knowledge and decision outcomes. The study was conducted in two phases. Phase I recruited 15 rural-residing cancer survivors in a qualitative usability study. Participants navigated the original DA and provided feedback regarding usability and implementation in rural settings. Phase II recruited 31 newly diagnosed rural-residing cancer patients. Patients completed a survey before and after using the revised DA, R-CHOICES. Primary outcomes included decisional conflict, decision self-efficacy, knowledge, communication self-efficacy, and attitudes towards and willingness to consider joining a trial. In phase I, the DA was viewed positively by rural-residing cancer survivors. Participants provided important feedback about factors rural-residing patients consider when thinking about trial participation. In phase II, after using R-CHOICES, participants had higher certainty about their choice (mean post-test = 3.10 vs. pre-test = 2.67; P = 0.025) and higher trial knowledge (mean percentage correct at post-test = 73.58 vs. pre-test = 57.77; P < 0.001). There was no significant change in decision self-efficacy, communication self-efficacy, and attitudes towards or willingness to join trials. The R-CHOICES improved rural-residing patients' knowledge of cancer clinical trials and reduced conflict about making a trial decision. More research is needed on ways to further support decisions about trial participation among this population.

Neural correlates of uncertain decision making: ERP evidence from the Iowa Gambling Task

PubMed Central

Cui, Ji-fang; Chen, Ying-he; Wang, Ya; Shum, David H. K.; Chan, Raymond C. K.

2013-01-01

In our daily life, it is very common to make decisions in uncertain situations. The Iowa Gambling Task (IGT) has been widely used in laboratory studies because of its good simulation of uncertainty in real life activities. The present study aimed to examine the neural correlates of uncertain decision making with the IGT. Twenty-six university students completed this study. An adapted IGT was administered to them, and the EEG data were recorded. The adapted IGT we used allowed us to analyze the choice evaluation, response selection, and feedback evaluation stages of uncertain decision making within the same paradigm. In the choice evaluation stage, the advantageous decks evoked larger P3 amplitude in the left hemisphere, while the disadvantageous decks evoked larger P3 in the right hemisphere. In the response selection stage, the response of “pass” (the card was not turned over; the participants neither won nor lost money) evoked larger negativity preceding the response compared to that of “play” (the card was turned over; the participant either won or lost money). In the feedback evaluation stage, feedback-related negativity (FRN) was only sensitive to the valence (win/loss) but not the magnitude (large/small) of the outcome, and P3 was sensitive to both the valence and the magnitude of the outcome. These results were consistent with the notion that a positive somatic state was represented in the left hemisphere and a negative somatic state was represented in the right hemisphere. There were also anticipatory ERP effects that guided the participants' responses and provided evidence for the somatic marker hypothesis with more precise timing. PMID:24298248

Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation.

PubMed

Zhang, Xiangjun; Wu, Xiaolin

2008-06-01

The challenge of image interpolation is to preserve spatial details. We propose a soft-decision interpolation technique that estimates missing pixels in groups rather than one at a time. The new technique learns and adapts to varying scene structures using a 2-D piecewise autoregressive model. The model parameters are estimated in a moving window in the input low-resolution image. The pixel structure dictated by the learnt model is enforced by the soft-decision estimation process onto a block of pixels, including both observed and estimated. The result is equivalent to that of a high-order adaptive nonseparable 2-D interpolation filter. This new image interpolation approach preserves spatial coherence of interpolated images better than the existing methods, and it produces the best results so far over a wide range of scenes in both PSNR measure and subjective visual quality. Edges and textures are well preserved, and common interpolation artifacts (blurring, ringing, jaggies, zippering, etc.) are greatly reduced.

Quantifying the Value of Downscaled Climate Model Information for Adaptation Decisions: When is Downscaling a Smart Decision?

NASA Astrophysics Data System (ADS)

Terando, A. J.; Wootten, A.; Eaton, M. J.; Runge, M. C.; Littell, J. S.; Bryan, A. M.; Carter, S. L.

2015-12-01

Two types of decisions face society with respect to anthropogenic climate change: (1) whether to enact a global greenhouse gas abatement policy, and (2) how to adapt to the local consequences of current and future climatic changes. The practice of downscaling global climate models (GCMs) is often used to address (2) because GCMs do not resolve key features that will mediate global climate change at the local scale. In response, the development of downscaling techniques and models has accelerated to aid decision makers seeking adaptation guidance. However, quantifiable estimates of the value of information are difficult to obtain, particularly in decision contexts characterized by deep uncertainty and low system-controllability. Here we demonstrate a method to quantify the additional value that decision makers could expect if research investments are directed towards developing new downscaled climate projections. As a proof of concept we focus on a real-world management problem: whether to undertake assisted migration for an endangered tropical avian species. We also take advantage of recently published multivariate methods that account for three vexing issues in climate impacts modeling: maximizing climate model quality information, accounting for model dependence in ensembles of opportunity, and deriving probabilistic projections. We expand on these global methods by including regional (Caribbean Basin) and local (Puerto Rico) domains. In the local domain, we test whether a high resolution (2km) dynamically downscaled GCM reduces the multivariate error estimate compared to the original coarse-scale GCM. Initial tests show little difference between the downscaled and original GCM multivariate error. When propagated through to a species population model, the Value of Information analysis indicates that the expected utility that would accrue to the manager (and species) if this downscaling were completed may not justify the cost compared to alternative actions.

Neural Approximation-Based Adaptive Control for a Class of Nonlinear Nonstrict Feedback Discrete-Time Systems.

PubMed

Yan-Jun Liu; Shu Li; Shaocheng Tong; Chen, C L Philip

2017-07-01

In this paper, an adaptive control approach-based neural approximation is developed for a class of uncertain nonlinear discrete-time (DT) systems. The main characteristic of the considered systems is that they can be viewed as a class of multi-input multioutput systems in the nonstrict feedback structure. The similar control problem of this class of systems has been addressed in the past, but it focused on the continuous-time systems. Due to the complicacies of the system structure, it will become more difficult for the controller design and the stability analysis. To stabilize this class of systems, a new recursive procedure is developed, and the effect caused by the noncausal problem in the nonstrict feedback DT structure can be solved using a semirecurrent neural approximation. Based on the Lyapunov difference approach, it is proved that all the signals of the closed-loop system are semiglobal, ultimately uniformly bounded, and a good tracking performance can be guaranteed. The feasibility of the proposed controllers can be validated by setting a simulation example.

Recent advances in applying decision science to managing national forests

USGS Publications Warehouse

Marcot, Bruce G.; Thompson, Matthew P.; Runge, Michael C.; Thompson, Frank R.; McNulty, Steven; Cleaves, David; Tomosy, Monica; Fisher, Larry A.; Andrew, Bliss

2012-01-01

Management of federal public forests to meet sustainability goals and multiple use regulations is an immense challenge. To succeed, we suggest use of formal decision science procedures and tools in the context of structured decision making (SDM). SDM entails four stages: problem structuring (framing the problem and defining objectives and evaluation criteria), problem analysis (defining alternatives, evaluating likely consequences, identifying key uncertainties, and analyzing tradeoffs), decision point (identifying the preferred alternative), and implementation and monitoring the preferred alternative with adaptive management feedbacks. We list a wide array of models, techniques, and tools available for each stage, and provide three case studies of their selected use in National Forest land management and project plans. Successful use of SDM involves participation by decision-makers, analysts, scientists, and stakeholders. We suggest specific areas for training and instituting SDM to foster transparency, rigor, clarity, and inclusiveness in formal decision processes regarding management of national forests.

Polya’s bees: A model of decentralized decision-making

PubMed Central

Golman, Russell; Hagmann, David; Miller, John H.

2015-01-01

How do social systems make decisions with no single individual in control? We observe that a variety of natural systems, including colonies of ants and bees and perhaps even neurons in the human brain, make decentralized decisions using common processes involving information search with positive feedback and consensus choice through quorum sensing. We model this process with an urn scheme that runs until hitting a threshold, and we characterize an inherent tradeoff between the speed and the accuracy of a decision. The proposed common mechanism provides a robust and effective means by which a decentralized system can navigate the speed-accuracy tradeoff and make reasonably good, quick decisions in a variety of environments. Additionally, consensus choice exhibits systemic risk aversion even while individuals are idiosyncratically risk-neutral. This too is adaptive. The model illustrates how natural systems make decentralized decisions, illuminating a mechanism that engineers of social and artificial systems could imitate. PMID:26601255

Modelling human decision-making in coupled human and natural systems

NASA Astrophysics Data System (ADS)

Feola, G.

2012-12-01

A solid understanding of human decision-making is essential to analyze the complexity of coupled human and natural systems (CHANS) and inform policies to promote resilience in the face of environmental change. Human decisions drive and/or mediate the interactions and feedbacks, and contribute to the heterogeneity and non-linearity that characterize CHANS. However, human decision-making is usually over-simplistically modeled, whereby human agents are represented deterministically either as dumb or clairvoyant decision-makers. Decision-making models fall short in the integration of both environmental and human behavioral drivers, and concerning the latter, tend to focus on only one category, e.g. economic, cultural, or psychological. Furthermore, these models render a linear decision-making process and therefore fail to account for the recursive co-evolutionary dynamics in CHANS. As a result, these models constitute only a weak basis for policy-making. There is therefore scope and an urgent need for better approaches to human decision-making, to produce the knowledge that can inform vulnerability reduction policies in the face of environmental change. This presentation synthesizes the current state-of-the-art of modelling human decision-making in CHANS, with particular reference to agricultural systems, and delineates how the above mentioned shortcomings can be overcome. Through examples from research on pesticide use and adaptation to climate change, both based on the integrative agent-centered framework (Feola and Binder, 2010), the approach for an improved understanding of human agents in CHANS are illustrated. This entails: integrative approach, focus on behavioral dynamics more than states, feedbacks between individual and system levels, and openness to heterogeneity.

Robust Brain-Machine Interface Design Using Optimal Feedback Control Modeling and Adaptive Point Process Filtering

PubMed Central

Carmena, Jose M.

2016-01-01

Much progress has been made in brain-machine interfaces (BMI) using decoders such as Kalman filters and finding their parameters with closed-loop decoder adaptation (CLDA). However, current decoders do not model the spikes directly, and hence may limit the processing time-scale of BMI control and adaptation. Moreover, while specialized CLDA techniques for intention estimation and assisted training exist, a unified and systematic CLDA framework that generalizes across different setups is lacking. Here we develop a novel closed-loop BMI training architecture that allows for processing, control, and adaptation using spike events, enables robust control and extends to various tasks. Moreover, we develop a unified control-theoretic CLDA framework within which intention estimation, assisted training, and adaptation are performed. The architecture incorporates an infinite-horizon optimal feedback-control (OFC) model of the brain’s behavior in closed-loop BMI control, and a point process model of spikes. The OFC model infers the user’s motor intention during CLDA—a process termed intention estimation. OFC is also used to design an autonomous and dynamic assisted training technique. The point process model allows for neural processing, control and decoder adaptation with every spike event and at a faster time-scale than current decoders; it also enables dynamic spike-event-based parameter adaptation unlike current CLDA methods that use batch-based adaptation on much slower adaptation time-scales. We conducted closed-loop experiments in a non-human primate over tens of days to dissociate the effects of these novel CLDA components. The OFC intention estimation improved BMI performance compared with current intention estimation techniques. OFC assisted training allowed the subject to consistently achieve proficient control. Spike-event-based adaptation resulted in faster and more consistent performance convergence compared with batch-based methods, and was robust to

Electrophysiological brain indices of risk behavior modification induced by contingent feedback.

PubMed

Megías, Alberto; Torres, Miguel Angel; Catena, Andrés; Cándido, Antonio; Maldonado, Antonio

2018-02-01

The main aim of this research was to study the effects of response feedback on risk behavior and the neural and cognitive mechanisms involved, as a function of the feedback contingency. Sixty drivers were randomly assigned to one of three feedback groups: contingent, non-contingent and no feedback. The participants' task consisted of braking or not when confronted with a set of risky driving situations, while their electroencephalographic activity was continuously recorded. We observed that contingent feedback, as opposed to non-contingent feedback, promoted changes in the response bias towards safer decisions. This behavioral modification implied a higher demand on cognitive control, reflected in a larger amplitude of the N400 component. Moreover, the contingent feedback, being predictable and entailing more informative value, gave rise to smaller SPN and larger FRN scores when compared with non-contingent feedback. Taken together, these findings provide a new and complex insight into the neurophysiological basis of the influence of feedback contingency on the processing of decision-making under risk. We suggest that response feedback, when contingent upon the risky behavior, appears to improve the functionality of the brain mechanisms involved in decision-making and can be a powerful tool for reducing the tendency to choose risky options in risk-prone individuals. Copyright © 2018 Elsevier B.V. All rights reserved.

Sure I'm Sure: Prefrontal Oscillations Support Metacognitive Monitoring of Decision Making.

PubMed

Wokke, Martijn E; Cleeremans, Axel; Ridderinkhof, K Richard

2017-01-25

Successful decision making critically involves metacognitive processes such as monitoring and control of our decision process. Metacognition enables agents to modify ongoing behavior adaptively and determine what to do next in situations in which external feedback is not (immediately) available. Despite the importance of metacognition for many aspects of life, little is known about how our metacognitive system operates or about what kind of information is used for metacognitive (second-order) judgments. In particular, it remains an open question whether metacognitive judgments are based on the same information as first-order decisions. Here, we investigated the relationship between metacognitive performance and first-order task performance by recording EEG signals while participants were asked to make a "diagnosis" after seeing a sample of fictitious patient data (a complex pattern of colored moving dots of different sizes). To assess metacognitive performance, participants provided an estimate about the quality of their diagnosis on each trial. Results demonstrate that the information that contributes to first-order decisions differs from the information that supports metacognitive judgments. Further, time-frequency analyses of EEG signals reveal that metacognitive performance is associated specifically with prefrontal theta-band activity. Together, our findings are consistent with a hierarchical model of metacognition and suggest a crucial role for prefrontal oscillations in metacognitive performance. Monitoring and control of our decision process (metacognition) is a crucial aspect of adaptive decision making. Crucially, metacognitive skills enable us to adjust ongoing behavior and determine future decision making when immediate feedback is not available. In the present study, we constructed a "diagnosis task" that allowed us to assess in what way first-order task performance and metacognition are related to each other. Results demonstrate that the contribution

Visuomotor adaptability in older adults with mild cognitive decline.

PubMed

Schaffert, Jeffrey; Lee, Chi-Mei; Neill, Rebecca; Bo, Jin

2017-02-01

The current study examined the augmentation of error feedback on visuomotor adaptability in older adults with varying degrees of cognitive decline (assessed by the Montreal Cognitive Assessment; MoCA). Twenty-three participants performed a center-out computerized visuomotor adaptation task when the visual feedback of their hand movement error was presented in a regular (ratio=1:1) or enhanced (ratio=1:2) error feedback schedule. Results showed that older adults with lower scores on the MoCA had less adaptability than those with higher MoCA scores during the regular feedback schedule. However, participants demonstrated similar adaptability during the enhanced feedback schedule, regardless of their cognitive ability. Furthermore, individuals with lower MoCA scores showed larger after-effects in spatial control during the enhanced schedule compared to the regular schedule, whereas individuals with higher MoCA scores displayed the opposite pattern. Additional neuro-cognitive assessments revealed that spatial working memory and processing speed were positively related to motor adaptability during the regular scheduled but negatively related to adaptability during the enhanced schedule. We argue that individuals with mild cognitive decline employed different adaptation strategies when encountering enhanced visual feedback, suggesting older adults with mild cognitive impairment (MCI) may benefit from enhanced visual error feedback during sensorimotor adaptation. Copyright © 2016 Elsevier B.V. All rights reserved.

A Study of Adaptive Relevance Feedback - UIUC TREC-2008 Relevance Feedback Experiments

DTIC Science & Technology

2008-11-01

terms. Journal of the American Society for Information Science, 27(3):129–146, 1976. [7] J . J . Rocchio. Relevance feedback in information retrieval. In...In The SMART Retrieval System: Experiments in Automatic Document Processing, pages 313–323. Prentice-Hall Inc., 1971. [8] Gerard Salton and Chris

Cortical potentials evoked by confirming and disconfirming feedback following an auditory discrimination.

NASA Technical Reports Server (NTRS)

Squires, K. C.; Hillyard, S. A.; Lindsay, P. H.

1973-01-01

Vertex potentials elicited by visual feedback signals following an auditory intensity discrimination have been studied with eight subjects. Feedback signals which confirmed the prior sensory decision elicited small P3s, while disconfirming feedback elicited P3s that were larger. On the average, the latency of P3 was also found to increase with increasing disparity between the judgment and the feedback information. These effects were part of an overall dichotomy in wave shape following confirming vs disconfirming feedback. These findings are incorporated in a general model of the role of P3 in perceptual decision making.

Improved memory for error feedback.

PubMed

Van der Borght, Liesbet; Schouppe, Nathalie; Notebaert, Wim

2016-11-01

Surprising feedback in a general knowledge test leads to an improvement in memory for both the surface features and the content of the feedback (Psychon Bull Rev 16:88-92, 2009). Based on the idea that in cognitive tasks, error is surprising (the orienting account, Cognition 111:275-279, 2009), we tested whether error feedback would be better remembered than correct feedback. Colored words were presented as feedback signals in a flanker task, where the color indicated the accuracy. Subsequently, these words were again presented during a recognition task (Experiment 1) or a lexical decision task (Experiments 2 and 3). In all experiments, memory was improved for words seen as error feedback. These results are compared to the attentional boost effect (J Exp Psychol Learn Mem Cogn 39:1223-12231, 2013) and related to the orienting account for post-error slowing (Cognition 111:275-279, 2009).

Prism adaptation in virtual and natural contexts: Evidence for a flexible adaptive process.

PubMed

Veilleux, Louis-Nicolas; Proteau, Luc

2015-01-01

Prism exposure when aiming at a visual target in a virtual condition (e.g., when the hand is represented by a video representation) produces no or only small adaptations (after-effects), whereas prism exposure in a natural condition produces large after-effects. Some researchers suggested that this difference may arise from distinct adaptive processes, but other studies suggested a unique process. The present study reconciled these conflicting interpretations. Forty participants were divided into two groups: One group used visual feedback of their hand (natural context), and the other group used computer-generated representational feedback (virtual context). Visual feedback during adaptation was concurrent or terminal. All participants underwent laterally displacing prism perturbation. The results showed that the after-effects were twice as large in the "natural context" than in the "virtual context". No significant differences were observed between the concurrent and terminal feedback conditions. The after-effects generalized to untested targets and workspace. These results suggest that prism adaptation in virtual and natural contexts involves the same process. The smaller after-effects in the virtual context suggest that the depth of adaptation is a function of the degree of convergence between the proprioceptive and visual information that arises from the hand.

Adaptive awareness for personal and small group decision making.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Perano, Kenneth J.; Tucker, Steve; Pancerella, Carmen M.

2003-12-01

Many situations call for the use of sensors monitoring physiological and environmental data. In order to use the large amounts of sensor data to affect decision making, we are coupling heterogeneous sensors with small, light-weight processors, other powerful computers, wireless communications, and embedded intelligent software. The result is an adaptive awareness and warning tool, which provides both situation awareness and personal awareness to individuals and teams. Central to this tool is a sensor-independent architecture, which combines both software agents and a reusable core software framework that manages the available hardware resources and provides services to the agents. Agents can recognizemore » cues from the data, warn humans about situations, and act as decision-making aids. Within the agents, self-organizing maps (SOMs) are used to process physiological data in order to provide personal awareness. We have employed a novel clustering algorithm to train the SOM to discern individual body states and activities. This awareness tool has broad applicability to emergency teams, military squads, military medics, individual exercise and fitness monitoring, health monitoring for sick and elderly persons, and environmental monitoring in public places. This report discusses our hardware decisions, software framework, and a pilot awareness tool, which has been developed at Sandia National Laboratories.« less

Homeostatic Regulation of Memory Systems and Adaptive Decisions

PubMed Central

Mizumori, Sheri JY; Jo, Yong Sang

2013-01-01

While it is clear that many brain areas process mnemonic information, understanding how their interactions result in continuously adaptive behaviors has been a challenge. A homeostatic-regulated prediction model of memory is presented that considers the existence of a single memory system that is based on a multilevel coordinated and integrated network (from cells to neural systems) that determines the extent to which events and outcomes occur as predicted. The “multiple memory systems of the brain” have in common output that signals errors in the prediction of events and/or their outcomes, although these signals differ in terms of what the error signal represents (e.g., hippocampus: context prediction errors vs. midbrain/striatum: reward prediction errors). The prefrontal cortex likely plays a pivotal role in the coordination of prediction analysis within and across prediction brain areas. By virtue of its widespread control and influence, and intrinsic working memory mechanisms. Thus, the prefrontal cortex supports the flexible processing needed to generate adaptive behaviors and predict future outcomes. It is proposed that prefrontal cortex continually and automatically produces adaptive responses according to homeostatic regulatory principles: prefrontal cortex may serve as a controller that is intrinsically driven to maintain in prediction areas an experience-dependent firing rate set point that ensures adaptive temporally and spatially resolved neural responses to future prediction errors. This same drive by prefrontal cortex may also restore set point firing rates after deviations (i.e. prediction errors) are detected. In this way, prefrontal cortex contributes to reducing uncertainty in prediction systems. An emergent outcome of this homeostatic view may be the flexible and adaptive control that prefrontal cortex is known to implement (i.e. working memory) in the most challenging of situations. Compromise to any of the prediction circuits should result

Homeostatic regulation of memory systems and adaptive decisions.

PubMed

Mizumori, Sheri J Y; Jo, Yong Sang

2013-11-01

While it is clear that many brain areas process mnemonic information, understanding how their interactions result in continuously adaptive behaviors has been a challenge. A homeostatic-regulated prediction model of memory is presented that considers the existence of a single memory system that is based on a multilevel coordinated and integrated network (from cells to neural systems) that determines the extent to which events and outcomes occur as predicted. The "multiple memory systems of the brain" have in common output that signals errors in the prediction of events and/or their outcomes, although these signals differ in terms of what the error signal represents (e.g., hippocampus: context prediction errors vs. midbrain/striatum: reward prediction errors). The prefrontal cortex likely plays a pivotal role in the coordination of prediction analysis within and across prediction brain areas. By virtue of its widespread control and influence, and intrinsic working memory mechanisms. Thus, the prefrontal cortex supports the flexible processing needed to generate adaptive behaviors and predict future outcomes. It is proposed that prefrontal cortex continually and automatically produces adaptive responses according to homeostatic regulatory principles: prefrontal cortex may serve as a controller that is intrinsically driven to maintain in prediction areas an experience-dependent firing rate set point that ensures adaptive temporally and spatially resolved neural responses to future prediction errors. This same drive by prefrontal cortex may also restore set point firing rates after deviations (i.e. prediction errors) are detected. In this way, prefrontal cortex contributes to reducing uncertainty in prediction systems. An emergent outcome of this homeostatic view may be the flexible and adaptive control that prefrontal cortex is known to implement (i.e. working memory) in the most challenging of situations. Compromise to any of the prediction circuits should result in

Effects of different feedback types on information integration in repeated monetary gambles

PubMed Central

Haffke, Peter; Hübner, Ronald

2015-01-01

Most models of risky decision making assume that all relevant information is taken into account (e.g., von Neumann and Morgenstern, 1944; Kahneman and Tversky, 1979). However, there are also some models supposing that only part of the information is considered (e.g., Brandstätter et al., 2006; Gigerenzer and Gaissmaier, 2011). To further investigate the amount of information that is usually used for decision making, and how the use depends on feedback, we conducted a series of three experiments in which participants choose between two lotteries and where no feedback, outcome feedback, and error feedback was provided, respectively. The results show that without feedback participants mostly chose the lottery with the higher winning probability, and largely ignored the potential gains. The same results occurred when the outcome of each decision was fed back. Only after presenting error feedback (i.e., signaling whether a choice was optimal or not), participants considered probabilities as well as gains, resulting in more optimal choices. We propose that outcome feedback was ineffective, because of its probabilistic and ambiguous nature. Participants improve information integration only if provided with a consistent and deterministic signal such as error feedback. PMID:25667576

Effects of different feedback types on information integration in repeated monetary gambles.

PubMed

Haffke, Peter; Hübner, Ronald

2014-01-01

Most models of risky decision making assume that all relevant information is taken into account (e.g., von Neumann and Morgenstern, 1944; Kahneman and Tversky, 1979). However, there are also some models supposing that only part of the information is considered (e.g., Brandstätter et al., 2006; Gigerenzer and Gaissmaier, 2011). To further investigate the amount of information that is usually used for decision making, and how the use depends on feedback, we conducted a series of three experiments in which participants choose between two lotteries and where no feedback, outcome feedback, and error feedback was provided, respectively. The results show that without feedback participants mostly chose the lottery with the higher winning probability, and largely ignored the potential gains. The same results occurred when the outcome of each decision was fed back. Only after presenting error feedback (i.e., signaling whether a choice was optimal or not), participants considered probabilities as well as gains, resulting in more optimal choices. We propose that outcome feedback was ineffective, because of its probabilistic and ambiguous nature. Participants improve information integration only if provided with a consistent and deterministic signal such as error feedback.

Reward abundance interferes with error-based learning in a visuomotor adaptation task

PubMed Central

Oostwoud Wijdenes, Leonie; Rigterink, Tessa; Overvliet, Krista E.; Smeets, Joeren B. J.

2018-01-01

The brain rapidly adapts reaching movements to changing circumstances by using visual feedback about errors. Providing reward in addition to error feedback facilitates the adaptation but the underlying mechanism is unknown. Here, we investigate whether the proportion of trials rewarded (the ‘reward abundance’) influences how much participants adapt to their errors. We used a 3D multi-target pointing task in which reward alone is insufficient for motor adaptation. Participants (N = 423) performed the pointing task with feedback based on a shifted hand-position. On a proportion of trials we gave them rewarding feedback that their hand hit the target. Half of the participants only received this reward feedback. The other half also received feedback about endpoint errors. In different groups, we varied the proportion of trials that was rewarded. As expected, participants who received feedback about their errors did adapt, but participants who only received reward-feedback did not. Critically, participants who received abundant rewards adapted less to their errors than participants who received less reward. Thus, reward abundance negatively influences how much participants learn from their errors. Probably participants used a mechanism that relied more on the reward feedback when the reward was abundant. Because participants could not adapt to the reward, this interfered with adaptation to errors. PMID:29513681

A decision support tool for adaptive management of native prairie ecosystems

USGS Publications Warehouse

Hunt, Victoria M.; Jacobi, Sarah; Gannon, Jill J.; Zorn, Jennifer E.; Moore, Clinton; Lonsdorf, Eric V.

2016-01-01

The Native Prairie Adaptive Management initiative is a decision support framework that provides cooperators with management-action recommendations to help them conserve native species and suppress invasive species on prairie lands. We developed a Web-based decision support tool (DST) for the U.S. Fish and Wildlife Service and the U.S. Geological Survey initiative. The DST facilitates cross-organizational data sharing, performs analyses to improve conservation delivery, and requires no technical expertise to operate. Each year since 2012, the DST has used monitoring data to update ecological knowledge that it translates into situation-specific management-action recommendations (e.g., controlled burn or prescribed graze). The DST provides annual recommendations for more than 10,000 acres on 20 refuge complexes in four U.S. states. We describe how the DST promotes the long-term implementation of the program for which it was designed and may facilitate decision support and improve ecological outcomes of other conservation efforts.

An Adaptive Source-Channel Coding with Feedback for Progressive Transmission of Medical Images

PubMed Central

Lo, Jen-Lung; Sanei, Saeid; Nazarpour, Kianoush

2009-01-01

A novel adaptive source-channel coding with feedback for progressive transmission of medical images is proposed here. In the source coding part, the transmission starts from the region of interest (RoI). The parity length in the channel code varies with respect to both the proximity of the image subblock to the RoI and the channel noise, which is iteratively estimated in the receiver. The overall transmitted data can be controlled by the user (clinician). In the case of medical data transmission, it is vital to keep the distortion level under control as in most of the cases certain clinically important regions have to be transmitted without any visible error. The proposed system significantly reduces the transmission time and error. Moreover, the system is very user friendly since the selection of the RoI, its size, overall code rate, and a number of test features such as noise level can be set by the users in both ends. A MATLAB-based TCP/IP connection has been established to demonstrate the proposed interactive and adaptive progressive transmission system. The proposed system is simulated for both binary symmetric channel (BSC) and Rayleigh channel. The experimental results verify the effectiveness of the design. PMID:19190770

How can clinical practice guidelines be adapted to facilitate shared decision making? A qualitative key-informant study.

PubMed

van der Weijden, Trudy; Pieterse, Arwen H; Koelewijn-van Loon, Marije S; Knaapen, Loes; Légaré, France; Boivin, Antoine; Burgers, Jako S; Stiggelbout, Anne M; Faber, Marjan; Elwyn, Glyn

2013-10-01

To explore how clinical practice guidelines can be adapted to facilitate shared decision making. This was a qualitative key-informant study with group discussions and semi-structured interviews. First, 75 experts in guideline development or shared decision making participated in group discussions at two international conferences. Next, health professionals known as experts in depression or breast cancer, experts on clinical practice guidelines and/or shared decision making, and patient representatives were interviewed (N=20). Using illustrative treatment decisions on depression or breast cancer, we asked the interviewees to indicate as specifically as they could how guidelines could be used to facilitate shared decision making. Interviewees suggested some generic strategies, namely to include a separate chapter on the importance of shared decision making, to use language that encourages patient involvement, and to develop patient versions of guidelines. Recommendation-specific strategies, related to specific decision points in the guideline, were also suggested: These include structuring the presentation of healthcare options to increase professionals' option awareness; structuring the deliberation process between professionals and patients; and providing relevant patient support tools embedded at important decision points in the guideline. This study resulted in an overview of strategies to adapt clinical practice guidelines to facilitate shared decision making. Some strategies seemed more contentious than others. Future research should assess the feasibility and impact of these strategies to make clinical practice guidelines more conducive to facilitate shared decision making.

Adaptive decision processes in perceptual comparisons: effects of changes in the global difficulty context.

PubMed

Baranski, Joseph V; Petrusic, William M

2003-06-01

Adaptive decision processes were investigated in experiments involving an unexpected change in the global ease or difficulty of the task. Under accuracy stress, a shift from an easy to a difficult context induced a marked increase in decision time, but a shift from a difficult to an easy context did not. Under speed stress, a shift to a more difficult context induced lower accuracy and rated confidence, depending on the difficulty of the decisions. A view of caution developed in D. Vickers's (1979) accumulator theory--whereby one seeks to base decisions on more information--is compared with a view based on slow and fast guessing theory (W. M. Petrusic, 1992; W. M. Petrusic & J. V. Baranski, 1989a)--whereby one seeks to base decisions on more diagnostic information. On balance, the findings support the latter view.

Adaptive channel estimation for soft decision decoding over non-Gaussian optical channel

NASA Astrophysics Data System (ADS)

Xiang, Jing-song; Miao, Tao-tao; Huang, Sheng; Liu, Huan-lin

2016-10-01

An adaptive priori likelihood ratio (LLR) estimation method is proposed over non-Gaussian channel in the intensity modulation/direct detection (IM/DD) optical communication systems. Using the nonparametric histogram and the weighted least square linear fitting in the tail regions, the LLR is estimated and used for the soft decision decoding of the low-density parity-check (LDPC) codes. This method can adapt well to the three main kinds of intensity modulation/direct detection (IM/DD) optical channel, i.e., the chi-square channel, the Webb-Gaussian channel and the additive white Gaussian noise (AWGN) channel. The performance penalty of channel estimation is neglected.

Understanding and applying principles of social cognition and decision making in adaptive environmental governance

EPA Science Inventory

Environmental governance systems are under greater pressure to adapt and to cope with increased social and ecological uncertainty from stressors like climate change. We review principles of social cognition and decision making that shape and constrain how environmental governance...

Adaptive output feedback control of flexible-joint robots using neural networks: dynamic surface design approach.

PubMed

Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho

2008-10-01

In this paper, we propose a new robust output feedback control approach for flexible-joint electrically driven (FJED) robots via the observer dynamic surface design technique. The proposed method only requires position measurements of the FJED robots. To estimate the link and actuator velocity information of the FJED robots with model uncertainties, we develop an adaptive observer using self-recurrent wavelet neural networks (SRWNNs). The SRWNNs are used to approximate model uncertainties in both robot (link) dynamics and actuator dynamics, and all their weights are trained online. Based on the designed observer, the link position tracking controller using the estimated states is induced from the dynamic surface design procedure. Therefore, the proposed controller can be designed more simply than the observer backstepping controller. From the Lyapunov stability analysis, it is shown that all signals in a closed-loop adaptive system are uniformly ultimately bounded. Finally, the simulation results on a three-link FJED robot are presented to validate the good position tracking performance and robustness of the proposed control system against payload uncertainties and external disturbances.

Sensorimotor adaptation of speech in Parkinson's disease.

PubMed

Mollaei, Fatemeh; Shiller, Douglas M; Gracco, Vincent L

2013-10-01

The basal ganglia are involved in establishing motor plans for a wide range of behaviors. Parkinson's disease (PD) is a manifestation of basal ganglia dysfunction associated with a deficit in sensorimotor integration and difficulty in acquiring new motor sequences, thereby affecting motor learning. Previous studies of sensorimotor integration and sensorimotor adaptation in PD have focused on limb movements using visual and force-field alterations. Here, we report the results from a sensorimotor adaptation experiment investigating the ability of PD patients to make speech motor adjustments to a constant and predictable auditory feedback manipulation. Participants produced speech while their auditory feedback was altered and maintained in a manner consistent with a change in tongue position. The degree of adaptation was associated with the severity of motor symptoms. The patients with PD exhibited adaptation to the induced sensory error; however, the degree of adaptation was reduced compared with healthy, age-matched control participants. The reduced capacity to adapt to a change in auditory feedback is consistent with reduced gain in the sensorimotor system for speech and with previous studies demonstrating limitations in the adaptation of limb movements after changes in visual feedback among patients with PD. © 2013 Movement Disorder Society.

Feedback (F) Fueling Adaptation (A) Network Growth (N) and Self-Organization (S): A Complex Systems Design and Evaluation Approach to Professional Development

ERIC Educational Resources Information Center

Yoon, Susan A.; Klopfer, Eric

2006-01-01

This paper reports on the efficacy of a professional development framework premised on four complex systems design principles: Feedback, Adaptation, Network Growth and Self-organization (FANS). The framework is applied to the design and delivery of the first 2 years of a 3-year study aimed at improving teacher and student understanding of…

Fear of feedback.

PubMed

Jackman, Jay M; Strober, Myra H

2003-04-01

Nobody likes performance reviews. Subordinates are terrified they'll hear nothing but criticism. Bosses think their direct reports will respond to even the mildest criticism with anger or tears. The result? Everyone keeps quiet. That's unfortunate, because most people need help figuring out how to improve their performance and advance their careers. This fear of feedback doesn't come into play just during annual reviews. At least half the executives with whom the authors have worked never ask for feedback. Many expect the worst: heated arguments, even threats of dismissal. So rather than seek feedback, people try to guess what their bosses are thinking. Fears and assumptions about feedback often manifest themselves in psychologically maladaptive behaviors such as procrastination, denial, brooding, jealousy, and self-sabotage. But there's hope, say the authors. Those who learn adaptive techniques can free themselves from destructive responses. They'll be able to deal with feedback better if they acknowledge negative emotions, reframe fear and criticism constructively, develop realistic goals, create support systems, and reward themselves for achievements along the way. Once you've begun to alter your maladaptive behaviors, you can begin seeking regular feedback from your boss. The authors take you through four steps for doing just that: self-assessment, external assessment, absorbing the feedback, and taking action toward change. Organizations profit when employees ask for feedback and deal well with criticism. Once people begin to know how they are doing relative to management's priorities, their work becomes better aligned with organizational goals. What's more, they begin to transform a feedback-averse environment into a more honest and open one, in turn improving performance throughout the organization.

Negative Feedback Enables Fast and Flexible Collective Decision-Making in Ants

PubMed Central

Grüter, Christoph; Schürch, Roger; Czaczkes, Tomer J.; Taylor, Keeley; Durance, Thomas; Jones, Sam M.; Ratnieks, Francis L. W.

2012-01-01

Positive feedback plays a major role in the emergence of many collective animal behaviours. In many ants pheromone trails recruit and direct nestmate foragers to food sources. The strong positive feedback caused by trail pheromones allows fast collective responses but can compromise flexibility. Previous laboratory experiments have shown that when the environment changes, colonies are often unable to reallocate their foragers to a more rewarding food source. Here we show both experimentally, using colonies of Lasius niger, and with an agent-based simulation model, that negative feedback caused by crowding at feeding sites allows ant colonies to maintain foraging flexibility even with strong recruitment to food sources. In a constant environment, negative feedback prevents the frequently found bias towards one feeder (symmetry breaking) and leads to equal distribution of foragers. In a changing environment, negative feedback allows a colony to quickly reallocate the majority of its foragers to a superior food patch that becomes available when foraging at an inferior patch is already well underway. The model confirms these experimental findings and shows that the ability of colonies to switch to a superior food source does not require the decay of trail pheromones. Our results help to resolve inconsistencies between collective foraging patterns seen in laboratory studies and observations in the wild, and show that the simultaneous action of negative and positive feedback is important for efficient foraging in mass-recruiting insect colonies. PMID:22984518

A kinesthetic washout filter for force-feedback rendering.

PubMed

Danieau, Fabien; Lecuyer, Anatole; Guillotel, Philippe; Fleureau, Julien; Mollet, Nicolas; Christie, Marc

2015-01-01

Today haptic feedback can be designed and associated to audiovisual content (haptic-audiovisuals or HAV). Although there are multiple means to create individual haptic effects, the issue of how to properly adapt such effects on force-feedback devices has not been addressed and is mostly a manual endeavor. We propose a new approach for the haptic rendering of HAV, based on a washout filter for force-feedback devices. A body model and an inverse kinematics algorithm simulate the user's kinesthetic perception. Then, the haptic rendering is adapted in order to handle transitions between haptic effects and to optimize the amplitude of effects regarding the device capabilities. Results of a user study show that this new haptic rendering can successfully improve the HAV experience.

Training complexity is not decisive factor for improving adaptation to visual sensory conflict.

PubMed

Yang, Yang; Pu, Fang; Li, Shuyu; Li, Yan; Li, Deyu; Fan, Yubo

2012-01-01

Ground-based preflight training utilizing unusual visual stimuli is useful for decreasing the susceptibility to space motion sickness (SMS). The effectiveness of the sensorimotor adaptation training is affected by the training tasks, but what kind of task is more effective remains unknown. Whether the complexity is the decisive factor to consider for designing the training and if other factors are more important need to be analyzed. The results from the analysis can help to optimize the preflight training tasks for astronauts. Twenty right-handed subjects were asked to draw the right path of 45° rotated maze before and after 30 min training. Subjects wore an up-down reversing prism spectacle in test and training sessions. Two training tasks were performed: drawing the right path of the horizontal maze (complex task but with different orientation feature) and drawing the L-shape lines (easy task with same orientation feature). The error rate and the executing time were measured during the test. Paired samples t test was used to compare the effects of the two training tasks. After each training, the error rate and the executing time were significantly decreased. However, the training effectiveness of the easy task was better as the test was finished more quickly and accurately. The complexity is not always the decisive factor for designing the adaptation training task, e.g. the orientation feature is more important in this study. In order to accelerate the adaptation and to counter SMS, the task for astronauts preflight adaptation training could be simple activities with the key features.

Functional Assessment of Genetic Variants with Outcomes Adapted to Clinical Decision-Making

PubMed Central

Thouvenot, Pierre; Ben Yamin, Barbara; Fourrière, Lou; Lescure, Aurianne; Boudier, Thomas; Del Nery, Elaine; Chauchereau, Anne; Goldgar, David E.; Stoppa-Lyonnet, Dominique; Nicolas, Alain; Millot, Gaël A.

2016-01-01

Understanding the medical effect of an ever-growing number of human variants detected is a long term challenge in genetic counseling. Functional assays, based on in vitro or in vivo evaluations of the variant effects, provide essential information, but they require robust statistical validation, as well as adapted outputs, to be implemented in the clinical decision-making process. Here, we assessed 25 pathogenic and 15 neutral missense variants of the BRCA1 breast/ovarian cancer susceptibility gene in four BRCA1 functional assays. Next, we developed a novel approach that refines the variant ranking in these functional assays. Lastly, we developed a computational system that provides a probabilistic classification of variants, adapted to clinical interpretation. Using this system, the best functional assay exhibits a variant classification accuracy estimated at 93%. Additional theoretical simulations highlight the benefit of this ready-to-use system in the classification of variants after functional assessment, which should facilitate the consideration of functional evidences in the decision-making process after genetic testing. Finally, we demonstrate the versatility of the system with the classification of siRNAs tested for human cell growth inhibition in high throughput screening. PMID:27272900

Feedback for reinforcement learning based brain-machine interfaces using confidence metrics.

PubMed

Prins, Noeline W; Sanchez, Justin C; Prasad, Abhishek

2017-06-01

For brain-machine interfaces (BMI) to be used in activities of daily living by paralyzed individuals, the BMI should be as autonomous as possible. One of the challenges is how the feedback is extracted and utilized in the BMI. Our long-term goal is to create autonomous BMIs that can utilize an evaluative feedback from the brain to update the decoding algorithm and use it intelligently in order to adapt the decoder. In this study, we show how to extract the necessary evaluative feedback from a biologically realistic (synthetic) source, use both the quantity and the quality of the feedback, and how that feedback information can be incorporated into a reinforcement learning (RL) controller architecture to maximize its performance. Motivated by the perception-action-reward cycle (PARC) in the brain which links reward for cognitive decision making and goal-directed behavior, we used a reward-based RL architecture named Actor-Critic RL as the model. Instead of using an error signal towards building an autonomous BMI, we envision to use a reward signal from the nucleus accumbens (NAcc) which plays a key role in the linking of reward to motor behaviors. To deal with the complexity and non-stationarity of biological reward signals, we used a confidence metric which was used to indicate the degree of feedback accuracy. This confidence was added to the Actor's weight update equation in the RL controller architecture. If the confidence was high (>0.2), the BMI decoder used this feedback to update its parameters. However, when the confidence was low, the BMI decoder ignored the feedback and did not update its parameters. The range between high confidence and low confidence was termed as the 'ambiguous' region. When the feedback was within this region, the BMI decoder updated its weight at a lower rate than when fully confident, which was decided by the confidence. We used two biologically realistic models to generate synthetic data for MI (Izhikevich model) and NAcc (Humphries

Feedback for reinforcement learning based brain-machine interfaces using confidence metrics

NASA Astrophysics Data System (ADS)

Prins, Noeline W.; Sanchez, Justin C.; Prasad, Abhishek

2017-06-01

Objective. For brain-machine interfaces (BMI) to be used in activities of daily living by paralyzed individuals, the BMI should be as autonomous as possible. One of the challenges is how the feedback is extracted and utilized in the BMI. Our long-term goal is to create autonomous BMIs that can utilize an evaluative feedback from the brain to update the decoding algorithm and use it intelligently in order to adapt the decoder. In this study, we show how to extract the necessary evaluative feedback from a biologically realistic (synthetic) source, use both the quantity and the quality of the feedback, and how that feedback information can be incorporated into a reinforcement learning (RL) controller architecture to maximize its performance. Approach. Motivated by the perception-action-reward cycle (PARC) in the brain which links reward for cognitive decision making and goal-directed behavior, we used a reward-based RL architecture named Actor-Critic RL as the model. Instead of using an error signal towards building an autonomous BMI, we envision to use a reward signal from the nucleus accumbens (NAcc) which plays a key role in the linking of reward to motor behaviors. To deal with the complexity and non-stationarity of biological reward signals, we used a confidence metric which was used to indicate the degree of feedback accuracy. This confidence was added to the Actor’s weight update equation in the RL controller architecture. If the confidence was high (>0.2), the BMI decoder used this feedback to update its parameters. However, when the confidence was low, the BMI decoder ignored the feedback and did not update its parameters. The range between high confidence and low confidence was termed as the ‘ambiguous’ region. When the feedback was within this region, the BMI decoder updated its weight at a lower rate than when fully confident, which was decided by the confidence. We used two biologically realistic models to generate synthetic data for MI (Izhikevich

Lost in Search: (Mal-)Adaptation to Probabilistic Decision Environments in Children and Adults

ERIC Educational Resources Information Center

Betsch, Tilmann; Lehmann, Anne; Lindow, Stefanie; Lang, Anna; Schoemann, Martin

2016-01-01

Adaptive decision making in probabilistic environments requires individuals to use probabilities as weights in predecisional information searches and/or when making subsequent choices. Within a child-friendly computerized environment (Mousekids), we tracked 205 children's (105 children 5-6 years of age and 100 children 9-10 years of age) and 103…

Performance Feedback Processing Is Positively Biased As Predicted by Attribution Theory.

PubMed

Korn, Christoph W; Rosenblau, Gabriela; Rodriguez Buritica, Julia M; Heekeren, Hauke R

2016-01-01

A considerable literature on attribution theory has shown that healthy individuals exhibit a positivity bias when inferring the causes of evaluative feedback on their performance. They tend to attribute positive feedback internally (e.g., to their own abilities) but negative feedback externally (e.g., to environmental factors). However, all empirical demonstrations of this bias suffer from at least one of the three following drawbacks: First, participants directly judge explicit causes for their performance. Second, participants have to imagine events instead of experiencing them. Third, participants assess their performance only after receiving feedback and thus differences in baseline assessments cannot be excluded. It is therefore unclear whether the classically reported positivity bias generalizes to setups without these drawbacks. Here, we aimed at establishing the relevance of attributions for decision-making by showing an attribution-related positivity bias in a decision-making task. We developed a novel task, which allowed us to test how participants changed their evaluations in response to positive and negative feedback about performance. Specifically, we used videos of actors expressing different facial emotional expressions. Participants were first asked to evaluate the actors' credibility in expressing a particular emotion. After this initial rating, participants performed an emotion recognition task and did--or did not--receive feedback on their veridical performance. Finally, participants re-rated the actors' credibility, which provided a measure of how they changed their evaluations after feedback. Attribution theory predicts that participants change their evaluations of the actors' credibility toward the positive after receiving positive performance feedback and toward the negative after negative performance feedback. Our results were in line with this prediction. A control condition without feedback showed that correct or incorrect performance

Performance Feedback Processing Is Positively Biased As Predicted by Attribution Theory

PubMed Central

Rodriguez Buritica, Julia M.; Heekeren, Hauke R.

2016-01-01

A considerable literature on attribution theory has shown that healthy individuals exhibit a positivity bias when inferring the causes of evaluative feedback on their performance. They tend to attribute positive feedback internally (e.g., to their own abilities) but negative feedback externally (e.g., to environmental factors). However, all empirical demonstrations of this bias suffer from at least one of the three following drawbacks: First, participants directly judge explicit causes for their performance. Second, participants have to imagine events instead of experiencing them. Third, participants assess their performance only after receiving feedback and thus differences in baseline assessments cannot be excluded. It is therefore unclear whether the classically reported positivity bias generalizes to setups without these drawbacks. Here, we aimed at establishing the relevance of attributions for decision-making by showing an attribution-related positivity bias in a decision-making task. We developed a novel task, which allowed us to test how participants changed their evaluations in response to positive and negative feedback about performance. Specifically, we used videos of actors expressing different facial emotional expressions. Participants were first asked to evaluate the actors’ credibility in expressing a particular emotion. After this initial rating, participants performed an emotion recognition task and did—or did not—receive feedback on their veridical performance. Finally, participants re-rated the actors’ credibility, which provided a measure of how they changed their evaluations after feedback. Attribution theory predicts that participants change their evaluations of the actors’ credibility toward the positive after receiving positive performance feedback and toward the negative after negative performance feedback. Our results were in line with this prediction. A control condition without feedback showed that correct or incorrect

What works for you? Using teacher feedback to inform adaptations of pivotal response training for classroom use.

PubMed

Stahmer, Aubyn C; Suhrheinrich, Jessica; Reed, Sarah; Schreibman, Laura

2012-01-01

Several evidence-based practices (EBPs) have been identified as efficacious for the education of students with autism spectrum disorders (ASD). However, effectiveness research has rarely been conducted in schools and teachers express skepticism about the clinical utility of EBPs for the classroom. Innovative methods are needed to optimally adapt EBPs for community use. This study utilizes qualitative methods to identify perceived benefits and barriers of classroom implementation of a specific EBP for ASD, Pivotal Response Training (PRT). Teachers' perspectives on the components of PRT, use of PRT as a classroom intervention strategy, and barriers to the use of PRT were identified through guided discussion. Teachers found PRT valuable; however, they also found some components challenging. Specific teacher recommendations for adaptation and resource development are discussed. This process of obtaining qualitative feedback from frontline practitioners provides a generalizable model for researchers to collaborate with teachers to optimally promote EBPs for classroom use.

Adaptive feedback synchronisation of complex dynamical network with discrete-time communications and delayed nodes

NASA Astrophysics Data System (ADS)

Wang, Tong; Ding, Yongsheng; Zhang, Lei; Hao, Kuangrong

2016-08-01

This paper considered the synchronisation of continuous complex dynamical networks with discrete-time communications and delayed nodes. The nodes in the dynamical networks act in the continuous manner, while the communications between nodes are discrete-time; that is, they communicate with others only at discrete time instants. The communication intervals in communication period can be uncertain and variable. By using a piecewise Lyapunov-Krasovskii function to govern the characteristics of the discrete communication instants, we investigate the adaptive feedback synchronisation and a criterion is derived to guarantee the existence of the desired controllers. The globally exponential synchronisation can be achieved by the controllers under the updating laws. Finally, two numerical examples including globally coupled network and nearest-neighbour coupled networks are presented to demonstrate the validity and effectiveness of the proposed control scheme.

Method and apparatus for adaptive force and position control of manipulators

NASA Technical Reports Server (NTRS)

Seraji, Homayoun (Inventor)

1989-01-01

The present invention discloses systematic methods and apparatus for the design of real time controllers. Real-time control employs adaptive force/position by use of feedforward and feedback controllers, with the feedforward controller being the inverse of the linearized model of robot dynamics and containing only proportional-double-derivative terms is disclosed. The feedback controller, of the proportional-integral-derivative type, ensures that manipulator joints follow reference trajectories and the feedback controller achieves robust tracking of step-plus-exponential trajectories, all in real time. The adaptive controller includes adaptive force and position control within a hybrid control architecture. The adaptive controller, for force control, achieves tracking of desired force setpoints, and the adaptive position controller accomplishes tracking of desired position trajectories. Circuits in the adaptive feedback and feedforward controllers are varied by adaptation laws.

Wearable real-time and adaptive feedback device to face the stuttering: a knowledge-based telehealthcare proposal.

PubMed

Prado, Manuel; Roa, Laura M

2007-01-01

Despite first written references to permanent developmental stuttering occurred more than 2500 years ago, the mechanisms underlying this disorder are still unknown. This paper briefly reviews stuttering causal hypothesis and treatments, and presents the requirements that a new stuttering therapeutic device should verify. As a result of the analysis, an adaptive altered auditory feedback device based on a multimodal intelligent monitor, within the framework of a knowledge-based telehealthcare system, is presented. The subsequent discussion, based partly on the successful outcomes of a similar intelligent monitor, suggests that this novel device is feasible and could help to fill the gap between research and clinic.

Impacts of Agricultural Decision Making and Adaptive Management on Food Security in Africa

NASA Astrophysics Data System (ADS)

Caylor, K. K.; Evans, T. P.; Estes, L. D.; Sheffield, J.; Plale, B. A.; Attari, S.

2014-12-01

Despite massive investments in food aid, agricultural extension, and seed/fertilizer subsidies, nearly 1 billion people in the developing world are food insecure and vulnerable to climate variability. Sub-Saharan Africa is most vulnerable, as approximately 25% of its people are undernourished (FAO/FAOSTAT 2013) and 96% of its cropland is rainfed (FAO 2002). The ability of subsistence farmers to respond to changes in water availability involves both inter-and intra-seasonal adaptation. Adaptive capacity diminishes over the season as decisions are made, resources are used, and the set of possible futures becomes restricted. Assessing the intra-seasonal adaptive capacity of smallholders requires integrating physical models of hydrological and agricultural dynamics with farmer decision-making at fine temporal (e.g. weekly) and spatial (e.g. crop field) scales. However, there is an intrinsic challenge to modeling the dynamics of these sociohydrologic systems, because important and uncharacterized spatial and temporal scale mismatches exist between the level at which the water resource is best understood and the level at which human dynamics are more predictable. For example, the skill of current process-based land surface models is primarily confined to short-term (daily to weekly), national- to regional-scale assessments, and reliable agricultural yield estimates and forecasts for small-scale farming systems remain elusive. In contrast, process-based social science modeling has focused on agent-based approaches that generate fine-scale (individual to community) dynamics over rather coarse time scales (yearly to decadal). A major obstacle to addressing this mismatch is the fundamental fact that the highest skill domain of one framework is essentially unpredictable in the other. We present a coupled sociohydrological observation framework designed to addressing this gap, and demonstrate its utility to understand relationships between climate variability, decision making

Neural Correlates of Sequence Learning with Stochastic Feedback

ERIC Educational Resources Information Center

Averbeck, Bruno B.; Kilner, James; Frith, Christopher D.

2011-01-01

Although much is known about decision making under uncertainty when only a single step is required in the decision process, less is known about sequential decision making. We carried out a stochastic sequence learning task in which subjects had to use noisy feedback to learn sequences of button presses. We compared flat and hierarchical behavioral…

Uncertainty assessment of urban pluvial flood risk in a context of climate change adaptation decision making

NASA Astrophysics Data System (ADS)

Arnbjerg-Nielsen, Karsten; Zhou, Qianqian

2014-05-01

There has been a significant increase in climatic extremes in many regions. In Central and Northern Europe, this has led to more frequent and more severe floods. Along with improved flood modelling technologies this has enabled development of economic assessment of climate change adaptation to increasing urban flood risk. Assessment of adaptation strategies often requires a comprehensive risk-based economic analysis of current risk, drivers of change of risk over time, and measures to reduce the risk. However, such studies are often associated with large uncertainties. The uncertainties arise from basic assumptions in the economic analysis and the hydrological model, but also from the projection of future societies to local climate change impacts and suitable adaptation options. This presents a challenge to decision makers when trying to identify robust measures. We present an integrated uncertainty analysis, which can assess and quantify the overall uncertainty in relation to climate change adaptation to urban flash floods. The analysis is based on an uncertainty cascade that by means of Monte Carlo simulations of flood risk assessments incorporates climate change impacts as a key driver of risk changes over time. The overall uncertainty is then attributed to six bulk processes: climate change impact, urban rainfall-runoff processes, stage-depth functions, unit cost of repair, cost of adaptation measures, and discount rate. We apply the approach on an urban hydrological catchment in Odense, Denmark, and find that the uncertainty on the climate change impact appears to have the least influence on the net present value of the studied adaptation measures-. This does not imply that the climate change impact is not important, but that the uncertainties are not dominating when deciding on action or in-action. We then consider the uncertainty related to choosing between adaptation options given that a decision of action has been taken. In this case the major part of the

Can corrective feedback improve recognition memory?

PubMed

Kantner, Justin; Lindsay, D Stephen

2010-06-01

An understanding of the effects of corrective feedback on recognition memory can inform both recognition theory and memory training programs, but few published studies have investigated the issue. Although the evidence to date suggests that feedback does not improve recognition accuracy, few studies have directly examined its effect on sensitivity, and fewer have created conditions that facilitate a feedback advantage by encouraging controlled processing at test. In Experiment 1, null effects of feedback were observed following both deep and shallow encoding of categorized study lists. In Experiment 2, feedback robustly influenced response bias by allowing participants to discern highly uneven base rates of old and new items, but sensitivity remained unaffected. In Experiment 3, a false-memory procedure, feedback failed to attenuate false recognition of critical lures. In Experiment 4, participants were unable to use feedback to learn a simple category rule separating old items from new items, despite the fact that feedback was of substantial benefit in a nearly identical categorization task. The recognition system, despite a documented ability to utilize controlled strategic or inferential decision-making processes, appears largely impenetrable to a benefit of corrective feedback.

Reasoned Decision Making Without Math? Adaptability and Robustness in Response to Surprise.

PubMed

Smithson, Michael; Ben-Haim, Yakov

2015-10-01

Many real-world planning and decision problems are far too uncertain, too variable, and too complicated to support realistic mathematical models. Nonetheless, we explain the usefulness, in these situations, of qualitative insights from mathematical decision theory. We demonstrate the integration of info-gap robustness in decision problems in which surprise and ignorance are predominant and where personal and collective psychological factors are critical. We present practical guidelines for employing adaptable-choice strategies as a proxy for robustness against uncertainty. These guidelines include being prepared for more surprises than we intuitively expect, retaining sufficiently many options to avoid premature closure and conflicts among preferences, and prioritizing outcomes that are steerable, whose consequences are observable, and that do not entail sunk costs, resource depletion, or high transition costs. We illustrate these concepts and guidelines with the example of the medical management of the 2003 SARS outbreak in Vietnam. © 2015 Society for Risk Analysis.

Identifying Decision-Makers’ Science Needs for Adaptation to Climate-Related Impacts on Forest Ecosystem Services

NASA Astrophysics Data System (ADS)

Gordon, E.; Lukas, J.

2009-12-01

Through the Western Water Assessment RISA program, we are conducting a research project that will produce science synthesis information to help local, state, and federal decision-makers in Colorado and Wyoming develop adaptation strategies to deal with climate-related threats to forest ecosystem services, in particular bark beetle infestations and stand-replacing wildfires. We begin by using the problem orientation framework, a policy sciences methodology, to understand how decision-makers can most effectively address policy problems that threaten the attainment of socially accepted goals. By applying this framework to the challenges facing decision-makers, we more accurately identify specific areas where scientific research can improve decision-making. WWA researchers will next begin to connect decision-makers with relevant scientific literature and identify specific areas of future scientific research that will be most effective at addressing their needs.

Frequency adaptation in controlled stochastic resonance utilizing delayed feedback method: two-pole approximation for response function.

PubMed

Tutu, Hiroki

2011-06-01

Stochastic resonance (SR) enhanced by time-delayed feedback control is studied. The system in the absence of control is described by a Langevin equation for a bistable system, and possesses a usual SR response. The control with the feedback loop, the delay time of which equals to one-half of the period (2π/Ω) of the input signal, gives rise to a noise-induced oscillatory switching cycle between two states in the output time series, while its average frequency is just smaller than Ω in a small noise regime. As the noise intensity D approaches an appropriate level, the noise constructively works to adapt the frequency of the switching cycle to Ω, and this changes the dynamics into a state wherein the phase of the output signal is entrained to that of the input signal from its phase slipped state. The behavior is characterized by power loss of the external signal or response function. This paper deals with the response function based on a dichotomic model. A method of delay-coordinate series expansion, which reduces a non-Markovian transition probability flux to a series of memory fluxes on a discrete delay-coordinate system, is proposed. Its primitive implementation suggests that the method can be a potential tool for a systematic analysis of SR phenomenon with delayed feedback loop. We show that a D-dependent behavior of poles of a finite Laplace transform of the response function qualitatively characterizes the structure of the power loss, and we also show analytical results for the correlation function and the power spectral density.

What Works for You? Using Teacher Feedback to Inform Adaptations of Pivotal Response Training for Classroom Use

PubMed Central

Stahmer, Aubyn C.; Suhrheinrich, Jessica; Reed, Sarah; Schreibman, Laura

2012-01-01

Several evidence-based practices (EBPs) have been identified as efficacious for the education of students with autism spectrum disorders (ASD). However, effectiveness research has rarely been conducted in schools and teachers express skepticism about the clinical utility of EBPs for the classroom. Innovative methods are needed to optimally adapt EBPs for community use. This study utilizes qualitative methods to identify perceived benefits and barriers of classroom implementation of a specific EBP for ASD, Pivotal Response Training (PRT). Teachers' perspectives on the components of PRT, use of PRT as a classroom intervention strategy, and barriers to the use of PRT were identified through guided discussion. Teachers found PRT valuable; however, they also found some components challenging. Specific teacher recommendations for adaptation and resource development are discussed. This process of obtaining qualitative feedback from frontline practitioners provides a generalizable model for researchers to collaborate with teachers to optimally promote EBPs for classroom use. PMID:23209896

Biased feedback in brain-computer interfaces.

PubMed

Barbero, Alvaro; Grosse-Wentrup, Moritz

2010-07-27

Even though feedback is considered to play an important role in learning how to operate a brain-computer interface (BCI), to date no significant influence of feedback design on BCI-performance has been reported in literature. In this work, we adapt a standard motor-imagery BCI-paradigm to study how BCI-performance is affected by biasing the belief subjects have on their level of control over the BCI system. Our findings indicate that subjects already capable of operating a BCI are impeded by inaccurate feedback, while subjects normally performing on or close to chance level may actually benefit from an incorrect belief on their performance level. Our results imply that optimal feedback design in BCIs should take into account a subject's current skill level.

Comparison between hybrid feedforward-feedback, feedforward, and feedback structures for active noise control of fMRI noise.

PubMed

Reddy, Rajiv M; Panahi, Issa M S

2008-01-01

The performance of FIR feedforward, IIR feedforward, FIR feedback, hybrid FIR feedforward--FIR feedback, and hybrid IIR feedforward - FIR feedback structures for active noise control (ANC) are compared for an fMRI noise application. The filtered-input normalized least squares (FxNLMS) algorithm is used to update the coefficients of the adaptive filters in all these structures. Realistic primary and secondary paths of an fMRI bore are used by estimating them on a half cylindrical acrylic bore of 0.76 m (D)x1.52 m (L). Detailed results of the performance of the ANC system are presented in the paper for each of these structures. We find that the IIR feedforward structure produces most of the performance improvement in the hybrid IIR feedforward - FIR feedback structure and adding the feedback structure becomes almost redundant in the case of fMRI noise.

Empirical Analysis of EEG and ERPs for Psychophysiological Adaptive Task Allocation

NASA Technical Reports Server (NTRS)

Prinzel, Lawrence J., III; Pope, Alan T.; Freeman, Frederick G.; Scerbo, Mark W.; Mikulka, Peter J.

2001-01-01

The present study was designed to test the efficacy of using Electroencephalogram (EEG) and Event-Related Potentials (ERPs) for making task allocation decisions. Thirty-six participants were randomly assigned to an experimental, yoked, or control group condition. Under the experimental condition, a tracking task was switched between task modes based upon the participant's EEG. The results showed that the use of adaptive aiding improved performance and lowered subjective workload under negative feedback as predicted. Additionally, participants in the adaptive group had significantly lower RMSE and NASA-TLX ratings than participants in either the yoked or control group conditions. Furthermore, the amplitudes of the N1 and P3 ERP components were significantly larger under the experimental group condition than under either the yoked or control group conditions. These results are discussed in terms of the implications for adaptive automation design.

Adaptive output feedback control of uncertain nonlinear systems using single-hidden-layer neural networks.

PubMed

Hovakimyan, N; Nardi, F; Calise, A; Kim, Nakwan

2002-01-01

We consider adaptive output feedback control of uncertain nonlinear systems, in which both the dynamics and the dimension of the regulated system may be unknown. However, the relative degree of the regulated output is assumed to be known. Given a smooth reference trajectory, the problem is to design a controller that forces the system measurement to track it with bounded errors. The classical approach requires a state observer. Finding a good observer for an uncertain nonlinear system is not an obvious task. We argue that it is sufficient to build an observer for the output tracking error. Ultimate boundedness of the error signals is shown through Lyapunov's direct method. The theoretical results are illustrated in the design of a controller for a fourth-order nonlinear system of relative degree two and a high-bandwidth attitude command system for a model R-50 helicopter.

Adaptation of a hospital electronic referral system for antimicrobial stewardship prospective audit and feedback rounds.

PubMed

Rawlins, Matthew D M; Raby, Edward; Sanfilippo, Frank M; Douglass, Rae; Chambers, Jonathan; McLellan, Duncan; Dyer, John R

2018-05-04

To evaluate the impact of the adaptation of an existing electronic referral application for use in antimicrobial stewardship prospective audit and feedback rounds (antimicrobial rounds). Retrospective, single-centre observational study between March 2015 and February 2016. A new quaternary referral centre. Adults referred for antimicrobial rounds outside of the intensive care and haematology units. Adaptation of an electronic referral application used by medical and allied health staff. A questionnaire-style referral form was designed to capture patient clinical details using a combination of free text and dropdown menus. Clinical pharmacists were educated and granted access to the system. The proportion of completed electronic referrals of total round reviews by month for the 12 months after implementation. The time from request to completion of reviews. The impact on adherence to advice provided on rounds. The impact on the institutional usage of broad-spectrum antibiotics: glycopeptides, carbapenems, third and fourth generation cephalosporins, fluoroquinolones and piperacillin/tazobactam. Over the study period, the proportion of electronic referrals of completed antimicrobial round reviews increased from 59% to 88% (P < 0.001); 75.7% of accepted electronic referrals were seen within 48 h of request. The proportion of advice ignored fell from 18% to 8.5% (P < 0.001). Piperacillin/tazobactam, fluoroquinolone and glycopeptide usage decreased. The adaptation of an electronic referral application for antimicrobial rounds was associated with increased adherence to advice and reduction in use in target antibiotics. Our model is now used at other institutions.

Adapting Cognitive Task Analysis to Investigate Clinical Decision Making and Medication Safety Incidents.

PubMed

Russ, Alissa L; Militello, Laura G; Glassman, Peter A; Arthur, Karen J; Zillich, Alan J; Weiner, Michael

2017-05-03

Cognitive task analysis (CTA) can yield valuable insights into healthcare professionals' cognition and inform system design to promote safe, quality care. Our objective was to adapt CTA-the critical decision method, specifically-to investigate patient safety incidents, overcome barriers to implementing this method, and facilitate more widespread use of cognitive task analysis in healthcare. We adapted CTA to facilitate recruitment of healthcare professionals and developed a data collection tool to capture incidents as they occurred. We also leveraged the electronic health record (EHR) to expand data capture and used EHR-stimulated recall to aid reconstruction of safety incidents. We investigated 3 categories of medication-related incidents: adverse drug reactions, drug-drug interactions, and drug-disease interactions. Healthcare professionals submitted incidents, and a subset of incidents was selected for CTA. We analyzed several outcomes to characterize incident capture and completed CTA interviews. We captured 101 incidents. Eighty incidents (79%) met eligibility criteria. We completed 60 CTA interviews, 20 for each incident category. Capturing incidents before interviews allowed us to shorten the interview duration and reduced reliance on healthcare professionals' recall. Incorporating the EHR into CTA enriched data collection. The adapted CTA technique was successful in capturing specific categories of safety incidents. Our approach may be especially useful for investigating safety incidents that healthcare professionals "fix and forget." Our innovations to CTA are expected to expand the application of this method in healthcare and inform a wide range of studies on clinical decision making and patient safety.

The Teaching Decisions Simulation: An Interactive Vehicle for Mapping Teaching Decisions.

ERIC Educational Resources Information Center

Strang, Harold R.

1996-01-01

Describes the Teaching Decisions Simulation, a program that allows participants to make decisions regarding lesson plan activities and student and teacher spatial arrangement or interactions. Postlesson feedback includes variables such as completion time and performance measures. Experienced teachers exhibited more deliberation in completing the…

When to throw the switch: The adaptiveness of modifying emotion regulation strategies based on affective and physiological feedback.

PubMed

Birk, Jeffrey L; Bonanno, George A

2016-08-01

Particular emotion regulation (ER) strategies are beneficial in certain contexts, but little is known about the adaptiveness of switching strategies after implementing an initial strategy. Research and theory on regulatory flexibility suggest that people switch strategies dynamically and that internal states provide feedback indicating when switches are appropriate. Frequent switching may predict positive outcomes among people who respond to this feedback. We investigated whether internal feedback (particularly corrugator activity, heart rate, or subjective negative intensity) guides people to switch to an optimal (i.e., distraction) but not nonoptimal (i.e., reappraisal) strategy for regulating strong emotion. We also tested whether switching frequency and responsiveness to internal feedback (RIF) together predict well-being. While attempting to regulate emotion elicited by unpleasant pictures, participants could switch to an optimal (Study 1; reappraisal-to-distraction order; N = 90) or nonoptimal (Study 2; distraction-to-reappraisal order; N = 95) strategy for high-arousal emotion. A RIF score for each emotion measure indexed the relative strength of emotion during the initial phase for trials on which participants later switched strategies. As hypothesized, negative intensity, corrugator activity, and the magnitude of heart rate deceleration during this early phase were higher on switch than maintain trials in Study 1 only. Critically, in Study 1 only, greater switching frequency predicted higher and lower life satisfaction for participants with high and low corrugator RIF, respectively, even after controlling for reappraisal success. Individual differences in RIF may contribute to subjective well-being provided that the direction of strategy switching aligns well with regulatory preferences for high emotion. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Prefrontal Neural Activity When Feedback Is Not Relevant to Adjust Performance

PubMed Central

Özyurt, Jale; Rietze, Mareike; Thiel, Christiane M.

2012-01-01

It has been shown that the rostral cingulate zone (RCZ) in humans uses both positive and negative feedback to evaluate performance and to flexibly adjust behaviour. Less is known on how the feedback types are processed by the RCZ and other prefrontal brain areas, when feedback can only be used to evaluate performance, but cannot be used to adjust behaviour. The present fMRI study aimed at investigating feedback that can only be used to evaluate performance in a word-learning paradigm. One group of volunteers (N = 17) received informative, performance-dependent positive or negative feedback after each trial. Since new words had to be learnt in each trial, the feedback could not be used for task-specific adaptations. The other group (N = 17) always received non-informative feedback, providing neither information about performance nor about possible task-specific adaptations. Effects of the informational value of feedback were assessed between-subjects, comparing trials with positive and negative informative feedback to non-informative feedback. Effects of feedback valence were assessed by comparing neural activity to positive and negative feedback within the informative-feedback group. Our results show that several prefrontal regions, including the pre-SMA, the inferior frontal cortex and the insula were sensitive to both, the informational value and the valence aspect of the feedback with stronger activations to informative as compared to non-informative feedback and to informative negative compared to informative positive feedback. The only exception was RCZ which was sensitive to the informational value of the feedback, but not to feedback valence. The findings indicate that outcome information per se is sufficient to activate prefrontal brain regions, with the RCZ being the only prefrontal brain region which is equally sensitive to positive and negative feedback. PMID:22615774

Learning in an interactive simulation tool against landslide risks: the role of strength and availability of experiential feedback

NASA Astrophysics Data System (ADS)

Chaturvedi, Pratik; Arora, Akshit; Dutt, Varun

2018-06-01

Feedback via simulation tools is likely to help people improve their decision-making against natural disasters. However, little is known on how differing strengths of experiential feedback and feedback's availability in simulation tools influence people's decisions against landslides. We tested the influence of differing strengths of experiential feedback and feedback's availability on people's decisions against landslides in Mandi, Himachal Pradesh, India. Experiential feedback (high or low) and feedback's availability (present or absent) were varied across four between-subject conditions in a tool called the Interactive Landslide Simulation (ILS): high damage with feedback present, high damage with feedback absent, low damage with feedback present, and low damage with feedback absent. In high-damage conditions, the probabilities of damages to life and property due to landslides were 10 times higher than those in the low-damage conditions. In feedback-present conditions, experiential feedback was provided in numeric, text, and graphical formats in ILS. In feedback-absent conditions, the probabilities of damages were described; however, there was no experiential feedback present. Investments were greater in conditions where experiential feedback was present and damages were high compared to conditions where experiential feedback was absent and damages were low. Furthermore, only high-damage feedback produced learning in ILS. Simulation tools like ILS seem appropriate for landslide risk communication and for performing what-if analyses.

Adaptation and Evaluation of a Multi-Criteria Decision Analysis Model for Lyme Disease Prevention

PubMed Central

Aenishaenslin, Cécile; Gern, Lise; Michel, Pascal; Ravel, André; Hongoh, Valérie; Waaub, Jean-Philippe; Milord, François; Bélanger, Denise

2015-01-01

Designing preventive programs relevant to vector-borne diseases such as Lyme disease (LD) can be complex given the need to include multiple issues and perspectives into prioritizing public health actions. A multi-criteria decision aid (MCDA) model was previously used to rank interventions for LD prevention in Quebec, Canada, where the disease is emerging. The aim of the current study was to adapt and evaluate the decision model constructed in Quebec under a different epidemiological context, in Switzerland, where LD has been endemic for the last thirty years. The model adaptation was undertaken with a group of Swiss stakeholders using a participatory approach. The PROMETHEE method was used for multi-criteria analysis. Key elements and results of the MCDA model are described and contrasted with the Quebec model. All criteria and most interventions of the MCDA model developed for LD prevention in Quebec were directly transferable to the Swiss context. Four new decision criteria were added, and the list of proposed interventions was modified. Based on the overall group ranking, interventions targeting human populations were prioritized in the Swiss model, with the top ranked action being the implementation of a large communication campaign. The addition of criteria did not significantly alter the intervention rankings, but increased the capacity of the model to discriminate between highest and lowest ranked interventions. The current study suggests that beyond the specificity of the MCDA models developed for Quebec and Switzerland, their general structure captures the fundamental and common issues that characterize the complexity of vector-borne disease prevention. These results should encourage public health organizations to adapt, use and share MCDA models as an effective and functional approach to enable the integration of multiple perspectives and considerations in the prevention and control of complex public health issues such as Lyme disease or other vector

Adaptation and Evaluation of a Multi-Criteria Decision Analysis Model for Lyme Disease Prevention.

PubMed

Aenishaenslin, Cécile; Gern, Lise; Michel, Pascal; Ravel, André; Hongoh, Valérie; Waaub, Jean-Philippe; Milord, François; Bélanger, Denise

2015-01-01

Designing preventive programs relevant to vector-borne diseases such as Lyme disease (LD) can be complex given the need to include multiple issues and perspectives into prioritizing public health actions. A multi-criteria decision aid (MCDA) model was previously used to rank interventions for LD prevention in Quebec, Canada, where the disease is emerging. The aim of the current study was to adapt and evaluate the decision model constructed in Quebec under a different epidemiological context, in Switzerland, where LD has been endemic for the last thirty years. The model adaptation was undertaken with a group of Swiss stakeholders using a participatory approach. The PROMETHEE method was used for multi-criteria analysis. Key elements and results of the MCDA model are described and contrasted with the Quebec model. All criteria and most interventions of the MCDA model developed for LD prevention in Quebec were directly transferable to the Swiss context. Four new decision criteria were added, and the list of proposed interventions was modified. Based on the overall group ranking, interventions targeting human populations were prioritized in the Swiss model, with the top ranked action being the implementation of a large communication campaign. The addition of criteria did not significantly alter the intervention rankings, but increased the capacity of the model to discriminate between highest and lowest ranked interventions. The current study suggests that beyond the specificity of the MCDA models developed for Quebec and Switzerland, their general structure captures the fundamental and common issues that characterize the complexity of vector-borne disease prevention. These results should encourage public health organizations to adapt, use and share MCDA models as an effective and functional approach to enable the integration of multiple perspectives and considerations in the prevention and control of complex public health issues such as Lyme disease or other vector

Decision support for patient care: implementing cybernetics.

PubMed

Ozbolt, Judy; Ozdas, Asli; Waitman, Lemuel R; Smith, Janis B; Brennan, Grace V; Miller, Randolph A

2004-01-01

The application of principles and methods of cybernetics permits clinicians and managers to use feedback about care effectiveness and resource expenditure to improve quality and to control costs. Keys to the process are the specification of therapeutic goals and the creation of an organizational culture that supports the use of feedback to improve care. Daily feedback on the achievement of each patient's therapeutic goals provides tactical decision support, enabling clinicians to adjust care as needed. Monthly or quarterly feedback on aggregated goal achievement for all patients on a clinical pathway provides strategic decision support, enabling clinicians and managers to identify problems with supposed "best practices" and to test hypotheses about solutions. Work is underway at Vanderbilt University Medical Center to implement feedback loops in care and management processes and to evaluate the effects.

Torque ripple reduction of brushless DC motor based on adaptive input-output feedback linearization.

PubMed

Shirvani Boroujeni, M; Markadeh, G R Arab; Soltani, J

2017-09-01

Torque ripple reduction of Brushless DC Motors (BLDCs) is an interesting subject in variable speed AC drives. In this paper at first, a mathematical expression for torque ripple harmonics is obtained. Then for a non-ideal BLDC motor with known harmonic contents of back-EMF, calculation of desired reference current amplitudes, which are required to eliminate some selected harmonics of torque ripple, are reviewed. In order to inject the reference harmonic currents to the motor windings, an Adaptive Input-Output Feedback Linearization (AIOFBL) control is proposed, which generates the reference voltages for three phases voltage source inverter in stationary reference frame. Experimental results are presented to show the capability and validity of the proposed control method and are compared with the vector control in Multi-Reference Frame (MRF) and Pseudo-Vector Control (P-VC) method results. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Managing for climate change on protected areas: An adaptive management decision making framework.

PubMed

Tanner-McAllister, Sherri L; Rhodes, Jonathan; Hockings, Marc

2017-12-15

Current protected area management is becoming more challenging with advancing climate change and current park management techniques may not be adequate to adapt for effective management into the future. The framework presented here provides an adaptive management decision making process to assist protected area managers with adapting on-park management to climate change. The framework sets out a 4 step process. One, a good understanding of the park's context within climate change. Secondly, a thorough understanding of the park management systems including governance, planning and management systems. Thirdly, a series of management options set out as an accept/prevent change style structure, including a systematic assessment of those options. The adaptive approaches are defined as acceptance of anthropogenic climate change impact and attempt to adapt to a new climatic environment or prevention of change and attempt to maintain current systems under new climatic variations. Last, implementation and monitoring of long term trends in response to ecological responses to management interventions and assessing management effectiveness. The framework addresses many issues currently with park management in dealing with climate change including the considerable amount of research focussing on 'off-reserve' strategies, and threats and stress focused in situ park management. Copyright © 2017 Elsevier Ltd. All rights reserved.

Building Adaptive Capacity with the Delphi Method and Mediated Modeling for Water Quality and Climate Change Adaptation in Lake Champlain Basin

NASA Astrophysics Data System (ADS)

Coleman, S.; Hurley, S.; Koliba, C.; Zia, A.; Exler, S.

2014-12-01

Eutrophication and nutrient pollution of surface waters occur within complex governance, social, hydrologic and biophysical basin contexts. The pervasive and perennial nutrient pollution in Lake Champlain Basin, despite decades of efforts, exemplifies problems found across the world's surface waters. Stakeholders with diverse values, interests, and forms of explicit and tacit knowledge determine water quality impacts through land use, agricultural and water resource decisions. Uncertainty, ambiguity and dynamic feedback further complicate the ability to promote the continual provision of water quality and ecosystem services. Adaptive management of water resources and land use requires mechanisms to allow for learning and integration of new information over time. The transdisciplinary Research on Adaptation to Climate Change (RACC) team is working to build regional adaptive capacity in Lake Champlain Basin while studying and integrating governance, land use, hydrological, and biophysical systems to evaluate implications for adaptive management. The RACC team has engaged stakeholders through mediated modeling workshops, online forums, surveys, focus groups and interviews. In March 2014, CSS2CC.org, an interactive online forum to source and identify adaptive interventions from a group of stakeholders across sectors was launched. The forum, based on the Delphi Method, brings forward the collective wisdom of stakeholders and experts to identify potential interventions and governance designs in response to scientific uncertainty and ambiguity surrounding the effectiveness of any strategy, climate change impacts, and the social and natural systems governing water quality and eutrophication. A Mediated Modeling Workshop followed the forum in May 2014, where participants refined and identified plausible interventions under different governance, policy and resource scenarios. Results from the online forum and workshop can identify emerging consensus across scales and sectors

Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning.

PubMed

Kastner, Lucas; Kube, Jana; Villringer, Arno; Neumann, Jane

2017-01-01

Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1) Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2) Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3) Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning.

Thyroid Allostasis–Adaptive Responses of Thyrotropic Feedback Control to Conditions of Strain, Stress, and Developmental Programming

PubMed Central

Chatzitomaris, Apostolos; Hoermann, Rudolf; Midgley, John E.; Hering, Steffen; Urban, Aline; Dietrich, Barbara; Abood, Assjana; Klein, Harald H.; Dietrich, Johannes W.

2017-01-01

The hypothalamus–pituitary–thyroid feedback control is a dynamic, adaptive system. In situations of illness and deprivation of energy representing type 1 allostasis, the stress response operates to alter both its set point and peripheral transfer parameters. In contrast, type 2 allostatic load, typically effective in psychosocial stress, pregnancy, metabolic syndrome, and adaptation to cold, produces a nearly opposite phenotype of predictive plasticity. The non-thyroidal illness syndrome (NTIS) or thyroid allostasis in critical illness, tumors, uremia, and starvation (TACITUS), commonly observed in hospitalized patients, displays a historically well-studied pattern of allostatic thyroid response. This is characterized by decreased total and free thyroid hormone concentrations and varying levels of thyroid-stimulating hormone (TSH) ranging from decreased (in severe cases) to normal or even elevated (mainly in the recovery phase) TSH concentrations. An acute versus chronic stage (wasting syndrome) of TACITUS can be discerned. The two types differ in molecular mechanisms and prognosis. The acute adaptation of thyroid hormone metabolism to critical illness may prove beneficial to the organism, whereas the far more complex molecular alterations associated with chronic illness frequently lead to allostatic overload. The latter is associated with poor outcome, independently of the underlying disease. Adaptive responses of thyroid homeostasis extend to alterations in thyroid hormone concentrations during fetal life, periods of weight gain or loss, thermoregulation, physical exercise, and psychiatric diseases. The various forms of thyroid allostasis pose serious problems in differential diagnosis of thyroid disease. This review article provides an overview of physiological mechanisms as well as major diagnostic and therapeutic implications of thyroid allostasis under a variety of developmental and straining conditions. PMID:28775711

Using engineering control principles to inform the design of adaptive interventions: a conceptual introduction.

PubMed

Rivera, Daniel E; Pew, Michael D; Collins, Linda M

2007-05-01

The goal of this paper is to describe the role that control engineering principles can play in developing and improving the efficacy of adaptive, time-varying interventions. It is demonstrated that adaptive interventions constitute a form of feedback control system in the context of behavioral health. Consequently, drawing from ideas in control engineering has the potential to significantly inform the analysis, design, and implementation of adaptive interventions, leading to improved adherence, better management of limited resources, a reduction of negative effects, and overall more effective interventions. This article illustrates how to express an adaptive intervention in control engineering terms, and how to use this framework in a computer simulation to investigate the anticipated impact of intervention design choices on efficacy. The potential benefits of operationalizing decision rules based on control engineering principles are particularly significant for adaptive interventions that involve multiple components or address co-morbidities, situations that pose significant challenges to conventional clinical practice.

Using Engineering Control Principles to Inform the Design of Adaptive Interventions: A Conceptual Introduction

PubMed Central

Rivera, Daniel E.; Pew, Michael D.; Collins, Linda M.

2007-01-01

The goal of this paper is to describe the role that control engineering principles can play in developing and improving the efficacy of adaptive, time-varying interventions. It is demonstrated that adaptive interventions constitute a form of feedback control system in the context of behavioral health. Consequently, drawing from ideas in control engineering has the potential to significantly inform the analysis, design, and implementation of adaptive interventions, leading to improved adherence, better management of limited resources, a reduction of negative effects, and overall more effective interventions. This article illustrates how to express an adaptive intervention in control engineering terms, and how to use this framework in a computer simulation to investigate the anticipated impact of intervention design choices on efficacy. The potential benefits of operationalizing decision rules based on control engineering principles are particularly significant for adaptive interventions that involve multiple components or address co-morbidities, situations that pose significant challenges to conventional clinical practice. PMID:17169503

Cal-Adapt: California's Climate Data Resource and Interactive Toolkit

NASA Astrophysics Data System (ADS)

Thomas, N.; Mukhtyar, S.; Wilhelm, S.; Galey, B.; Lehmer, E.

2016-12-01

Cal-Adapt is a web-based application that provides an interactive toolkit and information clearinghouse to help agencies, communities, local planners, resource managers, and the public understand climate change risks and impacts at the local level. The website offers interactive, visually compelling, and useful data visualization tools that show how climate change might affect California using downscaled continental climate data. Cal-Adapt is supporting California's Fourth Climate Change Assessment through providing access to the wealth of modeled and observed data and adaption-related information produced by California's scientific community. The site has been developed by UC Berkeley's Geospatial Innovation Facility (GIF) in collaboration with the California Energy Commission's (CEC) Research Program. The Cal-Adapt website allows decision makers, scientists and residents of California to turn research results and climate projections into effective adaptation decisions and policies. Since its release to the public in June 2011, Cal-Adapt has been visited by more than 94,000 unique visitors from over 180 countries, all 50 U.S. states, and 689 California localities. We will present several key visualizations that have been employed by Cal-Adapt's users to support their efforts to understand local impacts of climate change, indicate the breadth of data available, and delineate specific use cases. Recently, CEC and GIF have been developing and releasing Cal-Adapt 2.0, which includes updates and enhancements that are increasing its ease of use, information value, visualization tools, and data accessibility. We showcase how Cal-Adapt is evolving in response to feedback from a variety of sources to present finer-resolution downscaled data, and offer an open API that allows other organization to access Cal-Adapt climate data and build domain specific visualization and planning tools. Through a combination of locally relevant information, visualization tools, and access to

Optimal Fault-Tolerant Control for Discrete-Time Nonlinear Strict-Feedback Systems Based on Adaptive Critic Design.

PubMed

Wang, Zhanshan; Liu, Lei; Wu, Yanming; Zhang, Huaguang

2018-06-01

This paper investigates the problem of optimal fault-tolerant control (FTC) for a class of unknown nonlinear discrete-time systems with actuator fault in the framework of adaptive critic design (ACD). A pivotal highlight is the adaptive auxiliary signal of the actuator fault, which is designed to offset the effect of the fault. The considered systems are in strict-feedback forms and involve unknown nonlinear functions, which will result in the causal problem. To solve this problem, the original nonlinear systems are transformed into a novel system by employing the diffeomorphism theory. Besides, the action neural networks (ANNs) are utilized to approximate a predefined unknown function in the backstepping design procedure. Combined the strategic utility function and the ACD technique, a reinforcement learning algorithm is proposed to set up an optimal FTC, in which the critic neural networks (CNNs) provide an approximate structure of the cost function. In this case, it not only guarantees the stability of the systems, but also achieves the optimal control performance as well. In the end, two simulation examples are used to show the effectiveness of the proposed optimal FTC strategy.

Enhanced Adaptive Management: Integrating Decision Analysis, Scenario Analysis and Environmental Modeling for the Everglades

PubMed Central

Convertino, Matteo; Foran, Christy M.; Keisler, Jeffrey M.; Scarlett, Lynn; LoSchiavo, Andy; Kiker, Gregory A.; Linkov, Igor

2013-01-01

We propose to enhance existing adaptive management efforts with a decision-analytical approach that can guide the initial selection of robust restoration alternative plans and inform the need to adjust these alternatives in the course of action based on continuously acquired monitoring information and changing stakeholder values. We demonstrate an application of enhanced adaptive management for a wetland restoration case study inspired by the Florida Everglades restoration effort. We find that alternatives designed to reconstruct the pre-drainage flow may have a positive ecological impact, but may also have high operational costs and only marginally contribute to meeting other objectives such as reduction of flooding. Enhanced adaptive management allows managers to guide investment in ecosystem modeling and monitoring efforts through scenario and value of information analyses to support optimal restoration strategies in the face of uncertain and changing information. PMID:24113217

Moderate social sensitivity in a risky context supports adaptive decision making in adolescence: evidence from brain and behavior.

PubMed

van Hoorn, Jorien; McCormick, Ethan M; Telzer, Eva H

2018-05-01

Adolescence is a time of increased social-affective sensitivity, which is often related to heightened health-risk behaviors. However, moderate levels of social sensitivity, relative to either low (social vacuum) or high levels (exceptionally attuned), may confer benefits as it facilitates effective navigation of the social world. The present fMRI study tested a curvilinear relationship between social sensitivity and adaptive decision-making. Participants (ages 12-16; N = 35) played the Social Analogue Risk Task, which measures participants' willingness to knock on doors in order to earn points. With each knock, the facial expression of the house's resident shifted from happy to somewhat angrier. If the resident became too angry, the door slammed and participants lost points. Social sensitivity was defined as the extent to which adolescents adjusted their risky choices based on shifting facial expressions. Results confirmed a curvilinear relationship between social sensitivity and self-reported adaptive decision-making at the behavioral and neural level. Moderate adolescent social sensitivity was modulated via heightened tracking of social cues in the temporoparietal junction, insula and dorsolateral prefrontal cortex and related to adaptive decision-making. These findings suggest that social-affective sensitivity may positively impact outcomes in adolescence and have implications for interventions to help adolescents reach mature social goals into adulthood.

Full Gradient Solution to Adaptive Hybrid Control

NASA Technical Reports Server (NTRS)

Bean, Jacob; Schiller, Noah H.; Fuller, Chris

2017-01-01

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

Full Gradient Solution to Adaptive Hybrid Control

NASA Technical Reports Server (NTRS)

Bean, Jacob; Schiller, Noah H.; Fuller, Chris

2016-01-01

This paper focuses on the adaptation mechanisms in adaptive hybrid controllers. Most adaptive hybrid controllers update two filters individually according to the filtered-reference least mean squares (FxLMS) algorithm. Because this algorithm was derived for feedforward control, it does not take into account the presence of a feedback loop in the gradient calculation. This paper provides a derivation of the proper weight vector gradient for hybrid (or feedback) controllers that takes into account the presence of feedback. In this formulation, a single weight vector is updated rather than two individually. An internal model structure is assumed for the feedback part of the controller. The full gradient is equivalent to that used in the standard FxLMS algorithm with the addition of a recursive term that is a function of the modeling error. Some simulations are provided to highlight the advantages of using the full gradient in the weight vector update rather than the approximation.

Modeling trial by trial and block feedback in perceptual learning

PubMed Central

Liu, Jiajuan; Dosher, Barbara; Lu, Zhong-Lin

2014-01-01

Feedback has been shown to play a complex role in visual perceptual learning. It is necessary for performance improvement in some conditions while not others. Different forms of feedback, such as trial-by-trial feedback or block feedback, may both facilitate learning, but with different mechanisms. False feedback can abolish learning. We account for all these results with the Augmented Hebbian Reweight Model (AHRM). Specifically, three major factors in the model advance performance improvement: the external trial-by-trial feedback when available, the self-generated output as an internal feedback when no external feedback is available, and the adaptive criterion control based on the block feedback. Through simulating a comprehensive feedback study (Herzog & Fahle 1997, Vision Research, 37 (15), 2133–2141), we show that the model predictions account for the pattern of learning in seven major feedback conditions. The AHRM can fully explain the complex empirical results on the role of feedback in visual perceptual learning. PMID:24423783

Results of adaptive feedforward on GTA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ziomek, C.D.; Denney, P.M.; Regan, A.H.

1993-01-01

This paper presents the results of the adaptive feedforward system in use on the Ground Test Accelerator (GTA). The adaptive feedforward system was shown to correct repetitive, high-frequency errors in the amplitude and phase of the RF field of the pulsed accelerator. The adaptive feedforward system was designed as an augmentation to the RF field feedback control system and was able to extend the closed-loop bandwidth and disturbance rejection by a factor of ten. Within a second implementation, the adaptive feedforward hardware was implemented in place of the feedback control system and was shown to negate both beam transients andmore » phase droop in the klystron amplifier.« less

Results of adaptive feedforward on GTA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ziomek, C.D.; Denney, P.M.; Regan, A.H.

1993-06-01

This paper presents the results of the adaptive feedforward system in use on the Ground Test Accelerator (GTA). The adaptive feedforward system was shown to correct repetitive, high-frequency errors in the amplitude and phase of the RF field of the pulsed accelerator. The adaptive feedforward system was designed as an augmentation to the RF field feedback control system and was able to extend the closed-loop bandwidth and disturbance rejection by a factor of ten. Within a second implementation, the adaptive feedforward hardware was implemented in place of the feedback control system and was shown to negate both beam transients andmore » phase droop in the klystron amplifier.« less

Adaptation effects in static postural control by providing simultaneous visual feedback of center of pressure and center of gravity.

PubMed

Takeda, Kenta; Mani, Hiroki; Hasegawa, Naoya; Sato, Yuki; Tanaka, Shintaro; Maejima, Hiroshi; Asaka, Tadayoshi

2017-07-19

The benefit of visual feedback of the center of pressure (COP) on quiet standing is still debatable. This study aimed to investigate the adaptation effects of visual feedback training using both the COP and center of gravity (COG) during quiet standing. Thirty-four healthy young adults were divided into three groups randomly (COP + COG, COP, and control groups). A force plate was used to calculate the coordinates of the COP in the anteroposterior (COP AP ) and mediolateral (COP ML ) directions. A motion analysis system was used to calculate the coordinates of the center of mass (COM) in both directions (COM AP and COM ML ). The coordinates of the COG in the AP direction (COG AP ) were obtained from the force plate signals. Augmented visual feedback was presented on a screen in the form of fluctuation circles in the vertical direction that moved upward as the COP AP and/or COG AP moved forward and vice versa. The COP + COG group received the real-time COP AP and COG AP feedback simultaneously, whereas the COP group received the real-time COP AP feedback only. The control group received no visual feedback. In the training session, the COP + COG group was required to maintain an even distance between the COP AP and COG AP and reduce the COG AP fluctuation, whereas the COP group was required to reduce the COP AP fluctuation while standing on a foam pad. In test sessions, participants were instructed to keep their standing posture as quiet as possible on the foam pad before (pre-session) and after (post-session) the training sessions. In the post-session, the velocity and root mean square of COM AP in the COP + COG group were lower than those in the control group. In addition, the absolute value of the sum of the COP - COM distances in the COP + COG group was lower than that in the COP group. Furthermore, positive correlations were found between the COM AP velocity and COP - COM parameters. The results suggest that the novel visual feedback

Animal personality and state-behaviour feedbacks: a review and guide for empiricists.

PubMed

Sih, Andrew; Mathot, Kimberley J; Moirón, María; Montiglio, Pierre-Olivier; Wolf, Max; Dingemanse, Niels J

2015-01-01

An exciting area in behavioural ecology focuses on understanding why animals exhibit consistent among-individual differences in behaviour (animal personalities). Animal personality has been proposed to emerge as an adaptation to individual differences in state variables, leading to the question of why individuals differ consistently in state. Recent theory emphasizes the role that positive feedbacks between state and behaviour can play in producing consistent among-individual covariance between state and behaviour, hence state-dependent personality. We review the role of feedbacks in recent models of adaptive personalities, and provide guidelines for empirical testing of model assumptions and predictions. We discuss the importance of the mediating effects of ecology on these feedbacks, and provide a roadmap for including state-behaviour feedbacks in behavioural ecology research. Copyright © 2014 Elsevier Ltd. All rights reserved.

Stochastic dynamics of adaptive trait and neutral marker driven by eco-evolutionary feedbacks.

PubMed

Billiard, Sylvain; Ferrière, Régis; Méléard, Sylvie; Tran, Viet Chi

2015-11-01

How the neutral diversity is affected by selection and adaptation is investigated in an eco-evolutionary framework. In our model, we study a finite population in continuous time, where each individual is characterized by a trait under selection and a completely linked neutral marker. Population dynamics are driven by births and deaths, mutations at birth, and competition between individuals. Trait values influence ecological processes (demographic events, competition), and competition generates selection on trait variation, thus closing the eco-evolutionary feedback loop. The demographic effects of the trait are also expected to influence the generation and maintenance of neutral variation. We consider a large population limit with rare mutation, under the assumption that the neutral marker mutates faster than the trait under selection. We prove the convergence of the stochastic individual-based process to a new measure-valued diffusive process with jumps that we call Substitution Fleming-Viot Process (SFVP). When restricted to the trait space this process is the Trait Substitution Sequence first introduced by Metz et al. (1996). During the invasion of a favorable mutation, a genetical bottleneck occurs and the marker associated with this favorable mutant is hitchhiked. By rigorously analysing the hitchhiking effect and how the neutral diversity is restored afterwards, we obtain the condition for a time-scale separation; under this condition, we show that the marker distribution is approximated by a Fleming-Viot distribution between two trait substitutions. We discuss the implications of the SFVP for our understanding of the dynamics of neutral variation under eco-evolutionary feedbacks and illustrate the main phenomena with simulations. Our results highlight the joint importance of mutations, ecological parameters, and trait values in the restoration of neutral diversity after a selective sweep.

Secondary adaptation of memory-guided saccades

PubMed Central

Srimal, Riju; Curtis, Clayton E.

2011-01-01

Adaptation of saccade gains in response to errors keeps vision and action co-registered in the absence of awareness or effort. Timing is key, as the visual error must be available shortly after the saccade is generated or adaptation does not occur. Here, we tested the hypothesis that when feedback is delayed, learning still occurs, but does so through small secondary corrective saccades. Using a memory-guided saccade task, we gave feedback about the accuracy of saccades that was falsely displaced by a consistent amount, but only after long delays. Despite the delayed feedback, over time subjects improved in accuracy toward the false feedback. They did so not by adjusting their primary saccades, but via directed corrective saccades made before feedback was given. We propose that saccade learning may be driven by different types of feedback teaching signals. One teaching signal relies upon a tight temporal relation with the saccade and contributes to obligatory learning independent of awareness. When this signal is ineffective due to delayed error feedback, a second compensatory teaching signal enables flexible adjustments to the spatial goal of saccades and helps maintain sensorimotor accuracy. PMID:20803135

Neural-network-based state feedback control of a nonlinear discrete-time system in nonstrict feedback form.

PubMed

Jagannathan, Sarangapani; He, Pingan

2008-12-01

In this paper, a suite of adaptive neural network (NN) controllers is designed to deliver a desired tracking performance for the control of an unknown, second-order, nonlinear discrete-time system expressed in nonstrict feedback form. In the first approach, two feedforward NNs are employed in the controller with tracking error as the feedback variable whereas in the adaptive critic NN architecture, three feedforward NNs are used. In the adaptive critic architecture, two action NNs produce virtual and actual control inputs, respectively, whereas the third critic NN approximates certain strategic utility function and its output is employed for tuning action NN weights in order to attain the near-optimal control action. Both the NN control methods present a well-defined controller design and the noncausal problem in discrete-time backstepping design is avoided via NN approximation. A comparison between the controller methodologies is highlighted. The stability analysis of the closed-loop control schemes is demonstrated. The NN controller schemes do not require an offline learning phase and the NN weights can be initialized at zero or random. Results show that the performance of the proposed controller schemes is highly satisfactory while meeting the closed-loop stability.

Interactive Genetic Algorithm - An Adaptive and Interactive Decision Support Framework for Design of Optimal Groundwater Monitoring Plans

NASA Astrophysics Data System (ADS)

Babbar-Sebens, M.; Minsker, B. S.

2006-12-01

In the water resources management field, decision making encompasses many kinds of engineering, social, and economic constraints and objectives. Representing all of these problem dependant criteria through models (analytical or numerical) and various formulations (e.g., objectives, constraints, etc.) within an optimization- simulation system can be a very non-trivial issue. Most models and formulations utilized for discerning desirable traits in a solution can only approximate the decision maker's (DM) true preference criteria, and they often fail to consider important qualitative and incomputable phenomena related to the management problem. In our research, we have proposed novel decision support frameworks that allow DMs to actively participate in the optimization process. The DMs explicitly indicate their true preferences based on their subjective criteria and the results of various simulation models and formulations. The feedback from the DMs is then used to guide the search process towards solutions that are "all-rounders" from the perspective of the DM. The two main research questions explored in this work are: a) Does interaction between the optimization algorithm and a DM assist the system in searching for groundwater monitoring designs that are robust from the DM's perspective?, and b) How can an interactive search process be made more effective when human factors, such as human fatigue and cognitive learning processes, affect the performance of the algorithm? The application of these frameworks on a real-world groundwater long-term monitoring (LTM) case study in Michigan highlighted the following salient advantages: a) in contrast to the non-interactive optimization methodology, the proposed interactive frameworks were able to identify low cost monitoring designs whose interpolation maps respected the expected spatial distribution of the contaminants, b) for many same-cost designs, the interactive methodologies were able to propose multiple alternatives

Linking biogeomorphic feedbacks from ecosystem engineer to landscape scale: a panarchy approach

NASA Astrophysics Data System (ADS)

Eichel, Jana

2017-04-01

Scale is a fundamental concept in both ecology and geomorphology. Therefore, scale-based approaches are a valuable tool to bridge the disciplines and improve the understanding of feedbacks between geomorphic processes, landforms, material and organisms and ecological processes in biogeomorphology. Yet, linkages between biogeomorphic feedbacks on different scales, e.g. between ecosystem engineering and landscape scale patterns and dynamics, are not well understood. A panarchy approach sensu Holling et al. (2002) can help to close this research gap and explain how structure and function are created in biogeomorphic ecosystems. Based on results from previous biogeomorphic research in Turtmann glacier foreland (Switzerland; Eichel, 2017; Eichel et al. 2013, 2016), a panarchy concept is presented for lateral moraine slope biogeomorphic ecosystems. It depicts biogeomorphic feedbacks on different spatiotemporal scales as a set of nested adaptive cycles and links them by 'remember' and 'revolt' connections. On a small scale (cm2 - m2; seconds to years), the life cycle of the ecosystem engineer Dryas octopetala L. is considered as an adaptive cycle. Biogeomorphic succession within patches created by geomorphic processes represents an intermediate scale adaptive cycle (m2 - ha, years to decades), while geomorphic and ecologic pattern development at a landscape scale (ha - km2, decades to centuries) can be illustrated by an adaptive cycle of ‚biogeomorphic patch dynamics' (Eichel, 2017). In the panarchy, revolt connections link the smaller scale adaptive cycles to larger scale cycles: on lateral moraine slopes, the development of ecosystem engineer biomass and cover controls the engineering threshold of the biogeomorphic feedback window (Eichel et al., 2016) and therefore the onset of the biogeomorphic phase during biogeomorphic succession. In this phase, engineer patches and biogeomorphic structures can be created in the patch mosaic of the landscape. Remember connections

Automated Intelligent Training with a Tactical Decision Making Serious Game

DTIC Science & Technology

2014-01-01

tactical skills, but only if experiential events are accompanied with guided feedback. Practice alone is not sufficient for learning; it must be...micro-adaptation occurs within events (Shute, 1993). Micro-adaptation is a major component of InGEAR’s pedagogical strategy, with feedback tailored

Cardiac Concomitants of Feedback and Prediction Error Processing in Reinforcement Learning

PubMed Central

Kastner, Lucas; Kube, Jana; Villringer, Arno; Neumann, Jane

2017-01-01

Successful learning hinges on the evaluation of positive and negative feedback. We assessed differential learning from reward and punishment in a monetary reinforcement learning paradigm, together with cardiac concomitants of positive and negative feedback processing. On the behavioral level, learning from reward resulted in more advantageous behavior than learning from punishment, suggesting a differential impact of reward and punishment on successful feedback-based learning. On the autonomic level, learning and feedback processing were closely mirrored by phasic cardiac responses on a trial-by-trial basis: (1) Negative feedback was accompanied by faster and prolonged heart rate deceleration compared to positive feedback. (2) Cardiac responses shifted from feedback presentation at the beginning of learning to stimulus presentation later on. (3) Most importantly, the strength of phasic cardiac responses to the presentation of feedback correlated with the strength of prediction error signals that alert the learner to the necessity for behavioral adaptation. Considering participants' weight status and gender revealed obesity-related deficits in learning to avoid negative consequences and less consistent behavioral adaptation in women compared to men. In sum, our results provide strong new evidence for the notion that during learning phasic cardiac responses reflect an internal value and feedback monitoring system that is sensitive to the violation of performance-based expectations. Moreover, inter-individual differences in weight status and gender may affect both behavioral and autonomic responses in reinforcement-based learning. PMID:29163004

A Decentralized Compositional Framework for Dependable Decision Process in Self-Managed Cyber Physical Systems

PubMed Central

Hou, Kun-Mean; Zhang, Zhan

2017-01-01

Cyber Physical Systems (CPSs) need to interact with the changeable environment under various interferences. To provide continuous and high quality services, a self-managed CPS should automatically reconstruct itself to adapt to these changes and recover from failures. Such dynamic adaptation behavior introduces systemic challenges for CPS design, advice evaluation and decision process arrangement. In this paper, a formal compositional framework is proposed to systematically improve the dependability of the decision process. To guarantee the consistent observation of event orders for causal reasoning, this work first proposes a relative time-based method to improve the composability and compositionality of the timing property of events. Based on the relative time solution, a formal reference framework is introduced for self-managed CPSs, which includes a compositional FSM-based actor model (subsystems of CPS), actor-based advice and runtime decomposable decisions. To simplify self-management, a self-similar recursive actor interface is proposed for decision (actor) composition. We provide constraints and seven patterns for the composition of reliability and process time requirements. Further, two decentralized decision process strategies are proposed based on our framework, and we compare the reliability with the static strategy and the centralized processing strategy. The simulation results show that the one-order feedback strategy has high reliability, scalability and stability against the complexity of decision and random failure. This paper also shows a way to simplify the evaluation for dynamic system by improving the composability and compositionality of the subsystem. PMID:29120357

A Decentralized Compositional Framework for Dependable Decision Process in Self-Managed Cyber Physical Systems.

PubMed

Zhou, Peng; Zuo, Decheng; Hou, Kun-Mean; Zhang, Zhan

2017-11-09

Cyber Physical Systems (CPSs) need to interact with the changeable environment under various interferences. To provide continuous and high quality services, a self-managed CPS should automatically reconstruct itself to adapt to these changes and recover from failures. Such dynamic adaptation behavior introduces systemic challenges for CPS design, advice evaluation and decision process arrangement. In this paper, a formal compositional framework is proposed to systematically improve the dependability of the decision process. To guarantee the consistent observation of event orders for causal reasoning, this work first proposes a relative time-based method to improve the composability and compositionality of the timing property of events. Based on the relative time solution, a formal reference framework is introduced for self-managed CPSs, which includes a compositional FSM-based actor model (subsystems of CPS), actor-based advice and runtime decomposable decisions. To simplify self-management, a self-similar recursive actor interface is proposed for decision (actor) composition. We provide constraints and seven patterns for the composition of reliability and process time requirements. Further, two decentralized decision process strategies are proposed based on our framework, and we compare the reliability with the static strategy and the centralized processing strategy. The simulation results show that the one-order feedback strategy has high reliability, scalability and stability against the complexity of decision and random failure. This paper also shows a way to simplify the evaluation for dynamic system by improving the composability and compositionality of the subsystem.

Scattering Control Using Nonlinear Smart Metasurface with Internal Feedback

NASA Astrophysics Data System (ADS)

Semenikhina, D. V.; Semenikhin, A. I.

2017-05-01

The ideology of creation of a nonlinear smart metasurface with internal feedback for the adaptive control by spectral composition of scattered field is offered. The metasurface contains a lattice of strip elements with nonlinear loads-sensors. They are included in a circuit of internal feedback for the adaptive control of scattered field. Numerically it is shown that maximal levels of the second harmonic in the spectrum of scattered far field correspond to maximum of voltage rectified on metasurface. Experimentally the prototype of the plane smart covering on the basis of the metasurface in the form of strip lattice with controlled nonlinear loads-sensors is investigated for an idea confirmation.

Modeling Common-Sense Decisions

NASA Astrophysics Data System (ADS)

Zak, Michail

This paper presents a methodology for efficient synthesis of dynamical model simulating a common-sense decision making process. The approach is based upon the extension of the physics' First Principles that includes behavior of living systems. The new architecture consists of motor dynamics simulating actual behavior of the object, and mental dynamics representing evolution of the corresponding knowledge-base and incorporating it in the form of information flows into the motor dynamics. The autonomy of the decision making process is achieved by a feedback from mental to motor dynamics. This feedback replaces unavailable external information by an internal knowledgebase stored in the mental model in the form of probability distributions.

Reward and punishment enhance motor adaptation in stroke.

PubMed

Quattrocchi, Graziella; Greenwood, Richard; Rothwell, John C; Galea, Joseph M; Bestmann, Sven

2017-09-01

The effects of motor learning, such as motor adaptation, in stroke rehabilitation are often transient, thus mandating approaches that enhance the amount of learning and retention. Previously, we showed in young individuals that reward and punishment feedback have dissociable effects on motor adaptation, with punishment improving adaptation and reward enhancing retention. If these findings were able to generalise to patients with stroke, they would provide a way to optimise motor learning in these patients. Therefore, we tested this in 45 patients with chronic stroke allocated in three groups. Patients performed reaching movements with their paretic arm with a robotic manipulandum. After training (day 1), day 2 involved adaptation to a novel force field. During the adaptation phase, patients received performance-based feedback according to the group they were allocated: reward, punishment or no feedback (neutral). On day 3, patients readapted to the force field but all groups now received neutral feedback. All patients adapted, with reward and punishment groups displaying greater adaptation and readaptation than the neutral group, irrespective of demographic, cognitive or functional differences. Remarkably, the reward and punishment groups adapted to similar degree as healthy controls. Finally, the reward group showed greater retention. This study provides, for the first time, evidence that reward and punishment can enhance motor adaptation in patients with stroke. Further research on reinforcement-based motor learning regimes is warranted to translate these promising results into clinical practice and improve motor rehabilitation outcomes in patients with stroke. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

INITIATE: An Intelligent Adaptive Alert Environment.

PubMed

Jafarpour, Borna; Abidi, Samina Raza; Ahmad, Ahmad Marwan; Abidi, Syed Sibte Raza

2015-01-01

Exposure to a large volume of alerts generated by medical Alert Generating Systems (AGS) such as drug-drug interaction softwares or clinical decision support systems over-whelms users and causes alert fatigue in them. Some of alert fatigue effects are ignoring crucial alerts and longer response times. A common approach to avoid alert fatigue is to devise mechanisms in AGS to stop them from generating alerts that are deemed irrelevant. In this paper, we present a novel framework called INITIATE: an INtellIgent adapTIve AlerT Environment to avoid alert fatigue by managing alerts generated by one or more AGS. We have identified and categories the lifecycle of different alerts and have developed alert management logic as per the alerts' lifecycle. Our framework incorporates an ontology that represents the alert management strategy and an alert management engine that executes this strategy. Our alert management framework offers the following features: (1) Adaptability based on users' feedback; (2) Personalization and aggregation of messages; and (3) Connection to Electronic Medical Records by implementing a HL7 Clinical Document Architecture parser.

Adaptive hybrid control of manipulators

NASA Technical Reports Server (NTRS)

Seraji, H.

1987-01-01

Simple methods for the design of adaptive force and position controllers for robot manipulators within the hybrid control architecuture is presented. The force controller is composed of an adaptive PID feedback controller, an auxiliary signal and a force feedforward term, and it achieves tracking of desired force setpoints in the constraint directions. The position controller consists of adaptive feedback and feedforward controllers and an auxiliary signal, and it accomplishes tracking of desired position trajectories in the free directions. The controllers are capable of compensating for dynamic cross-couplings that exist between the position and force control loops in the hybrid control architecture. The adaptive controllers do not require knowledge of the complex dynamic model or parameter values of the manipulator or the environment. The proposed control schemes are computationally fast and suitable for implementation in on-line control with high sampling rates.

A rapidly changing global medicines environment: How adaptable are funding decision-making systems?

PubMed

Leopold, Christine; Morgan, Steven G; Wagner, Anita K

2017-06-01

With the launch of very highly priced therapies and sudden price increases of generics, pressures on health systems have drastically increased. We aimed to elicit opinions of key decision makers responsible for national assessment and funding decisions on their experiences to adapt to these new realities. Through interviews with decision makers of pharmaceutical assessment and/or funding agencies, we describe the challenges systems are currently facing, systems' responses and systems' characteristics facilitating or hindering responses to changes and overarching topics for the future. Among the most common challenges are increased funding pressures, increased uncertainty and lack of transparency in decision-making. Systems' responses include utilization management, changing of assessment processes, stakeholder engagement and a focus on outcomes and on coordinated negotiations. Integrated delivery systems, fixed health care budgets and geographic and historical characteristics facilitate or sometimes hinder responses to change. Future policy emphasis lays on expanding data structures, managing the exit of drugs funded early, and implementing processes for communications with patients and the public. Going forward emphasis has to be given to structured communications with all stakeholders with a specific emphasis on the broader public and patients about financial limits and priority setting in health care. Copyright © 2017 Elsevier B.V. All rights reserved.

The Role of Decision Support in Adapting to Climate Change: Findings from Three Place-based Regional Assessments

EPA Science Inventory

This report summarizes the methodologies and findings of three regional assessments and considers the role of decision support in assisting adaptation to climate change. Background. In conjunction with the US Global Change Research Program’s (USGCRP’s) National Assessment of ...

Adaptive decision making in a dynamic environment: a test of a sequential sampling model of relative judgment.

PubMed

Vuckovic, Anita; Kwantes, Peter J; Neal, Andrew

2013-09-01

Research has identified a wide range of factors that influence performance in relative judgment tasks. However, the findings from this research have been inconsistent. Studies have varied with respect to the identification of causal variables and the perceptual and decision-making mechanisms underlying performance. Drawing on the ecological rationality approach, we present a theory of the judgment and decision-making processes involved in a relative judgment task that explains how people judge a stimulus and adapt their decision process to accommodate their own uncertainty associated with those judgments. Undergraduate participants performed a simulated air traffic control conflict detection task. Across two experiments, we systematically manipulated variables known to affect performance. In the first experiment, we manipulated the relative distances of aircraft to a common destination while holding aircraft speeds constant. In a follow-up experiment, we introduced a direct manipulation of relative speed. We then fit a sequential sampling model to the data, and used the best fitting parameters to infer the decision-making processes responsible for performance. Findings were consistent with the theory that people adapt to their own uncertainty by adjusting their criterion and the amount of time they take to collect evidence in order to make a more accurate decision. From a practical perspective, the paper demonstrates that one can use a sequential sampling model to understand performance in a dynamic environment, allowing one to make sense of and interpret complex patterns of empirical findings that would otherwise be difficult to interpret using standard statistical analyses. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Learning Rate Updating Methods Applied to Adaptive Fuzzy Equalizers for Broadband Power Line Communications

NASA Astrophysics Data System (ADS)

Ribeiro, Moisés V.

2004-12-01

This paper introduces adaptive fuzzy equalizers with variable step size for broadband power line (PL) communications. Based on delta-bar-delta and local Lipschitz estimation updating rules, feedforward, and decision feedback approaches, we propose singleton and nonsingleton fuzzy equalizers with variable step size to cope with the intersymbol interference (ISI) effects of PL channels and the hardness of the impulse noises generated by appliances and nonlinear loads connected to low-voltage power grids. The computed results show that the convergence rates of the proposed equalizers are higher than the ones attained by the traditional adaptive fuzzy equalizers introduced by J. M. Mendel and his students. Additionally, some interesting BER curves reveal that the proposed techniques are efficient for mitigating the above-mentioned impairments.

How to Cope with Bias While Adapting for Inclusion in Physical Education and Sports: A Judgment and Decision-Making Perspective

ERIC Educational Resources Information Center

Hutzler, Yeshayahu; Bar-Eli, Michael

2013-01-01

The purpose of this article is to describe a theoretical model and practice examples of judgment and decision making bias within the context of inclusion in physical education and sports. After presenting the context of adapting for inclusion, the theoretical roots of judgment and decision are described, and are linked to the practice of physical…

Auditory-Perceptual Learning Improves Speech Motor Adaptation in Children

PubMed Central

Shiller, Douglas M.; Rochon, Marie-Lyne

2015-01-01

Auditory feedback plays an important role in children’s speech development by providing the child with information about speech outcomes that is used to learn and fine-tune speech motor plans. The use of auditory feedback in speech motor learning has been extensively studied in adults by examining oral motor responses to manipulations of auditory feedback during speech production. Children are also capable of adapting speech motor patterns to perceived changes in auditory feedback, however it is not known whether their capacity for motor learning is limited by immature auditory-perceptual abilities. Here, the link between speech perceptual ability and the capacity for motor learning was explored in two groups of 5–7-year-old children who underwent a period of auditory perceptual training followed by tests of speech motor adaptation to altered auditory feedback. One group received perceptual training on a speech acoustic property relevant to the motor task while a control group received perceptual training on an irrelevant speech contrast. Learned perceptual improvements led to an enhancement in speech motor adaptation (proportional to the perceptual change) only for the experimental group. The results indicate that children’s ability to perceive relevant speech acoustic properties has a direct influence on their capacity for sensory-based speech motor adaptation. PMID:24842067

Modifying Evaluations and Decisions in Risky Situations.

PubMed

Maldonado, Antonio; Serra, Sara; Catena, Andrés; Cándido, Antonio; Megías, Alberto

2016-09-20

The main aim of this research was to investigate the decision making process in risky situations. We studied how different types of feedback on risky driving behaviors modulate risk evaluation and risk-taking. For a set of risky traffic situations, participants had to make evaluative judgments (judge the situation as risky or not) and urgent decisions (brake or not). In Experiment 1, participants received feedback with and without negative emotional content when they made risky behaviors. In Experiment 2 we investigated the independent effects of feedback and negative emotional stimuli. The results showed three important findings: First, urgent decisions were faster [F(1, 92) = 6.76, p = .01] and more cautious [F(1, 92) = 17.16, p < .001] than evaluative judgments. These results suggest that evaluative judgments of risk and actual risk-taking may not always coincide, and that they seem to be controlled by two different processing systems as proposed by dual process theories. Second, feedback made participants' responses even faster [F(1, 111) = 71.53, p < .001], allowing greater risk sensitivity [F(1, 111) = 22.12, p < .001] and skewing towards more cautious responses [F(1, 111) = 14.09, p < .001]. Finally, emotional stimuli had an effect only when they were presented as feedback. The results of this research increase our understanding of the processes involved in risky driving behavior and suggest efficient ways to control risk taking through the use of feedback.

Risk assessment and adaptive runoff utilization in water resource system considering the complex relationship among water supply, electricity generation and environment

NASA Astrophysics Data System (ADS)

Zhou, J.; Zeng, X.; Mo, L.; Chen, L.; Jiang, Z.; Feng, Z.; Yuan, L.; He, Z.

2017-12-01

Generally, the adaptive utilization and regulation of runoff in the source region of China's southwest rivers is classified as a typical multi-objective collaborative optimization problem. There are grim competitions and incidence relation in the subsystems of water supply, electricity generation and environment, which leads to a series of complex problems represented by hydrological process variation, blocked electricity output and water environment risk. Mathematically, the difficulties of multi-objective collaborative optimization focus on the description of reciprocal relationships and the establishment of evolving model of adaptive systems. Thus, based on the theory of complex systems science, this project tries to carry out the research from the following aspects: the changing trend of coupled water resource, the covariant factor and driving mechanism, the dynamic evolution law of mutual feedback dynamic process in the supply-generation-environment coupled system, the environmental response and influence mechanism of coupled mutual feedback water resource system, the relationship between leading risk factor and multiple risk based on evolutionary stability and dynamic balance, the transfer mechanism of multiple risk response with the variation of the leading risk factor, the multidimensional coupled feedback system of multiple risk assessment index system and optimized decision theory. Based on the above-mentioned research results, the dynamic method balancing the efficiency of multiple objectives in the coupled feedback system and optimized regulation model of water resources is proposed, and the adaptive scheduling mode considering the internal characteristics and external response of coupled mutual feedback system of water resource is established. In this way, the project can make a contribution to the optimal scheduling theory and methodology of water resource management under uncertainty in the source region of Southwest River.

Affect-Aware Adaptive Tutoring Based on Human-Automation Etiquette Strategies.

PubMed

Yang, Euijung; Dorneich, Michael C

2018-06-01

We investigated adapting the interaction style of intelligent tutoring system (ITS) feedback based on human-automation etiquette strategies. Most ITSs adapt the content difficulty level, adapt the feedback timing, or provide extra content when they detect cognitive or affective decrements. Our previous work demonstrated that changing the interaction style via different feedback etiquette strategies has differential effects on students' motivation, confidence, satisfaction, and performance. The best etiquette strategy was also determined by user frustration. Based on these findings, a rule set was developed that systemically selected the proper etiquette strategy to address one of four learning factors (motivation, confidence, satisfaction, and performance) under two different levels of user frustration. We explored whether etiquette strategy selection based on this rule set (systematic) or random changes in etiquette strategy for a given level of frustration affected the four learning factors. Participants solved mathematics problems under different frustration conditions with feedback that adapted dynamic changes in etiquette strategies either systematically or randomly. The results demonstrated that feedback with etiquette strategies chosen systematically via the rule set could selectively target and improve motivation, confidence, satisfaction, and performance more than changing etiquette strategies randomly. The systematic adaptation was effective no matter the level of frustration for the participant. If computer tutors can vary the interaction style to effectively mitigate negative emotions, then ITS designers would have one more mechanism in which to design affect-aware adaptations that provide the proper responses in situations where human emotions affect the ability to learn.

Feedback-related potentials in a gambling task with randomised reward.

PubMed

Mushtaq, Faisal; Guillen, Pablo Puente; Wilkie, Richard M; Mon-Williams, Mark A; Schaefer, Alexandre

2016-03-01

Event-related potentials (ERPs) time-locked to decision outcomes are reported. Participants engaged in a gambling task (see [1] for details) in which they decided between a risky and a safe option (presented as different coloured shapes) on each trial (416 in total). Each decision was associated with (fully randomised) feedback about the reward outcome (Win/Loss) and its magnitude (varying as a function of decision response; 5-9 points for Risky decisions and 1-4 points for Safe decisions). Here, we show data demonstrating: (a) the influence of Win feedback in the preceding outcome (Outcome t-1) on activity related to the current outcome (Outcome t ); (b) difference wave analysis for outcome expectancy- separating Expected Outcomes (consecutive Loss trials subtracted from consecutive reward) from Unexpected Outcomes (subtracting Loss t-1Win t trials from Win t-1Loss t trials); (c) difference waves separating Switch and Stay responses for Outcome Expectancy; (d) the effect of magnitude induced by decisions (Risk t vs. Safe t ) on Outcome Expectancy; and finally, (e) expectations reflected by response switch direction (Risk to Safe responses vs. Safe to Risk t ) on the FRN at Outcome t .

Adaptive learning in a compartmental model of visual cortex—how feedback enables stable category learning and refinement

PubMed Central

Layher, Georg; Schrodt, Fabian; Butz, Martin V.; Neumann, Heiko

2014-01-01

The categorization of real world objects is often reflected in the similarity of their visual appearances. Such categories of objects do not necessarily form disjunct sets of objects, neither semantically nor visually. The relationship between categories can often be described in terms of a hierarchical structure. For instance, tigers and leopards build two separate mammalian categories, both of which are subcategories of the category Felidae. In the last decades, the unsupervised learning of categories of visual input stimuli has been addressed by numerous approaches in machine learning as well as in computational neuroscience. However, the question of what kind of mechanisms might be involved in the process of subcategory learning, or category refinement, remains a topic of active investigation. We propose a recurrent computational network architecture for the unsupervised learning of categorial and subcategorial visual input representations. During learning, the connection strengths of bottom-up weights from input to higher-level category representations are adapted according to the input activity distribution. In a similar manner, top-down weights learn to encode the characteristics of a specific stimulus category. Feedforward and feedback learning in combination realize an associative memory mechanism, enabling the selective top-down propagation of a category's feedback weight distribution. We suggest that the difference between the expected input encoded in the projective field of a category node and the current input pattern controls the amplification of feedforward-driven representations. Large enough differences trigger the recruitment of new representational resources and the establishment of additional (sub-) category representations. We demonstrate the temporal evolution of such learning and show how the proposed combination of an associative memory with a modulatory feedback integration successfully establishes category and subcategory representations

Neural signatures of experience-based improvements in deterministic decision-making

PubMed Central

Tremel, Joshua J.; Laurent, Patryk A.; Wolk, David A.; Wheeler, Mark E.; Fiez, Julie A.

2016-01-01

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. PMID:27523644

Decoupling feedforward and feedback structures in hybrid active noise control systems for uncorrelated narrowband disturbances

NASA Astrophysics Data System (ADS)

Wu, Lifu; Qiu, Xiaojun; Burnett, Ian S.; Guo, Yecai

2015-08-01

Hybrid feedforward and feedback structures are useful for active noise control (ANC) applications where the noise can only be partially obtained with reference sensors. The traditional method uses the secondary signals of both the feedforward and feedback structures to synthesize a reference signal for the feedback structure in the hybrid structure. However, this approach introduces coupling between the feedforward and feedback structures and parameter changes in one structure affect the other during adaptation such that the feedforward and feedback structures must be optimized simultaneously in practical ANC system design. Two methods are investigated in this paper to remove such coupling effects. One is a simplified method, which uses the error signal directly as the reference signal in the feedback structure, and the second method generates the reference signal for the feedback structure by using only the secondary signal from the feedback structure and utilizes the generated reference signal as the error signal of the feedforward structure. Because the two decoupling methods can optimize the feedforward and feedback structures separately, they provide more flexibility in the design and optimization of the adaptive filters in practical ANC applications.

Diversity in School Performance Feedback Systems

ERIC Educational Resources Information Center

Verhaeghe, Goedele; Schildkamp, Kim; Luyten, Hans; Valcke, Martin

2015-01-01

As data-based decision making is receiving increased attention in education, more and more school performance feedback systems (SPFSs) are being developed and used worldwide. These systems provide schools with data on their functioning. However, little research is available on the characteristics of the different SPFSs. Therefore, this study…

Water security and adaptive capacity for climate: Learning lessons from drought decision making in U.S. urban contexts

NASA Astrophysics Data System (ADS)

Dilling, L.

2017-12-01

Cities in the U.S. have been adapting to drought for many years, implementing a combination of mechanisms to cope with climate and water variability and increasing population. Cities are also at the frontline for making decisions about adaptation to climate change. Are decisions made to cope with drought helping cities to build the adaptive capacity necessary for adapting to climate change? We examined this question by conducting interviews with practitioners involved in drought management at urban water utilities across the U.S. to understand responses to drought and perceptions of their effectiveness. We then drew on established criteria for evaluating successful adaptation (effectiveness, efficiency, equity and legitimacy) to analyze whether these drought policies would build adaptive capacity for climate change. We find that drought responses overall are seen as successful in helping cities balance the demand and supply of water, and maintain system reliability as well as improve water awareness, but can have unintended consequences and shift vulnerability in unexpected ways. For example, even though cities are successful at reducing water use when needed, some are concerned with the increasing difficulty of finding new water savings during a future drought. Secondly, water conservation can affect revenue, impacting the ability of cities to plan for maintenance and capital costs. Third, the social acceptability of policy options is critical and depends on perceived fairness and other factors. Water managers are also challenged by "no fail" expectations that make it difficult to experiment. Moreover some measures can shift vulnerability from one risk, such as running out of water, to another risk, such as water becoming too expensive, lowering quality, or not meeting other key infrastructure design requirements. These findings demonstrate that adaptation measures that seek to reduce exposure to water scarcity can impact aspects of adaptive capacity, and shift

Forgone but not forgotten: the effects of partial and full feedback in "harsh" and "kind" environments.

PubMed

Rakow, Tim; Newell, Ben R; Wright, Louise

2015-12-01

In a perfect world, the choice of any course of action would lead to a satisfactory outcome, and we would obtain feedback about both our chosen course and those we have chosen to forgo. In reality, however, we often face harsh environments in which we can only minimize losses, and we receive impoverished feedback. In these studies, we examined how decision makers dealt with these challenges in a simple task in which we manipulated three features of the decision: The outcomes from the available options were either mostly positive or mostly negative (kind or harsh environment); feedback was either full or partial (outcomes revealed for all options or only for the chosen option); and for the final 20 trials in a sequence, participants either chose on each trial or set an "advance-directive" policy. The propensity to choose the better option was explained by several factors: Full feedback was more beneficial in harsh than in kind environments; policy decisions encouraged better decisions and ameliorated the adverse impact of a harsh environment; and beliefs about the value of strategy diversification predicted switch rates and choice quality. The results suggest a subtle interplay between bottom-up and top-down processes: Although harsh environments encourage poor choices, and some decision makers choose less well than others, this need not imply that the decision maker has failed to identify the better option.

Gray matter volume and rapid decision-making in major depressive disorder.

PubMed

Nakano, Masayuki; Matsuo, Koji; Nakashima, Mami; Matsubara, Toshio; Harada, Kenichiro; Egashira, Kazuteru; Masaki, Hiroaki; Takahashi, Kanji; Watanabe, Yoshifumi

2014-01-03

Reduced motivation and blunted decision-making are key features of major depressive disorder (MDD). Patients with MDD show abnormal decision-making when given negative feedback regarding a reward. The brain mechanisms underpinning this behavior remain unclear. In the present study, we examined the association between rapid decision-making with negative feedback and brain volume in MDD. Thirty-six patients with MDD and 54 age-, sex- and IQ-matched healthy subjects were studied. Subjects performed a rapid decision-making monetary task in which participants could make high- or low-risk choices. We compared between the 2 groups the probability that a high-risk choice followed negative feedback. In addition, we used voxel-based morphometry (VBM) to compare between group differences in gray matter volume, and the correlation between the probability for high-risk choices and brain volume. Compared to the healthy group, the MDD group showed significantly lower probabilities for high-risk choices following negative feedback. VBM analysis revealed that the MDD group had less gray matter volume in the right medial prefrontal cortex and orbitofrontal cortex (OFC) compared to the healthy group. The right OFC volume was negatively correlated with the probability that a high-risk choice followed negative feedback in patients with MDD. We did not observe these trends in healthy subjects. Patients with MDD show reduced motivation for monetary incentives when they were required to make rapid decisions following negative feedback. We observed a correlation between this reduced motivation and gray matter volume in the medial and ventral prefrontal cortex, which suggests that these brain regions are likely involved in the pathophysiology of aberrant decision-making in MDD. © 2013.

Effectiveness of Instruction and Video Feedback on Staff's Use of Prompts and Children's Adaptive Responses during One-to-One Training in Children with Severe to Profound Intellectual Disability

ERIC Educational Resources Information Center

van Vonderen, Annemarie; de Swart, Charlotte; Didden, Robert

2010-01-01

Although relatively many studies have addressed staff training and its effect on trainer behavior, the effects of staff training on trainee's adaptive behaviors have seldomly been examined. We therefore assessed effectiveness of staff training, consisting of instruction and video feedback, on (a) staff's response prompting, and (b) staff's trainer…

Lost in search: (Mal-)adaptation to probabilistic decision environments in children and adults.

PubMed

Betsch, Tilmann; Lehmann, Anne; Lindow, Stefanie; Lang, Anna; Schoemann, Martin

2016-02-01

Adaptive decision making in probabilistic environments requires individuals to use probabilities as weights in predecisional information searches and/or when making subsequent choices. Within a child-friendly computerized environment (Mousekids), we tracked 205 children's (105 children 5-6 years of age and 100 children 9-10 years of age) and 103 adults' (age range: 21-22 years) search behaviors and decisions under different probability dispersions (.17; .33, .83 vs. .50, .67, .83) and constraint conditions (instructions to limit search: yes vs. no). All age groups limited their depth of search when instructed to do so and when probability dispersion was high (range: .17-.83). Unlike adults, children failed to use probabilities as weights for their searches, which were largely not systematic. When examining choices, however, elementary school children (unlike preschoolers) systematically used probabilities as weights in their decisions. This suggests that an intuitive understanding of probabilities and the capacity to use them as weights during integration is not a sufficient condition for applying simple selective search strategies that place one's focus on weight distributions. PsycINFO Database Record (c) 2016 APA, all rights reserved.

Modelling Feedback in Virtual Patients: An Iterative Approach.

PubMed

Stathakarou, Natalia; Kononowicz, Andrzej A; Henningsohn, Lars; McGrath, Cormac

2018-01-01

Virtual Patients (VPs) offer learners the opportunity to practice clinical reasoning skills and have recently been integrated in Massive Open Online Courses (MOOCs). Feedback is a central part of a branched VP, allowing the learner to reflect on the consequences of their decisions and actions. However, there is insufficient guidance on how to design feedback models within VPs and especially in the context of their application in MOOCs. In this paper, we share our experiences from building a feedback model for a bladder cancer VP in a Urology MOOC, following an iterative process in three steps. Our results demonstrate how we can systematize the process of improving the quality of VP components by the application of known literature frameworks and extend them with a feedback module. We illustrate the design and re-design process and exemplify with content from our VP. Our results can act as starting point for discussions on modelling feedback in VPs and invite future research on the topic.

Adaptation by Stealth: Understanding climate information use across scales and decision spaces in water management in the United States

NASA Astrophysics Data System (ADS)

Kirchhoff, C.; Vang Rasmussen, L.; Lemos, M. C.

2016-12-01

While there has been considerable focus on understanding how factors related to the creation of climate knowledge affect its uptake and use, less attention has been paid to the actors, decisions, and processes through which climate information supports, or fails to support, action. This is particularly the case concerning how different scales of decision-making influence information uptake. In this study, we seek to understand how water and resource managers' decision space influences climate information use in two Great Lakes watersheds. We find that despite the availability of tailored climate information, actual use of information in decision making remains low. Reasons include: a) lack of willingness to place climate on agendas because local managers perceive climate change as politically risky and a difficult and intangible problem; b) lack of formal mandate or authority at the city and county scale to translate climate information into on-the-ground action, c) problems with the information itself, and d) perceived lack of demand for climate information by those managers who have the mandate and authority (e.g. at the state level) to use (or help others use) climate information. Our findings suggest that 1) climate scientists and information brokers should produce information that meets a range of decision needs and reserve intensive tailoring efforts for decision makers who have authority and willingness to employ climate information, 2) without support from higher levels of decision-making (e.g. state) it is unlikely that climate information use for adaptation decisions will accelerate significantly in the next few years, and 3) the trend towards adopting more sustainability and resilience practices over climate-specific actions should be supported as an important component of the climate adaptation repertoire.

Impaired Feedforward Control and Enhanced Feedback Control of Speech in Patients with Cerebellar Degeneration.

PubMed

Parrell, Benjamin; Agnew, Zarinah; Nagarajan, Srikantan; Houde, John; Ivry, Richard B

2017-09-20

The cerebellum has been hypothesized to form a crucial part of the speech motor control network. Evidence for this comes from patients with cerebellar damage, who exhibit a variety of speech deficits, as well as imaging studies showing cerebellar activation during speech production in healthy individuals. To date, the precise role of the cerebellum in speech motor control remains unclear, as it has been implicated in both anticipatory (feedforward) and reactive (feedback) control. Here, we assess both anticipatory and reactive aspects of speech motor control, comparing the performance of patients with cerebellar degeneration and matched controls. Experiment 1 tested feedforward control by examining speech adaptation across trials in response to a consistent perturbation of auditory feedback. Experiment 2 tested feedback control, examining online corrections in response to inconsistent perturbations of auditory feedback. Both male and female patients and controls were tested. The patients were impaired in adapting their feedforward control system relative to controls, exhibiting an attenuated anticipatory response to the perturbation. In contrast, the patients produced even larger compensatory responses than controls, suggesting an increased reliance on sensory feedback to guide speech articulation in this population. Together, these results suggest that the cerebellum is crucial for maintaining accurate feedforward control of speech, but relatively uninvolved in feedback control. SIGNIFICANCE STATEMENT Speech motor control is a complex activity that is thought to rely on both predictive, feedforward control as well as reactive, feedback control. While the cerebellum has been shown to be part of the speech motor control network, its functional contribution to feedback and feedforward control remains controversial. Here, we use real-time auditory perturbations of speech to show that patients with cerebellar degeneration are impaired in adapting feedforward control of

Impaired Feedforward Control and Enhanced Feedback Control of Speech in Patients with Cerebellar Degeneration

PubMed Central

Agnew, Zarinah; Nagarajan, Srikantan; Houde, John; Ivry, Richard B.

2017-01-01

The cerebellum has been hypothesized to form a crucial part of the speech motor control network. Evidence for this comes from patients with cerebellar damage, who exhibit a variety of speech deficits, as well as imaging studies showing cerebellar activation during speech production in healthy individuals. To date, the precise role of the cerebellum in speech motor control remains unclear, as it has been implicated in both anticipatory (feedforward) and reactive (feedback) control. Here, we assess both anticipatory and reactive aspects of speech motor control, comparing the performance of patients with cerebellar degeneration and matched controls. Experiment 1 tested feedforward control by examining speech adaptation across trials in response to a consistent perturbation of auditory feedback. Experiment 2 tested feedback control, examining online corrections in response to inconsistent perturbations of auditory feedback. Both male and female patients and controls were tested. The patients were impaired in adapting their feedforward control system relative to controls, exhibiting an attenuated anticipatory response to the perturbation. In contrast, the patients produced even larger compensatory responses than controls, suggesting an increased reliance on sensory feedback to guide speech articulation in this population. Together, these results suggest that the cerebellum is crucial for maintaining accurate feedforward control of speech, but relatively uninvolved in feedback control. SIGNIFICANCE STATEMENT Speech motor control is a complex activity that is thought to rely on both predictive, feedforward control as well as reactive, feedback control. While the cerebellum has been shown to be part of the speech motor control network, its functional contribution to feedback and feedforward control remains controversial. Here, we use real-time auditory perturbations of speech to show that patients with cerebellar degeneration are impaired in adapting feedforward control of

Shared internal models for feedforward and feedback control.

PubMed

Wagner, Mark J; Smith, Maurice A

2008-10-15

A child often learns to ride a bicycle in the driveway, free of unforeseen obstacles. Yet when she first rides in the street, we hope that if a car suddenly pulls out in front of her, she will combine her innate goal of avoiding an accident with her learned knowledge of the bicycle, and steer away or brake. In general, when we train to perform a new motor task, our learning is most robust if it updates the rules of online error correction to reflect the rules and goals of the new task. Here we provide direct evidence that, after a new feedforward motor adaptation, motor feedback responses to unanticipated errors become precisely task appropriate, even when such errors were never experienced during training. To study this ability, we asked how, if at all, do online responses to occasional, unanticipated force pulses during reaching arm movements change after adapting to altered arm dynamics? Specifically, do they change in a task-appropriate manner? In our task, subjects learned novel velocity-dependent dynamics. However, occasional force-pulse perturbations produced unanticipated changes in velocity. Therefore, after adaptation, task-appropriate responses to unanticipated pulses should compensate corresponding changes in velocity-dependent dynamics. We found that after adaptation, pulse responses precisely compensated these changes, although they were never trained to do so. These results provide evidence for a smart feedback controller which automatically produces responses specific to the learned dynamics of the current task. To accomplish this, the neural processes underlying feedback control must (1) be capable of accurate real-time state prediction for velocity via a forward model and (2) have access to recently learned changes in internal models of limb dynamics.

Walking Speed Influences the Effects of Implicit Visual Feedback Distortion on Modulation of Gait Symmetry

PubMed Central

Maestas, Gabrielle; Hu, Jiyao; Trevino, Jessica; Chunduru, Pranathi; Kim, Seung-Jae; Lee, Hyunglae

2018-01-01

The use of visual feedback in gait rehabilitation has been suggested to promote recovery of locomotor function by incorporating interactive visual components. Our prior work demonstrated that visual feedback distortion of changes in step length symmetry entails an implicit or unconscious adaptive process in the subjects’ spatial gait patterns. We investigated whether the effect of the implicit visual feedback distortion would persist at three different walking speeds (slow, self-preferred and fast speeds) and how different walking speeds would affect the amount of adaption. In the visual feedback distortion paradigm, visual vertical bars portraying subjects’ step lengths were distorted so that subjects perceived their step lengths to be asymmetric during testing. Measuring the adjustments in step length during the experiment showed that healthy subjects made spontaneous modulations away from actual symmetry in response to the implicit visual distortion, no matter the walking speed. In all walking scenarios, the effects of implicit distortion became more significant at higher distortion levels. In addition, the amount of adaptation induced by the visual distortion was significantly greater during walking at preferred or slow speed than at the fast speed. These findings indicate that although a link exists between supraspinal function through visual system and human locomotion, sensory feedback control for locomotion is speed-dependent. Ultimately, our results support the concept that implicit visual feedback can act as a dominant form of feedback in gait modulation, regardless of speed. PMID:29632481

Feedback and Feedforward Control During Walking in Individuals With Chronic Ankle Instability.

PubMed

Yen, Sheng-Che; Corkery, Marie B; Donohoe, Amy; Grogan, Maddison; Wu, Yi-Ning

2016-09-01

Study Design Controlled laboratory study. Background Recurrent ankle sprains associated with chronic ankle instability (CAI) occur not only in challenging sports but also in daily walking. Understanding whether and how CAI alters feedback and feedforward controls during walking may be important for developing interventions for CAI prevention or treatment. Objective To understand whether CAI is associated with changes in feedback and feedforward control when individuals with CAI are subjected to experimental perturbation during walking. Methods Twelve subjects with CAI and 12 control subjects walked on a treadmill while adapting to external loading that generated inversion perturbation at the ankle joint. Ankle kinematics around heel contact during and after the adaptation were compared between the 2 groups. Results Both healthy and CAI groups showed an increase in eversion around heel contact in early adaptation to the external loading. However, the CAI group adapted back toward the baseline, while the healthy controls showed further increase in eversion in late adaptation. When the external loading was removed in the postadaptation period, healthy controls showed an aftereffect consisting of an increase in eversion around heel contact, but the CAI group showed no aftereffect. Conclusion The results provide preliminary evidence that CAI may alter individuals' feedback and feedforward control during walking. J Orthop Sports Phys Ther 2016;46(9):775-783. Epub 5 Aug 2016. doi:10.2519/jospt.2016.6403.

Neural signatures of experience-based improvements in deterministic decision-making.

PubMed

Tremel, Joshua J; Laurent, Patryk A; Wolk, David A; Wheeler, Mark E; Fiez, Julie A

2016-12-15

Feedback about our choices is a crucial part of how we gather information and learn from our environment. It provides key information about decision experiences that can be used to optimize future choices. However, our understanding of the processes through which feedback translates into improved decision-making is lacking. Using neuroimaging (fMRI) and cognitive models of decision-making and learning, we examined the influence of feedback on multiple aspects of decision processes across learning. Subjects learned correct choices to a set of 50 word pairs across eight repetitions of a concurrent discrimination task. Behavioral measures were then analyzed with both a drift-diffusion model and a reinforcement learning model. Parameter values from each were then used as fMRI regressors to identify regions whose activity fluctuates with specific cognitive processes described by the models. The patterns of intersecting neural effects across models support two main inferences about the influence of feedback on decision-making. First, frontal, anterior insular, fusiform, and caudate nucleus regions behave like performance monitors, reflecting errors in performance predictions that signal the need for changes in control over decision-making. Second, temporoparietal, supplementary motor, and putamen regions behave like mnemonic storage sites, reflecting differences in learned item values that inform optimal decision choices. As information about optimal choices is accrued, these neural systems dynamically adjust, likely shifting the burden of decision processing from controlled performance monitoring to bottom-up, stimulus-driven choice selection. Collectively, the results provide a detailed perspective on the fundamental ability to use past experiences to improve future decisions. Copyright © 2016 Elsevier B.V. All rights reserved.

Study of a co-designed decision feedback equalizer, deinterleaver, and decoder

NASA Technical Reports Server (NTRS)

Peile, Robert E.; Welch, Loyd

1990-01-01

A technique that promises better quality data from band limited channels at lower received power in digital transmission systems is presented. Data transmission, in such systems often suffers from intersymbol interference (ISI) and noise. Two separate techniques, channel coding and equalization, have caused considerable advances in the state of communication systems and both concern themselves with removing the undesired effects of a communication channel. Equalizers mitigate the ISI whereas coding schemes are used to incorporate error-correction. In the past, most of the research in these two areas has been carried out separately. However, the individual techniques have strengths and weaknesses that are complementary in many applications: an integrated approach realizes gains in excess to that of a simple juxtaposition. Coding schemes have been successfully used in cascade with linear equalizers which in the absence of ISI provide excellent performance. However, when both ISI and the noise level are relatively high, nonlinear receivers like the decision feedback equalizer (DFE) perform better. The DFE has its drawbacks: it suffers from error propagation. The technique presented here takes advantage of interleaving to integrate the two approaches so that the error propagation in DFE can be reduced with the help of error correction provided by the decoder. The results of simulations carried out for both, binary, and non-binary, channels confirm that significant gain can be obtained by codesigning equalizer and decoder. Although, systems with time-invariant channels and simple DFE having linear filters were looked into, the technique is fairly general and can easily be modified for more sophisticated equalizers to obtain even larger gains.

Eye movements in interception with delayed visual feedback.

PubMed

Cámara, Clara; de la Malla, Cristina; López-Moliner, Joan; Brenner, Eli

2018-07-01

The increased reliance on electronic devices such as smartphones in our everyday life exposes us to various delays between our actions and their consequences. Whereas it is known that people can adapt to such delays, the mechanisms underlying such adaptation remain unclear. To better understand these mechanisms, the current study explored the role of eye movements in interception with delayed visual feedback. In two experiments, eye movements were recorded as participants tried to intercept a moving target with their unseen finger while receiving delayed visual feedback about their own movement. In Experiment 1, the target randomly moved in one of two different directions at one of two different velocities. The delay between the participant's finger movement and movement of the cursor that provided feedback about the finger movements was gradually increased. Despite the delay, participants followed the target with their gaze. They were quite successful at hitting the target with the cursor. Thus, they moved their finger to a position that was ahead of where they were looking. Removing the feedback showed that participants had adapted to the delay. In Experiment 2, the target always moved in the same direction and at the same velocity, while the cursor's delay varied across trials. Participants still always directed their gaze at the target. They adjusted their movement to the delay on each trial, often succeeding to intercept the target with the cursor. Since their gaze was always directed at the target, and they could not know the delay until the cursor started moving, participants must have been using peripheral vision of the delayed cursor to guide it to the target. Thus, people deal with delays by directing their gaze at the target and using both experience from previous trials (Experiment 1) and peripheral visual information (Experiment 2) to guide their finger in a way that will make the cursor hit the target.

"The Gaze Heuristic:" Biography of an Adaptively Rational Decision Process.

PubMed

Hamlin, Robert P

2017-04-01

This article is a case study that describes the natural and human history of the gaze heuristic. The gaze heuristic is an interception heuristic that utilizes a single input (deviation from a constant angle of approach) repeatedly as a task is performed. Its architecture, advantages, and limitations are described in detail. A history of the gaze heuristic is then presented. In natural history, the gaze heuristic is the only known technique used by predators to intercept prey. In human history the gaze heuristic was discovered accidentally by Royal Air Force (RAF) fighter command just prior to World War II. As it was never discovered by the Luftwaffe, the technique conferred a decisive advantage upon the RAF throughout the war. After the end of the war in America, German technology was combined with the British heuristic to create the Sidewinder AIM9 missile, the most successful autonomous weapon ever built. There are no plans to withdraw it or replace its guiding gaze heuristic. The case study demonstrates that the gaze heuristic is a specific heuristic type that takes a single best input at the best time (take the best 2 ). Its use is an adaptively rational response to specific, rapidly evolving decision environments that has allowed those animals/humans/machines who use it to survive, prosper, and multiply relative to those who do not. Copyright © 2017 Cognitive Science Society, Inc.

Increased anterior cingulate cortex response precedes behavioural adaptation in anorexia nervosa

PubMed Central

Geisler, Daniel; Ritschel, Franziska; King, Joseph A.; Bernardoni, Fabio; Seidel, Maria; Boehm, Ilka; Runge, Franziska; Goschke, Thomas; Roessner, Veit; Smolka, Michael N.; Ehrlich, Stefan

2017-01-01

Patients with anorexia nervosa (AN) are characterised by increased self-control, cognitive rigidity and impairments in set-shifting, but the underlying neural mechanisms are poorly understood. Here we used functional magnetic resonance imaging (fMRI) to elucidate the neural correlates of behavioural adaptation to changes in reward contingencies in young acutely ill AN patients. Thirty-six adolescent/young adult, non-chronic female AN patients and 36 age-matched healthy females completed a well-established probabilistic reversal learning task during fMRI. We analysed hemodynamic responses in empirically-defined regions of interest during positive feedback and negative feedback not followed/followed by behavioural adaptation and conducted functional connectivity analyses. Although overall task performance was comparable between groups, AN showed increased shifting after receiving negative feedback (lose-shift behaviour) and altered dorsal anterior cingulate cortex (dACC) responses as a function of feedback. Specifically, patients had increased dACC responses (which correlated with perfectionism) and task-related coupling with amygdala preceding behavioural adaption. Given the generally preserved task performance in young AN, elevated dACC responses specifically during behavioural adaption is suggestive of increased monitoring for the need to adjust performance strategies. Higher dACC-amygdala coupling and increased adaptation after negative feedback underlines this interpretation and could be related to intolerance of uncertainty which has been suggested for AN. PMID:28198813

Iterative inversion of deformation vector fields with feedback control.

PubMed

Dubey, Abhishek; Iliopoulos, Alexandros-Stavros; Sun, Xiaobai; Yin, Fang-Fang; Ren, Lei

2018-05-14

Often, the inverse deformation vector field (DVF) is needed together with the corresponding forward DVF in four-dimesional (4D) reconstruction and dose calculation, adaptive radiation therapy, and simultaneous deformable registration. This study aims at improving both accuracy and efficiency of iterative algorithms for DVF inversion, and advancing our understanding of divergence and latency conditions. We introduce a framework of fixed-point iteration algorithms with active feedback control for DVF inversion. Based on rigorous convergence analysis, we design control mechanisms for modulating the inverse consistency (IC) residual of the current iterate, to be used as feedback into the next iterate. The control is designed adaptively to the input DVF with the objective to enlarge the convergence area and expedite convergence. Three particular settings of feedback control are introduced: constant value over the domain throughout the iteration; alternating values between iteration steps; and spatially variant values. We also introduce three spectral measures of the displacement Jacobian for characterizing a DVF. These measures reveal the critical role of what we term the nontranslational displacement component (NTDC) of the DVF. We carry out inversion experiments with an analytical DVF pair, and with DVFs associated with thoracic CT images of six patients at end of expiration and end of inspiration. The NTDC-adaptive iterations are shown to attain a larger convergence region at a faster pace compared to previous nonadaptive DVF inversion iteration algorithms. By our numerical experiments, alternating control yields smaller IC residuals and inversion errors than constant control. Spatially variant control renders smaller residuals and errors by at least an order of magnitude, compared to other schemes, in no more than 10 steps. Inversion results also show remarkable quantitative agreement with analysis-based predictions. Our analysis captures properties of DVF data

Sensorimotor adaptation is influenced by background music.

PubMed

Bock, Otmar

2010-06-01

It is well established that listening to music can modify subjects' cognitive performance. The present study evaluates whether this so-called Mozart Effect extends beyond cognitive tasks and includes sensorimotor adaptation. Three subject groups listened to musical pieces that in the author's judgment were serene, neutral, or sad, respectively. This judgment was confirmed by the subjects' introspective reports. While listening to music, subjects engaged in a pointing task that required them to adapt to rotated visual feedback. All three groups adapted successfully, but the speed and magnitude of adaptive improvement was more pronounced with serene music than with the other two music types. In contrast, aftereffects upon restoration of normal feedback were independent of music type. These findings support the existence of a "Mozart effect" for strategic movement control, but not for adaptive recalibration. Possibly, listening to music modifies neural activity in an intertwined cognitive-emotional network.

Adaptive Inner-Loop Rover Control

NASA Technical Reports Server (NTRS)

Kulkarni, Nilesh; Ippolito, Corey; Krishnakumar, Kalmanje; Al-Ali, Khalid M.

2006-01-01

Adaptive control technology is developed for the inner-loop speed and steering control of the MAX Rover. MAX, a CMU developed rover, is a compact low-cost 4-wheel drive, 4-wheel steer (double Ackerman), high-clearance agile durable chassis, outfitted with sensors and electronics that make it ideally suited for supporting research relevant to intelligent teleoperation and as a low-cost autonomous robotic test bed and appliance. The design consists of a feedback linearization based controller with a proportional - integral (PI) feedback that is augmented by an online adaptive neural network. The adaptation law has guaranteed stability properties for safe operation. The control design is retrofit in nature so that it fits inside the outer-loop path planning algorithms. Successful hardware implementation of the controller is illustrated for several scenarios consisting of actuator failures and modeling errors in the nominal design.

Brain Activity Elicited by Positive and Negative Feedback in Preschool-Aged Children

PubMed Central

Mai, Xiaoqin; Tardif, Twila; Doan, Stacey N.; Liu, Chao; Gehring, William J.; Luo, Yue-Jia

2011-01-01

To investigate the processing of positive vs. negative feedback in children aged 4–5 years, we devised a prize-guessing game that is analogous to gambling tasks used to measure feedback-related brain responses in adult studies. Unlike adult studies, the feedback-related negativity (FRN) elicited by positive feedback was as large as that elicited by negative feedback, suggesting that the neural system underlying the FRN may not process feedback valence in early childhood. In addition, positive feedback, compared with negative feedback, evoked a larger P1 over the occipital scalp area and a larger positive slow wave (PSW) over the right central-parietal scalp area. We believe that the PSW is related to emotional arousal and the intensive focus on positive feedback that is present in the preschool and early school years has adaptive significance for both cognitive and emotional development during this period. PMID:21526189

Stochastic Adaptive Particle Beam Tracker Using Meer Filter Feedback.

DTIC Science & Technology

1986-12-01

breakthrough required in controlling the beam location. In 1983, Zicker (27] conducted a feasibility study of a simple proportional gain controller... Zicker synthesized his stochastic controller designs from a deterministic optimal LQ controller assuming full state feedback. An LQ controller is a...34Merge" Method 2.5 Simlifying the eer Filter a Zicker ran a performance analysis on the Meer filter and found the Meer filter virtually insensitive to

Effect of Concurrent Visual Feedback Frequency on Postural Control Learning in Adolescents.

PubMed

Marco-Ahulló, Adrià; Sánchez-Tormo, Alexis; García-Pérez, José A; Villarrasa-Sapiña, Israel; González, Luis M; García-Massó, Xavier

2018-04-13

The purpose was to find better augmented visual feedback frequency (100% or 67%) for learning a balance task in adolescents. Thirty subjects were divided randomly into a control group, and 100% and 67% feedback groups. The three groups performed pretest (3 trials), practice (12 trials), posttest (3 trials) and retention (3 trials, 24 hours later). The reduced feedback group showed lower RMS in the posttest than in the pretest (p = 0.04). The control and reduced feedback groups showed significant lower median frequency in the posttest than in the pretest (p < 0.05). Both feedback groups showed lower values in retention than in the pretest (p < 0.05). Even when the effect of feedback frequency could not be detected in motor learning, 67% of the feedback was recommended for motor adaptation.

Real-time feedback to improve gait in children with cerebral palsy.

PubMed

van Gelder, Linda; Booth, Adam T C; van de Port, Ingrid; Buizer, Annemieke I; Harlaar, Jaap; van der Krogt, Marjolein M

2017-02-01

Real-time feedback may be useful for enhancing information gained from clinical gait analysis of children with cerebral palsy (CP). It may also be effective in functional gait training, however, it is not known if children with CP can adapt gait in response to real-time feedback of kinematic parameters. Sixteen children with cerebral palsy (age 6-16; GMFCS I-III), walking with a flexed-knee gait pattern, walked on an instrumented treadmill with virtual reality in three conditions: regular walking without feedback (NF), feedback on hip angle (FH) and feedback on knee angle (FK). Clinically relevant gait parameters were calculated and the gait profile score (GPS) was used as a measure of overall gait changes between conditions. All children, except one, were able to improve hip and/or knee extension during gait in response to feedback, with nine achieving a clinically relevant improvement. Peak hip extension improved significantly by 5.1±5.9° (NF: 8.9±12.8°, FH: 3.8±10.4°, p=0.01). Peak knee extension improved significantly by 7.7±7.1° (NF: 22.2±12.0°, FK: 14.5±12.7°, p<0.01). GPS did not change between conditions due to increased deviations in other gait parameters. Responders to feedback were shown to have worse initial gait as measured by GPS (p=0.005) and functional selectivity score (p=0.049). In conclusion, ambulatory children with CP show adaptability in gait and are able to respond to real-time feedback, resulting in significant and clinically relevant improvements in peak hip and knee extension. These findings show the potential of real-time feedback as a tool for functional gait training and advanced gait analysis in CP. Copyright © 2016 Elsevier B.V. All rights reserved.