Development of probabilistic thinking-oriented learning tools for probability materials at junior high school students

NASA Astrophysics Data System (ADS)

Sari, Dwi Ivayana; Hermanto, Didik

2017-08-01

This research is a developmental research of probabilistic thinking-oriented learning tools for probability materials at ninth grade students. This study is aimed to produce a good probabilistic thinking-oriented learning tools. The subjects were IX-A students of MTs Model Bangkalan. The stages of this development research used 4-D development model which has been modified into define, design and develop. Teaching learning tools consist of lesson plan, students' worksheet, learning teaching media and students' achievement test. The research instrument used was a sheet of learning tools validation, a sheet of teachers' activities, a sheet of students' activities, students' response questionnaire and students' achievement test. The result of those instruments were analyzed descriptively to answer research objectives. The result was teaching learning tools in which oriented to probabilistic thinking of probability at ninth grade students which has been valid. Since teaching and learning tools have been revised based on validation, and after experiment in class produced that teachers' ability in managing class was effective, students' activities were good, students' responses to the learning tools were positive and the validity, sensitivity and reliability category toward achievement test. In summary, this teaching learning tools can be used by teacher to teach probability for develop students' probabilistic thinking.

Incidental learning of probability information is differentially affected by the type of visual working memory representation.

PubMed

van Lamsweerde, Amanda E; Beck, Melissa R

2015-12-01

In this study, we investigated whether the ability to learn probability information is affected by the type of representation held in visual working memory. Across 4 experiments, participants detected changes to displays of coloured shapes. While participants detected changes in 1 dimension (e.g., colour), a feature from a second, nonchanging dimension (e.g., shape) predicted which object was most likely to change. In Experiments 1 and 3, items could be grouped by similarity in the changing dimension across items (e.g., colours and shapes were repeated in the display), while in Experiments 2 and 4 items could not be grouped by similarity (all features were unique). Probability information from the predictive dimension was learned and used to increase performance, but only when all of the features within a display were unique (Experiments 2 and 4). When it was possible to group by feature similarity in the changing dimension (e.g., 2 blue objects appeared within an array), participants were unable to learn probability information and use it to improve performance (Experiments 1 and 3). The results suggest that probability information can be learned in a dimension that is not explicitly task-relevant, but only when the probability information is represented with the changing dimension in visual working memory. (c) 2015 APA, all rights reserved).

Probability estimation with machine learning methods for dichotomous and multicategory outcome: theory.

PubMed

Kruppa, Jochen; Liu, Yufeng; Biau, Gérard; Kohler, Michael; König, Inke R; Malley, James D; Ziegler, Andreas

2014-07-01

Probability estimation for binary and multicategory outcome using logistic and multinomial logistic regression has a long-standing tradition in biostatistics. However, biases may occur if the model is misspecified. In contrast, outcome probabilities for individuals can be estimated consistently with machine learning approaches, including k-nearest neighbors (k-NN), bagged nearest neighbors (b-NN), random forests (RF), and support vector machines (SVM). Because machine learning methods are rarely used by applied biostatisticians, the primary goal of this paper is to explain the concept of probability estimation with these methods and to summarize recent theoretical findings. Probability estimation in k-NN, b-NN, and RF can be embedded into the class of nonparametric regression learning machines; therefore, we start with the construction of nonparametric regression estimates and review results on consistency and rates of convergence. In SVMs, outcome probabilities for individuals are estimated consistently by repeatedly solving classification problems. For SVMs we review classification problem and then dichotomous probability estimation. Next we extend the algorithms for estimating probabilities using k-NN, b-NN, and RF to multicategory outcomes and discuss approaches for the multicategory probability estimation problem using SVM. In simulation studies for dichotomous and multicategory dependent variables we demonstrate the general validity of the machine learning methods and compare it with logistic regression. However, each method fails in at least one simulation scenario. We conclude with a discussion of the failures and give recommendations for selecting and tuning the methods. Applications to real data and example code are provided in a companion article (doi:10.1002/bimj.201300077). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences

PubMed Central

Bouchard, Kristofer E.; Ganguli, Surya; Brainard, Michael S.

2015-01-01

The majority of distinct sensory and motor events occur as temporally ordered sequences with rich probabilistic structure. Sequences can be characterized by the probability of transitioning from the current state to upcoming states (forward probability), as well as the probability of having transitioned to the current state from previous states (backward probability). Despite the prevalence of probabilistic sequencing of both sensory and motor events, the Hebbian mechanisms that mold synapses to reflect the statistics of experienced probabilistic sequences are not well understood. Here, we show through analytic calculations and numerical simulations that Hebbian plasticity (correlation, covariance, and STDP) with pre-synaptic competition can develop synaptic weights equal to the conditional forward transition probabilities present in the input sequence. In contrast, post-synaptic competition can develop synaptic weights proportional to the conditional backward probabilities of the same input sequence. We demonstrate that to stably reflect the conditional probability of a neuron's inputs and outputs, local Hebbian plasticity requires balance between competitive learning forces that promote synaptic differentiation and homogenizing learning forces that promote synaptic stabilization. The balance between these forces dictates a prior over the distribution of learned synaptic weights, strongly influencing both the rate at which structure emerges and the entropy of the final distribution of synaptic weights. Together, these results demonstrate a simple correspondence between the biophysical organization of neurons, the site of synaptic competition, and the temporal flow of information encoded in synaptic weights by Hebbian plasticity while highlighting the utility of balancing learning forces to accurately encode probability distributions, and prior expectations over such probability distributions. PMID:26257637

Probability Modeling and Thinking: What Can We Learn from Practice?

ERIC Educational Resources Information Center

Pfannkuch, Maxine; Budgett, Stephanie; Fewster, Rachel; Fitch, Marie; Pattenwise, Simeon; Wild, Chris; Ziedins, Ilze

2016-01-01

Because new learning technologies are enabling students to build and explore probability models, we believe that there is a need to determine the big enduring ideas that underpin probabilistic thinking and modeling. By uncovering the elements of the thinking modes of expert users of probability models we aim to provide a base for the setting of…

The Effect of Simulation-Based Learning on Prospective Teachers' Inference Skills in Teaching Probability

ERIC Educational Resources Information Center

Koparan, Timur; Yilmaz, Gül Kaleli

2015-01-01

The effect of simulation-based probability teaching on the prospective teachers' inference skills has been examined with this research. In line with this purpose, it has been aimed to examine the design, implementation and efficiency of a learning environment for experimental probability. Activities were built on modeling, simulation and the…

Learning Probabilities in Computer Engineering by Using a Competency- and Problem-Based Approach

ERIC Educational Resources Information Center

Khoumsi, Ahmed; Hadjou, Brahim

2005-01-01

Our department has redesigned its electrical and computer engineering programs by adopting a learning methodology based on competence development, problem solving, and the realization of design projects. In this article, we show how this pedagogical approach has been successfully used for learning probabilities and their application to computer…

Word Learning by Preschoolers with SLI: Effect of Phonotactic Probability and Object Familiarity

ERIC Educational Resources Information Center

Gray, Shelley; Brinkley, Shara; Svetina, Dubravka

2012-01-01

Purpose: In this study, the authors investigated whether previous findings of a low phonotactic probability/unfamiliar object word-learning advantage in preschoolers could be replicated, whether this advantage would be apparent at different "stages" of word learning, and whether findings would differ for preschoolers with specific language…

Understanding Bilingual Word Learning: The Role of Phonotactic Probability and Phonological Neighborhood Density

ERIC Educational Resources Information Center

Nair, Vishnu K. K.; Biedermann, Britta; Nickels, Lyndsey

2017-01-01

Purpose: Previous research has shown that the language-learning mechanism is affected by bilingualism resulting in a novel word learning advantage for bilingual speakers. However, less is known about the factors that might influence this advantage. This article reports an investigation of 2 factors: phonotactic probability and phonological…

Fostering Positive Attitude in Probability Learning Using Graphing Calculator

ERIC Educational Resources Information Center

Tan, Choo-Kim; Harji, Madhubala Bava; Lau, Siong-Hoe

2011-01-01

Although a plethora of research evidence highlights positive and significant outcomes of the incorporation of the Graphing Calculator (GC) in mathematics education, its use in the teaching and learning process appears to be limited. The obvious need to revisit the teaching and learning of Probability has resulted in this study, i.e. to incorporate…

Guidance of spatial attention by incidental learning and endogenous cuing

PubMed Central

Jiang, Yuhong V.; Swallow, Khena M.; Rosenbaum, Gail M.

2012-01-01

Our visual system is highly sensitive to regularities in the environment. Locations that were important in one’s previous experience are often prioritized during search, even though observers may not be aware of the learning. In this study we characterized the guidance of spatial attention by incidental learning of a target’s spatial probability, and examined the interaction between endogenous cuing and probability cuing. Participants searched for a target (T) among distractors (L’s). The target was more often located in one region of the screen than in others. We found that search RT was faster when the target appeared in the high-frequency region rather than the low-frequency regions. This difference increased when there were more items on the display, suggesting that probability cuing guides spatial attention. Additional data indicated that on their own, probability cuing and endogenous cuing (e.g., a central arrow that predicted a target’s location) were similarly effective at guiding attention. However, when both cues were presented at once, probability cuing was largely eliminated. Thus, although both incidental learning and endogenous cuing can effectively guide attention, endogenous cuing takes precedence over incidental learning. PMID:22506784

What subject matter questions motivate the use of machine learning approaches compared to statistical models for probability prediction?

PubMed

Binder, Harald

2014-07-01

This is a discussion of the following papers: "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Theory" by Jochen Kruppa, Yufeng Liu, Gérard Biau, Michael Kohler, Inke R. König, James D. Malley, and Andreas Ziegler; and "Probability estimation with machine learning methods for dichotomous and multicategory outcome: Applications" by Jochen Kruppa, Yufeng Liu, Hans-Christian Diener, Theresa Holste, Christian Weimar, Inke R. König, and Andreas Ziegler. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Effect of Incremental Changes in Phonotactic Probability and Neighborhood Density on Word Learning by Preschool Children

ERIC Educational Resources Information Center

Storkel, Holly L.; Bontempo, Daniel E.; Aschenbrenner, Andrew J.; Maekawa, Junko; Lee, Su-Yeon

2013-01-01

Purpose: Phonotactic probability or neighborhood density has predominately been defined through the use of gross distinctions (i.e., low vs. high). In the current studies, the authors examined the influence of finer changes in probability (Experiment 1) and density (Experiment 2) on word learning. Method: The authors examined the full range of…

Psychomotor development and learning difficulties in preschool children with probable attention deficit hyperactivity disorder: An epidemiological study in Navarre and La Rioja.

PubMed

Marín-Méndez, J J; Borra-Ruiz, M C; Álvarez-Gómez, M J; Soutullo Esperón, C

2017-10-01

ADHD symptoms begin to appear at preschool age. ADHD may have a significant negative impact on academic performance. In Spain, there are no standardized tools for detecting ADHD at preschool age, nor is there data about the incidence of this disorder. To evaluate developmental factors and learning difficulties associated with probable ADHD and to assess the impact of ADHD in school performance. We conducted a population-based study with a stratified multistage proportional cluster sample design. We found significant differences between probable ADHD and parents' perception of difficulties in expressive language, comprehension, and fine motor skills, as well as in emotions, concentration, behaviour, and relationships. Around 34% of preschool children with probable ADHD showed global learning difficulties, mainly in patients with the inattentive type. According to the multivariate analysis, learning difficulties were significantly associated with both delayed psychomotor development during the first 3 years of life (OR: 5.57) as assessed by parents, and probable ADHD (OR: 2.34) CONCLUSIONS: There is a connection between probable ADHD in preschool children and parents' perception of difficulties in several dimensions of development and learning. Early detection of ADHD at preschool ages is necessary to start prompt and effective clinical and educational interventions. Copyright © 2016 Sociedad Española de Neurología. Publicado por Elsevier España, S.L.U. All rights reserved.

Modulation of Complex-Spike Duration and Probability during Cerebellar Motor Learning in Visually Guided Smooth-Pursuit Eye Movements of Monkeys

PubMed Central

2017-01-01

Abstract Activation of an inferior olivary neuron powerfully excites Purkinje cells via its climbing fiber input and triggers a characteristic high-frequency burst, known as the complex spike (CS). The theory of cerebellar learning postulates that the CS induces long-lasting depression of the strength of synapses from active parallel fibers onto Purkinje cells, and that synaptic depression leads to changes in behavior. Prior reports showed that a CS on one learning trial is linked to a properly timed depression of simple spikes on the subsequent trial, as well as a learned change in pursuit eye movement. Further, the duration of a CS is a graded instruction for single-trial plasticity and behavioral learning. We now show across multiple learning paradigms that both the probability and duration of CS responses are correlated with the magnitudes of neural and behavioral learning in awake behaving monkeys. When the direction of the instruction for learning repeatedly was in the same direction or alternated directions, the duration and probability of CS responses decreased over a learning block along with the magnitude of trial-over-trial neural learning. When the direction of the instruction was randomized, CS duration, CS probability, and neural and behavioral learning remained stable across time. In contrast to depression, potentiation of simple-spike firing rate for ON-direction learning instructions follows a longer time course and plays a larger role as depression wanes. Computational analysis provides a model that accounts fully for the detailed statistics of a complex set of data. PMID:28698888

Q-Learning-Based Adjustable Fixed-Phase Quantum Grover Search Algorithm

NASA Astrophysics Data System (ADS)

Guo, Ying; Shi, Wensha; Wang, Yijun; Hu, Jiankun

2017-02-01

We demonstrate that the rotation phase can be suitably chosen to increase the efficiency of the phase-based quantum search algorithm, leading to a dynamic balance between iterations and success probabilities of the fixed-phase quantum Grover search algorithm with Q-learning for a given number of solutions. In this search algorithm, the proposed Q-learning algorithm, which is a model-free reinforcement learning strategy in essence, is used for performing a matching algorithm based on the fraction of marked items λ and the rotation phase α. After establishing the policy function α = π(λ), we complete the fixed-phase Grover algorithm, where the phase parameter is selected via the learned policy. Simulation results show that the Q-learning-based Grover search algorithm (QLGA) enables fewer iterations and gives birth to higher success probabilities. Compared with the conventional Grover algorithms, it avoids the optimal local situations, thereby enabling success probabilities to approach one.

Stimulus discriminability may bias value-based probabilistic learning.

PubMed

Schutte, Iris; Slagter, Heleen A; Collins, Anne G E; Frank, Michael J; Kenemans, J Leon

2017-01-01

Reinforcement learning tasks are often used to assess participants' tendency to learn more from the positive or more from the negative consequences of one's action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimination experiment. Participants were more reliable in selecting rewarding characters that were more discriminable, leading to differences in learning curves and their sensitivity to reward probability. This difference in experienced reinforcement history was accompanied by performance biases in a test phase assessing ability to learn from positive vs. negative outcomes. In a subsequent large-scale web-based experiment, this impact of task version on learning and test measures was replicated and extended. Collectively, these findings imply a key role for perceptual factors in guiding reward learning and underscore the need to control stimulus discriminability when making inferences about individual differences in reinforcement learning.

The Influence of Part-Word Phonotactic Probability/Neighborhood Density on Word Learning by Preschool Children Varying in Expressive Vocabulary

ERIC Educational Resources Information Center

Storkel, Holly L.; Hoover, Jill R.

2011-01-01

The goal of this study was to examine the influence of part-word phonotactic probability/neighborhood density on word learning by preschool children with normal vocabularies that varied in size. Ninety-eight children (age 2 ; 11-6 ; 0) were taught consonant-vowel-consonant (CVC) nonwords orthogonally varying in the probability/density of the CV…

Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability.

PubMed

Dawson, Michael R W; Gupta, Maya

2017-01-01

Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent's environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned.

Probability matching in perceptrons: Effects of conditional dependence and linear nonseparability

PubMed Central

2017-01-01

Probability matching occurs when the behavior of an agent matches the likelihood of occurrence of events in the agent’s environment. For instance, when artificial neural networks match probability, the activity in their output unit equals the past probability of reward in the presence of a stimulus. Our previous research demonstrated that simple artificial neural networks (perceptrons, which consist of a set of input units directly connected to a single output unit) learn to match probability when presented different cues in isolation. The current paper extends this research by showing that perceptrons can match probabilities when presented simultaneous cues, with each cue signaling different reward likelihoods. In our first simulation, we presented up to four different cues simultaneously; the likelihood of reward signaled by the presence of one cue was independent of the likelihood of reward signaled by other cues. Perceptrons learned to match reward probabilities by treating each cue as an independent source of information about the likelihood of reward. In a second simulation, we violated the independence between cues by making some reward probabilities depend upon cue interactions. We did so by basing reward probabilities on a logical combination (AND or XOR) of two of the four possible cues. We also varied the size of the reward associated with the logical combination. We discovered that this latter manipulation was a much better predictor of perceptron performance than was the logical structure of the interaction between cues. This indicates that when perceptrons learn to match probabilities, they do so by assuming that each signal of a reward is independent of any other; the best predictor of perceptron performance is a quantitative measure of the independence of these input signals, and not the logical structure of the problem being learned. PMID:28212422

The analysis of probability task completion; Taxonomy of probabilistic thinking-based across gender in elementary school students

NASA Astrophysics Data System (ADS)

Sari, Dwi Ivayana; Budayasa, I. Ketut; Juniati, Dwi

2017-08-01

Formulation of mathematical learning goals now is not only oriented on cognitive product, but also leads to cognitive process, which is probabilistic thinking. Probabilistic thinking is needed by students to make a decision. Elementary school students are required to develop probabilistic thinking as foundation to learn probability at higher level. A framework of probabilistic thinking of students had been developed by using SOLO taxonomy, which consists of prestructural probabilistic thinking, unistructural probabilistic thinking, multistructural probabilistic thinking and relational probabilistic thinking. This study aimed to analyze of probability task completion based on taxonomy of probabilistic thinking. The subjects were two students of fifth grade; boy and girl. Subjects were selected by giving test of mathematical ability and then based on high math ability. Subjects were given probability tasks consisting of sample space, probability of an event and probability comparison. The data analysis consisted of categorization, reduction, interpretation and conclusion. Credibility of data used time triangulation. The results was level of boy's probabilistic thinking in completing probability tasks indicated multistructural probabilistic thinking, while level of girl's probabilistic thinking in completing probability tasks indicated unistructural probabilistic thinking. The results indicated that level of boy's probabilistic thinking was higher than level of girl's probabilistic thinking. The results could contribute to curriculum developer in developing probability learning goals for elementary school students. Indeed, teachers could teach probability with regarding gender difference.

Probability Learning: Changes in Behavior Across Time and Development

PubMed Central

Plate, Rista C.; Fulvio, Jacqueline M.; Shutts, Kristin; Green, C. Shawn; Pollak, Seth D.

2017-01-01

Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience—within a learning session and over development—influences people’s use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4–11 years) and adults searched for rewards hidden in locations with predetermined probabilities. In Experiment 1, children (n = 42) and adults (n = 32) changed strategies to maximize reward receipt over time. However, adults demonstrated greater strategy change efficiency. Making the predetermined probabilities more difficult to learn (Experiment 2) delayed effective strategy change for children (n = 39) and adults (n = 33). Taken together, these data characterize how children and adults alike react flexibly and change behavior according to incoming information. PMID:28121026

Feedback Valence Affects Auditory Perceptual Learning Independently of Feedback Probability

PubMed Central

Amitay, Sygal; Moore, David R.; Molloy, Katharine; Halliday, Lorna F.

2015-01-01

Previous studies have suggested that negative feedback is more effective in driving learning than positive feedback. We investigated the effect on learning of providing varying amounts of negative and positive feedback while listeners attempted to discriminate between three identical tones; an impossible task that nevertheless produces robust learning. Four feedback conditions were compared during training: 90% positive feedback or 10% negative feedback informed the participants that they were doing equally well, while 10% positive or 90% negative feedback informed them they were doing equally badly. In all conditions the feedback was random in relation to the listeners’ responses (because the task was to discriminate three identical tones), yet both the valence (negative vs. positive) and the probability of feedback (10% vs. 90%) affected learning. Feedback that informed listeners they were doing badly resulted in better post-training performance than feedback that informed them they were doing well, independent of valence. In addition, positive feedback during training resulted in better post-training performance than negative feedback, but only positive feedback indicating listeners were doing badly on the task resulted in learning. As we have previously speculated, feedback that better reflected the difficulty of the task was more effective in driving learning than feedback that suggested performance was better than it should have been given perceived task difficulty. But contrary to expectations, positive feedback was more effective than negative feedback in driving learning. Feedback thus had two separable effects on learning: feedback valence affected motivation on a subjectively difficult task, and learning occurred only when feedback probability reflected the subjective difficulty. To optimize learning, training programs need to take into consideration both feedback valence and probability. PMID:25946173

Post-traumatic stress is associated with verbal learning, memory, and psychomotor speed in HIV-infected and HIV-uninfected women.

PubMed

Rubin, Leah H; Pyra, Maria; Cook, Judith A; Weber, Kathleen M; Cohen, Mardge H; Martin, Eileen; Valcour, Victor; Milam, Joel; Anastos, Kathryn; Young, Mary A; Alden, Christine; Gustafson, Deborah R; Maki, Pauline M

2016-04-01

The prevalence of post-traumatic stress disorder (PTSD) is higher among HIV-infected (HIV+) women compared with HIV-uninfected (HIV-) women, and deficits in episodic memory are a common feature of both PTSD and HIV infection. We investigated the association between a probable PTSD diagnosis using the PTSD Checklist-Civilian (PCL-C) version and verbal learning and memory using the Hopkins Verbal Learning Test in 1004 HIV+ and 496 at-risk HIV- women. HIV infection was not associated with a probable PTSD diagnosis (17% HIV+, 16% HIV-; p = 0.49) but was associated with lower verbal learning (p < 0.01) and memory scores (p < 0.01). Irrespective of HIV status, a probable PTSD diagnosis was associated with poorer performance in verbal learning (p < 0.01) and memory (p < 0.01) and psychomotor speed (p < 0.001). The particular pattern of cognitive correlates of probable PTSD varied depending on exposure to sexual abuse and/or violence, with exposure to either being associated with a greater number of cognitive domains and a worse cognitive profile. A statistical interaction between HIV serostatus and PTSD was observed on the fine motor skills domain (p = 0.03). Among women with probable PTSD, HIV- women performed worse than HIV+ women on fine motor skills (p = 0.01), but among women without probable PTSD, there was no significant difference in performance between the groups (p = 0.59). These findings underscore the importance of considering mental health factors as correlates to cognitive deficits in women with HIV.

Learning about Posterior Probability: Do Diagrams and Elaborative Interrogation Help?

ERIC Educational Resources Information Center

Clinton, Virginia; Alibali, Martha Wagner; Nathan, Mitchel J.

2016-01-01

To learn from a text, students must make meaningful connections among related ideas in that text. This study examined the effectiveness of two methods of improving connections--elaborative interrogation and diagrams--in written lessons about posterior probability. Undergraduate students (N = 198) read a lesson in one of three questioning…

Spatial Probability Cuing and Right Hemisphere Damage

ERIC Educational Resources Information Center

Shaqiri, Albulena; Anderson, Britt

2012-01-01

In this experiment we studied statistical learning, inter-trial priming, and visual attention. We assessed healthy controls and right brain damaged (RBD) patients with and without neglect, on a simple visual discrimination task designed to measure priming effects and probability learning. All participants showed a preserved priming effect for item…

Diagnosis of students' ability in a statistical course based on Rasch probabilistic outcome

NASA Astrophysics Data System (ADS)

Mahmud, Zamalia; Ramli, Wan Syahira Wan; Sapri, Shamsiah; Ahmad, Sanizah

2017-06-01

Measuring students' ability and performance are important in assessing how well students have learned and mastered the statistical courses. Any improvement in learning will depend on the student's approaches to learning, which are relevant to some factors of learning, namely assessment methods carrying out tasks consisting of quizzes, tests, assignment and final examination. This study has attempted an alternative approach to measure students' ability in an undergraduate statistical course based on the Rasch probabilistic model. Firstly, this study aims to explore the learning outcome patterns of students in a statistics course (Applied Probability and Statistics) based on an Entrance-Exit survey. This is followed by investigating students' perceived learning ability based on four Course Learning Outcomes (CLOs) and students' actual learning ability based on their final examination scores. Rasch analysis revealed that students perceived themselves as lacking the ability to understand about 95% of the statistics concepts at the beginning of the class but eventually they had a good understanding at the end of the 14 weeks class. In terms of students' performance in their final examination, their ability in understanding the topics varies at different probability values given the ability of the students and difficulty of the questions. Majority found the probability and counting rules topic to be the most difficult to learn.

Simple artificial neural networks that match probability and exploit and explore when confronting a multiarmed bandit.

PubMed

Dawson, Michael R W; Dupuis, Brian; Spetch, Marcia L; Kelly, Debbie M

2009-08-01

The matching law (Herrnstein 1961) states that response rates become proportional to reinforcement rates; this is related to the empirical phenomenon called probability matching (Vulkan 2000). Here, we show that a simple artificial neural network generates responses consistent with probability matching. This behavior was then used to create an operant procedure for network learning. We use the multiarmed bandit (Gittins 1989), a classic problem of choice behavior, to illustrate that operant training balances exploiting the bandit arm expected to pay off most frequently with exploring other arms. Perceptrons provide a medium for relating results from neural networks, genetic algorithms, animal learning, contingency theory, reinforcement learning, and theories of choice.

Saccade selection when reward probability is dynamically manipulated using Markov chains

PubMed Central

Lovejoy, Lee P.; Krauzlis, Richard J.

2012-01-01

Markov chains (stochastic processes where probabilities are assigned based on the previous outcome) are commonly used to examine the transitions between behavioral states, such as those that occur during foraging or social interactions. However, relatively little is known about how well primates can incorporate knowledge about Markov chains into their behavior. Saccadic eye movements are an example of a simple behavior influenced by information about probability, and thus are good candidates for testing whether subjects can learn Markov chains. In addition, when investigating the influence of probability on saccade target selection, the use of Markov chains could provide an alternative method that avoids confounds present in other task designs. To investigate these possibilities, we evaluated human behavior on a task in which stimulus reward probabilities were assigned using a Markov chain. On each trial, the subject selected one of four identical stimuli by saccade; after selection, feedback indicated the rewarded stimulus. Each session consisted of 200–600 trials, and on some sessions, the reward magnitude varied. On sessions with a uniform reward, subjects (n = 6) learned to select stimuli at a frequency close to reward probability, which is similar to human behavior on matching or probability classification tasks. When informed that a Markov chain assigned reward probabilities, subjects (n = 3) learned to select the greatest reward probability more often, bringing them close to behavior that maximizes reward. On sessions where reward magnitude varied across stimuli, subjects (n = 6) demonstrated preferences for both greater reward probability and greater reward magnitude, resulting in a preference for greater expected value (the product of reward probability and magnitude). These results demonstrate that Markov chains can be used to dynamically assign probabilities that are rapidly exploited by human subjects during saccade target selection. PMID:18330552

Saccade selection when reward probability is dynamically manipulated using Markov chains.

PubMed

Nummela, Samuel U; Lovejoy, Lee P; Krauzlis, Richard J

2008-05-01

Markov chains (stochastic processes where probabilities are assigned based on the previous outcome) are commonly used to examine the transitions between behavioral states, such as those that occur during foraging or social interactions. However, relatively little is known about how well primates can incorporate knowledge about Markov chains into their behavior. Saccadic eye movements are an example of a simple behavior influenced by information about probability, and thus are good candidates for testing whether subjects can learn Markov chains. In addition, when investigating the influence of probability on saccade target selection, the use of Markov chains could provide an alternative method that avoids confounds present in other task designs. To investigate these possibilities, we evaluated human behavior on a task in which stimulus reward probabilities were assigned using a Markov chain. On each trial, the subject selected one of four identical stimuli by saccade; after selection, feedback indicated the rewarded stimulus. Each session consisted of 200-600 trials, and on some sessions, the reward magnitude varied. On sessions with a uniform reward, subjects (n = 6) learned to select stimuli at a frequency close to reward probability, which is similar to human behavior on matching or probability classification tasks. When informed that a Markov chain assigned reward probabilities, subjects (n = 3) learned to select the greatest reward probability more often, bringing them close to behavior that maximizes reward. On sessions where reward magnitude varied across stimuli, subjects (n = 6) demonstrated preferences for both greater reward probability and greater reward magnitude, resulting in a preference for greater expected value (the product of reward probability and magnitude). These results demonstrate that Markov chains can be used to dynamically assign probabilities that are rapidly exploited by human subjects during saccade target selection.

Probability Learning: Changes in Behavior across Time and Development

ERIC Educational Resources Information Center

Plate, Rista C.; Fulvio, Jacqueline M.; Shutts, Kristin; Green, C. Shawn; Pollak, Seth D.

2018-01-01

Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience--within a learning session and over development--influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults…

Hold It! The Influence of Lingering Rewards on Choice Diversification and Persistence

ERIC Educational Resources Information Center

Schulze, Christin; van Ravenzwaaij, Don; Newell, Ben R.

2017-01-01

Learning to choose adaptively when faced with uncertain and variable outcomes is a central challenge for decision makers. This study examines repeated choice in dynamic probability learning tasks in which outcome probabilities changed either as a function of the choices participants made or independently of those choices. This presence/absence of…

Impact of Homeland Security Communities of Learning: Developing a Strategy for Training and Collaboration

DTIC Science & Technology

2006-09-01

education. LCMS allow subject matter experts, with little technology skills to develop curriculum, deliver courses, and monitor e- learning. Distance...occurring in year seven and therefore it has zero probability of occurring in the first five years. The probability of Ev-2 occurring between years 7 and

Learning Axes and Bridging Tools in a Technology-Based Design for Statistics

ERIC Educational Resources Information Center

Abrahamson, Dor; Wilensky, Uri

2007-01-01

We introduce a design-based research framework, "learning axes and bridging tools," and demonstrate its application in the preparation and study of an implementation of a middle-school experimental computer-based unit on probability and statistics, "ProbLab" (Probability Laboratory, Abrahamson and Wilensky 2002 [Abrahamson, D., & Wilensky, U.…

Implementing Inquiry-Based Learning and Examining the Effects in Junior College Probability Lessons

ERIC Educational Resources Information Center

Chong, Jessie Siew Yin; Chong, Maureen Siew Fang; Shahrill, Masitah; Abdullah, Nor Azura

2017-01-01

This study examined how Year 12 students use their inquiry skills in solving conditional probability questions by means of Inquiry-Based Learning application. The participants consisted of 66 students of similar academic abilities in Mathematics, selected from three classes, along with their respective teachers. Observational rubric and lesson…

Influence of Phonotactic Probability/Neighbourhood Density on Lexical Learning in Late Talkers

ERIC Educational Resources Information Center

MacRoy-Higgins, Michelle; Schwartz, Richard G.; Shafer, Valerie L.; Marton, Klara

2013-01-01

Background: Toddlers who are late talkers demonstrate delays in phonological and lexical skills. However, the influence of phonological factors on lexical acquisition in toddlers who are late talkers has not been examined directly. Aims: To examine the influence of phonotactic probability/neighbourhood density on word learning in toddlers who were…

Probability Learning: Changes in Behavior Across Time and Development.

PubMed

Plate, Rista C; Fulvio, Jacqueline M; Shutts, Kristin; Green, C Shawn; Pollak, Seth D

2018-01-01

Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience-within a learning session and over development-influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults searched for rewards hidden in locations with predetermined probabilities. In Experiment 1, children (n = 42) and adults (n = 32) changed strategies to maximize reward receipt over time. However, adults demonstrated greater strategy change efficiency. Making the predetermined probabilities more difficult to learn (Experiment 2) delayed effective strategy change for children (n = 39) and adults (n = 33). Taken together, these data characterize how children and adults alike react flexibly and change behavior according to incoming information. © 2017 The Authors. Child Development © 2017 Society for Research in Child Development, Inc.

Calibrating perceived understanding and competency in probability concepts: A diagnosis of learning difficulties based on Rasch probabilistic model

NASA Astrophysics Data System (ADS)

Mahmud, Zamalia; Porter, Anne; Salikin, Masniyati; Ghani, Nor Azura Md

2015-12-01

Students' understanding of probability concepts have been investigated from various different perspectives. Competency on the other hand is often measured separately in the form of test structure. This study was set out to show that perceived understanding and competency can be calibrated and assessed together using Rasch measurement tools. Forty-four students from the STAT131 Understanding Uncertainty and Variation course at the University of Wollongong, NSW have volunteered to participate in the study. Rasch measurement which is based on a probabilistic model is used to calibrate the responses from two survey instruments and investigate the interactions between them. Data were captured from the e-learning platform Moodle where students provided their responses through an online quiz. The study shows that majority of the students perceived little understanding about conditional and independent events prior to learning about it but tend to demonstrate a slightly higher competency level afterward. Based on the Rasch map, there is indication of some increase in learning and knowledge about some probability concepts at the end of the two weeks lessons on probability concepts.

Implicit Learning of Predictive Relationships in Three-element Visual Sequences by Young and Old Adults

PubMed Central

Howard, James H.; Howard, Darlene V.; Dennis, Nancy A.; Kelly, Andrew J.

2008-01-01

Knowledge of sequential relationships enables future events to be anticipated and processed efficiently. Research with the serial reaction time task (SRTT) has shown that sequence learning often occurs implicitly without effort or awareness. Here we report four experiments that use a triplet-learning task (TLT) to investigate sequence learning in young and older adults. In the TLT people respond only to the last target event in a series of discrete, three-event sequences or triplets. Target predictability is manipulated by varying the triplet frequency (joint probability) and/or the statistical relationships (conditional probabilities) among events within the triplets. Results revealed that both groups learned, though older adults showed less learning of both joint and conditional probabilities. Young people used the statistical information in both cues, but older adults relied primarily on information in the second cue alone. We conclude that the TLT complements and extends the SRTT and other tasks by offering flexibility in the kinds of sequential statistical regularities that may be studied as well as by controlling event timing and eliminating motor response sequencing. PMID:18763897

Radar detection with the Neyman-Pearson criterion using supervised-learning-machines trained with the cross-entropy error

NASA Astrophysics Data System (ADS)

Jarabo-Amores, María-Pilar; la Mata-Moya, David de; Gil-Pita, Roberto; Rosa-Zurera, Manuel

2013-12-01

The application of supervised learning machines trained to minimize the Cross-Entropy error to radar detection is explored in this article. The detector is implemented with a learning machine that implements a discriminant function, which output is compared to a threshold selected to fix a desired probability of false alarm. The study is based on the calculation of the function the learning machine approximates to during training, and the application of a sufficient condition for a discriminant function to be used to approximate the optimum Neyman-Pearson (NP) detector. In this article, the function a supervised learning machine approximates to after being trained to minimize the Cross-Entropy error is obtained. This discriminant function can be used to implement the NP detector, which maximizes the probability of detection, maintaining the probability of false alarm below or equal to a predefined value. Some experiments about signal detection using neural networks are also presented to test the validity of the study.

Exploiting risk-reward structures in decision making under uncertainty.

PubMed

Leuker, Christina; Pachur, Thorsten; Hertwig, Ralph; Pleskac, Timothy J

2018-06-01

People often have to make decisions under uncertainty-that is, in situations where the probabilities of obtaining a payoff are unknown or at least difficult to ascertain. One solution to this problem is to infer the probability from the magnitude of the potential payoff and thus exploit the inverse relationship between payoffs and probabilities that occurs in many domains in the environment. Here, we investigated how the mind may implement such a solution: (1) Do people learn about risk-reward relationships from the environment-and if so, how? (2) How do learned risk-reward relationships impact preferences in decision-making under uncertainty? Across three experiments (N = 352), we found that participants can learn risk-reward relationships from being exposed to choice environments with a negative, positive, or uncorrelated risk-reward relationship. They were able to learn the associations both from gambles with explicitly stated payoffs and probabilities (Experiments 1 & 2) and from gambles about epistemic events (Experiment 3). In subsequent decisions under uncertainty, participants often exploited the learned association by inferring probabilities from the magnitudes of the payoffs. This inference systematically influenced their preferences under uncertainty: Participants who had been exposed to a negative risk-reward relationship tended to prefer the uncertain option over a smaller sure option for low payoffs, but not for high payoffs. This pattern reversed in the positive condition and disappeared in the uncorrelated condition. This adaptive change in preferences is consistent with the use of the risk-reward heuristic. Copyright © 2018 Elsevier B.V. All rights reserved.

Statistical Learning in a Natural Language by 8-Month-Old Infants

PubMed Central

Pelucchi, Bruna; Hay, Jessica F.; Saffran, Jenny R.

2013-01-01

Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants’ ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition. PMID:19489896

Probability density function learning by unsupervised neurons.

PubMed

Fiori, S

2001-10-01

In a recent work, we introduced the concept of pseudo-polynomial adaptive activation function neuron (FAN) and presented an unsupervised information-theoretic learning theory for such structure. The learning model is based on entropy optimization and provides a way of learning probability distributions from incomplete data. The aim of the present paper is to illustrate some theoretical features of the FAN neuron, to extend its learning theory to asymmetrical density function approximation, and to provide an analytical and numerical comparison with other known density function estimation methods, with special emphasis to the universal approximation ability. The paper also provides a survey of PDF learning from incomplete data, as well as results of several experiments performed on real-world problems and signals.

Statistical learning in a natural language by 8-month-old infants.

PubMed

Pelucchi, Bruna; Hay, Jessica F; Saffran, Jenny R

2009-01-01

Numerous studies over the past decade support the claim that infants are equipped with powerful statistical language learning mechanisms. The primary evidence for statistical language learning in word segmentation comes from studies using artificial languages, continuous streams of synthesized syllables that are highly simplified relative to real speech. To what extent can these conclusions be scaled up to natural language learning? In the current experiments, English-learning 8-month-old infants' ability to track transitional probabilities in fluent infant-directed Italian speech was tested (N = 72). The results suggest that infants are sensitive to transitional probability cues in unfamiliar natural language stimuli, and support the claim that statistical learning is sufficiently robust to support aspects of real-world language acquisition.

Feedback-Driven Trial-by-Trial Learning in Autism Spectrum Disorders

PubMed Central

Solomon, Marjorie; Frank, Michael J.; Ragland, J. Daniel; Smith, Anne C.; Niendam, Tara A.; Lesh, Tyler A.; Grayson, David S.; Beck, Jonathan S.; Matter, John C.; Carter, Cameron S.

2017-01-01

Objective Impairments in learning are central to autism spectrum disorders. The authors investigated the cognitive and neural basis of these deficits in young adults with autism spectrum disorders using a well-characterized probabilistic reinforcement learning paradigm. Method The probabilistic selection task was implemented among matched participants with autism spectrum disorders (N=22) and with typical development (N=25), aged 18–40 years, using rapid event-related functional MRI. Participants were trained to choose the correct stimulus in high-probability (AB), medium-probability (CD), and low-probability (EF) pairs, presented with valid feedback 80%, 70%, and 60% of the time, respectively. Whole-brain voxel-wise and parametric modulator analyses examined early and late learning during the stimulus and feedback epochs of the task. Results The groups exhibited comparable performance on medium- and low-probability pairs. Typically developing persons showed higher accuracy on the high-probability pair, better win-stay performance (selection of the previously rewarded stimulus on the next trial of that type), and more robust recruitment of the anterior and medial prefrontal cortex during the stimulus epoch, suggesting development of an intact reward-based working memory for recent stimulus values. Throughout the feedback epoch, individuals with autism spectrum disorders exhibited greater recruitment of the anterior cingulate and orbito-frontal cortices compared with individuals with typical development, indicating continuing trial-by-trial activity related to feedback processing. Conclusions Individuals with autism spectrum disorders exhibit learning deficits reflecting impaired ability to develop an effective reward-based working memory to guide stimulus selection. Instead, they continue to rely on trial-by-trial feedback processing to support learning dependent upon engagement of the anterior cingulate and orbito-frontal cortices. PMID:25158242

Reinforcement Learning for Constrained Energy Trading Games With Incomplete Information.

PubMed

Wang, Huiwei; Huang, Tingwen; Liao, Xiaofeng; Abu-Rub, Haitham; Chen, Guo

2017-10-01

This paper considers the problem of designing adaptive learning algorithms to seek the Nash equilibrium (NE) of the constrained energy trading game among individually strategic players with incomplete information. In this game, each player uses the learning automaton scheme to generate the action probability distribution based on his/her private information for maximizing his own averaged utility. It is shown that if one of admissible mixed-strategies converges to the NE with probability one, then the averaged utility and trading quantity almost surely converge to their expected ones, respectively. For the given discontinuous pricing function, the utility function has already been proved to be upper semicontinuous and payoff secure which guarantee the existence of the mixed-strategy NE. By the strict diagonal concavity of the regularized Lagrange function, the uniqueness of NE is also guaranteed. Finally, an adaptive learning algorithm is provided to generate the strategy probability distribution for seeking the mixed-strategy NE.

More than words: Adults learn probabilities over categories and relationships between them.

PubMed

Hudson Kam, Carla L

2009-04-01

This study examines whether human learners can acquire statistics over abstract categories and their relationships to each other. Adult learners were exposed to miniature artificial languages containing variation in the ordering of the Subject, Object, and Verb constituents. Different orders (e.g. SOV, VSO) occurred in the input with different frequencies, but the occurrence of one order versus another was not predictable. Importantly, the language was constructed such that participants could only match the overall input probabilities if they were tracking statistics over abstract categories, not over individual words. At test, participants reproduced the probabilities present in the input with a high degree of accuracy. Closer examination revealed that learner's were matching the probabilities associated with individual verbs rather than the category as a whole. However, individual nouns had no impact on word orders produced. Thus, participants learned the probabilities of a particular ordering of the abstract grammatical categories Subject and Object associated with each verb. Results suggest that statistical learning mechanisms are capable of tracking relationships between abstract linguistic categories in addition to individual items.

Blind Students' Learning of Probability through the Use of a Tactile Model

ERIC Educational Resources Information Center

Vita, Aida Carvalho; Kataoka, Verônica Yumi

2014-01-01

The objective of this paper is to discuss how blind students learn basic concepts of probability using the tactile model proposed by Vita (2012). Among the activities were part of the teaching sequence "Jefferson's Random Walk", in which students built a tree diagram (using plastic trays, foam cards, and toys), and pictograms in 3D…

Effects of Multiple Simulation Presentation among Students of Different Anxiety Levels in the Learning of Probability

ERIC Educational Resources Information Center

Fong, Soon Fook; Por, Fei Ping; Tang, Ai Ling

2012-01-01

The purpose of this study was to investigate the effects of multiple simulation presentation in interactive multimedia are on the achievement of students with different levels of anxiety in the learning of Probability. The interactive multimedia courseware was developed in two different modes, which were Multiple Simulation Presentation (MSP) and…

Computer-Based Graphical Displays for Enhancing Mental Animation and Improving Reasoning in Novice Learning of Probability

ERIC Educational Resources Information Center

Kaplan, Danielle E.; Wu, Erin Chia-ling

2006-01-01

Our research suggests static and animated graphics can lead to more animated thinking and more correct problem solving in computer-based probability learning. Pilot software modules were developed for graduate online statistics courses and representation research. A study with novice graduate student statisticians compared problem solving in five…

Probability cueing of distractor locations: both intertrial facilitation and statistical learning mediate interference reduction.

PubMed

Goschy, Harriet; Bakos, Sarolta; Müller, Hermann J; Zehetleitner, Michael

2014-01-01

Targets in a visual search task are detected faster if they appear in a probable target region as compared to a less probable target region, an effect which has been termed "probability cueing." The present study investigated whether probability cueing cannot only speed up target detection, but also minimize distraction by distractors in probable distractor regions as compared to distractors in less probable distractor regions. To this end, three visual search experiments with a salient, but task-irrelevant, distractor ("additional singleton") were conducted. Experiment 1 demonstrated that observers can utilize uneven spatial distractor distributions to selectively reduce interference by distractors in frequent distractor regions as compared to distractors in rare distractor regions. Experiments 2 and 3 showed that intertrial facilitation, i.e., distractor position repetitions, and statistical learning (independent of distractor position repetitions) both contribute to the probability cueing effect for distractor locations. Taken together, the present results demonstrate that probability cueing of distractor locations has the potential to serve as a strong attentional cue for the shielding of likely distractor locations.

Brain networks for confidence weighting and hierarchical inference during probabilistic learning.

PubMed

Meyniel, Florent; Dehaene, Stanislas

2017-05-09

Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This "confidence weighting" implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an ideal-observer analysis, we demonstrate that the brain's learning algorithm relies on confidence weighting. While in the fMRI scanner, human adults attempted to learn the transition probabilities underlying an auditory or visual sequence, and reported their confidence in those estimates. They knew that these transition probabilities could change simultaneously at unpredicted moments, and therefore that the learning problem was inherently hierarchical. Subjective confidence reports tightly followed the predictions derived from the ideal observer. In particular, subjects managed to attach distinct levels of confidence to each learned transition probability, as required by Bayes-optimal inference. Distinct brain areas tracked the likelihood of new observations given current predictions, and the confidence in those predictions. Both signals were combined in the right inferior frontal gyrus, where they operated in agreement with the confidence-weighting model. This brain region also presented signatures of a hierarchical process that disentangles distinct sources of uncertainty. Together, our results provide evidence that the sense of confidence is an essential ingredient of probabilistic learning in the human brain, and that the right inferior frontal gyrus hosts a confidence-based statistical learning algorithm for auditory and visual sequences.

Brain networks for confidence weighting and hierarchical inference during probabilistic learning

PubMed Central

Meyniel, Florent; Dehaene, Stanislas

2017-01-01

Learning is difficult when the world fluctuates randomly and ceaselessly. Classical learning algorithms, such as the delta rule with constant learning rate, are not optimal. Mathematically, the optimal learning rule requires weighting prior knowledge and incoming evidence according to their respective reliabilities. This “confidence weighting” implies the maintenance of an accurate estimate of the reliability of what has been learned. Here, using fMRI and an ideal-observer analysis, we demonstrate that the brain’s learning algorithm relies on confidence weighting. While in the fMRI scanner, human adults attempted to learn the transition probabilities underlying an auditory or visual sequence, and reported their confidence in those estimates. They knew that these transition probabilities could change simultaneously at unpredicted moments, and therefore that the learning problem was inherently hierarchical. Subjective confidence reports tightly followed the predictions derived from the ideal observer. In particular, subjects managed to attach distinct levels of confidence to each learned transition probability, as required by Bayes-optimal inference. Distinct brain areas tracked the likelihood of new observations given current predictions, and the confidence in those predictions. Both signals were combined in the right inferior frontal gyrus, where they operated in agreement with the confidence-weighting model. This brain region also presented signatures of a hierarchical process that disentangles distinct sources of uncertainty. Together, our results provide evidence that the sense of confidence is an essential ingredient of probabilistic learning in the human brain, and that the right inferior frontal gyrus hosts a confidence-based statistical learning algorithm for auditory and visual sequences. PMID:28439014

Teaching Probability to Pre-Service Teachers with Argumentation Based Science Learning Approach

ERIC Educational Resources Information Center

Can, Ömer Sinan; Isleyen, Tevfik

2016-01-01

The aim of this study is to explore the effects of the argumentation based science learning (ABSL) approach on the teaching probability to pre-service teachers. The sample of the study included 41 students studying at the Department of Elementary School Mathematics Education in a public university during the 2014-2015 academic years. The study is…

Help-Seeking Decisions of Battered Women: A Test of Learned Helplessness and Two Stress Theories.

ERIC Educational Resources Information Center

Wauchope, Barbara A.

This study tested the learned helplessness theory, stress theory, and a modified stress theory to determine the best model for predicting the probability that a woman would seek help when she experienced severe violence from a male partner. The probability was hypothesized to increase as the stress of the violence experienced increased. Data were…

The Influence of Phonotactic Probability and Neighborhood Density on Children's Production of Newly Learned Words

ERIC Educational Resources Information Center

Heisler, Lori; Goffman, Lisa

2016-01-01

A word learning paradigm was used to teach children novel words that varied in phonotactic probability and neighborhood density. The effects of frequency and density on speech production were examined when phonetic forms were nonreferential (i.e., when no referent was attached) and when phonetic forms were referential (i.e., when a referent was…

Learn-as-you-go acceleration of cosmological parameter estimates

NASA Astrophysics Data System (ADS)

Aslanyan, Grigor; Easther, Richard; Price, Layne C.

2015-09-01

Cosmological analyses can be accelerated by approximating slow calculations using a training set, which is either precomputed or generated dynamically. However, this approach is only safe if the approximations are well understood and controlled. This paper surveys issues associated with the use of machine-learning based emulation strategies for accelerating cosmological parameter estimation. We describe a learn-as-you-go algorithm that is implemented in the Cosmo++ code and (1) trains the emulator while simultaneously estimating posterior probabilities; (2) identifies unreliable estimates, computing the exact numerical likelihoods if necessary; and (3) progressively learns and updates the error model as the calculation progresses. We explicitly describe and model the emulation error and show how this can be propagated into the posterior probabilities. We apply these techniques to the Planck likelihood and the calculation of ΛCDM posterior probabilities. The computation is significantly accelerated without a pre-defined training set and uncertainties in the posterior probabilities are subdominant to statistical fluctuations. We have obtained a speedup factor of 6.5 for Metropolis-Hastings and 3.5 for nested sampling. Finally, we discuss the general requirements for a credible error model and show how to update them on-the-fly.

Learn-as-you-go acceleration of cosmological parameter estimates

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

Aslanyan, Grigor; Easther, Richard; Price, Layne C., E-mail: g.aslanyan@auckland.ac.nz, E-mail: r.easther@auckland.ac.nz, E-mail: lpri691@aucklanduni.ac.nz

2015-09-01

Cosmological analyses can be accelerated by approximating slow calculations using a training set, which is either precomputed or generated dynamically. However, this approach is only safe if the approximations are well understood and controlled. This paper surveys issues associated with the use of machine-learning based emulation strategies for accelerating cosmological parameter estimation. We describe a learn-as-you-go algorithm that is implemented in the Cosmo++ code and (1) trains the emulator while simultaneously estimating posterior probabilities; (2) identifies unreliable estimates, computing the exact numerical likelihoods if necessary; and (3) progressively learns and updates the error model as the calculation progresses. We explicitlymore » describe and model the emulation error and show how this can be propagated into the posterior probabilities. We apply these techniques to the Planck likelihood and the calculation of ΛCDM posterior probabilities. The computation is significantly accelerated without a pre-defined training set and uncertainties in the posterior probabilities are subdominant to statistical fluctuations. We have obtained a speedup factor of 6.5 for Metropolis-Hastings and 3.5 for nested sampling. Finally, we discuss the general requirements for a credible error model and show how to update them on-the-fly.« less

Implicit chaining in cotton-top tamarins (Saguinus oedipus) with elements equated for probability of reinforcement

PubMed Central

Dillon, Laura; Collins, Meaghan; Conway, Maura; Cunningham, Kate

2013-01-01

Three experiments examined the implicit learning of sequences under conditions in which the elements comprising a sequence were equated in terms of reinforcement probability. In Experiment 1 cotton-top tamarins (Saguinus oedipus) experienced a five-element sequence displayed serially on a touch screen in which reinforcement probability was equated across elements at .16 per element. Tamarins demonstrated learning of this sequence with higher latencies during a random test as compared to baseline sequence training. In Experiments 2 and 3, manipulations of the procedure used in the first experiment were undertaken to rule out a confound owing to the fact that the elements in Experiment 1 bore different temporal relations to the intertrial interval (ITI), an inhibitory period. The results of Experiments 2 and 3 indicated that the implicit learning observed in Experiment 1 was not due to temporal proximity between some elements and the inhibitory ITI. The results taken together support two conclusion: First that tamarins engaged in sequence learning whether or not there was contingent reinforcement for learning the sequence, and second that this learning was not due to subtle differences in associative strength between the elements of the sequence. PMID:23344718

Optimizing one-shot learning with binary synapses.

PubMed

Romani, Sandro; Amit, Daniel J; Amit, Yali

2008-08-01

A network of excitatory synapses trained with a conservative version of Hebbian learning is used as a model for recognizing the familiarity of thousands of once-seen stimuli from those never seen before. Such networks were initially proposed for modeling memory retrieval (selective delay activity). We show that the same framework allows the incorporation of both familiarity recognition and memory retrieval, and estimate the network's capacity. In the case of binary neurons, we extend the analysis of Amit and Fusi (1994) to obtain capacity limits based on computations of signal-to-noise ratio of the field difference between selective and non-selective neurons of learned signals. We show that with fast learning (potentiation probability approximately 1), the most recently learned patterns can be retrieved in working memory (selective delay activity). A much higher number of once-seen learned patterns elicit a realistic familiarity signal in the presence of an external field. With potentiation probability much less than 1 (slow learning), memory retrieval disappears, whereas familiarity recognition capacity is maintained at a similarly high level. This analysis is corroborated in simulations. For analog neurons, where such analysis is more difficult, we simplify the capacity analysis by studying the excess number of potentiated synapses above the steady-state distribution. In this framework, we derive the optimal constraint between potentiation and depression probabilities that maximizes the capacity.

A Comprehensive Probability Project for the Upper Division One-Semester Probability Course Using Yahtzee

ERIC Educational Resources Information Center

Wilson, Jason; Lawman, Joshua; Murphy, Rachael; Nelson, Marissa

2011-01-01

This article describes a probability project used in an upper division, one-semester probability course with third-semester calculus and linear algebra prerequisites. The student learning outcome focused on developing the skills necessary for approaching project-sized math/stat application problems. These skills include appropriately defining…

The Influence of Phonotactic Probability on Nonword Repetition and Fast Mapping in 3-Year-Olds With a History of Expressive Language Delay.

PubMed

MacRoy-Higgins, Michelle; Dalton, Kevin Patrick

2015-12-01

The purpose of this study was to examine the influence of phonotactic probability on sublexical (phonological) and lexical representations in 3-year-olds who had a history of being late talkers in comparison with their peers with typical language development. Ten 3-year-olds who were late talkers and 10 age-matched typically developing controls completed nonword repetition and fast mapping tasks; stimuli for both experimental procedures differed in phonotactic probability. Both participant groups repeated nonwords containing high phonotactic probability sequences more accurately than nonwords containing low phonotactic probability sequences. Participants with typical language showed an early advantage for fast mapping high phonotactic probability words; children who were late talkers required more exposures to the novel words to show the same advantage for fast mapping high phonotactic probability words. Children who were late talkers showed similar sensitivities to phonotactic probability in nonword repetition and word learning when compared with their peers with no history of language delay. However, word learning in children who were late talkers appeared to be slower when compared with their peers.

Statistical learning of an auditory sequence and reorganization of acquired knowledge: A time course of word segmentation and ordering.

PubMed

Daikoku, Tatsuya; Yatomi, Yutaka; Yumoto, Masato

2017-01-27

Previous neural studies have supported the hypothesis that statistical learning mechanisms are used broadly across different domains such as language and music. However, these studies have only investigated a single aspect of statistical learning at a time, such as recognizing word boundaries or learning word order patterns. In this study, we neutrally investigated how the two levels of statistical learning for recognizing word boundaries and word ordering could be reflected in neuromagnetic responses and how acquired statistical knowledge is reorganised when the syntactic rules are revised. Neuromagnetic responses to the Japanese-vowel sequence (a, e, i, o, and u), presented every .45s, were recorded from 14 right-handed Japanese participants. The vowel order was constrained by a Markov stochastic model such that five nonsense words (aue, eao, iea, oiu, and uoi) were chained with an either-or rule: the probability of the forthcoming word was statistically defined (80% for one word; 20% for the other word) by the most recent two words. All of the word transition probabilities (80% and 20%) were switched in the middle of the sequence. In the first and second quarters of the sequence, the neuromagnetic responses to the words that appeared with higher transitional probability were significantly reduced compared with those that appeared with a lower transitional probability. After switching the word transition probabilities, the response reduction was replicated in the last quarter of the sequence. The responses to the final vowels in the words were significantly reduced compared with those to the initial vowels in the last quarter of the sequence. The results suggest that both within-word and between-word statistical learning are reflected in neural responses. The present study supports the hypothesis that listeners learn larger structures such as phrases first, and they subsequently extract smaller structures, such as words, from the learned phrases. The present study provides the first neurophysiological evidence that the correction of statistical knowledge requires more time than the acquisition of new statistical knowledge. Copyright © 2016 Elsevier Ltd. All rights reserved.

The extraction and integration framework: a two-process account of statistical learning.

PubMed

Thiessen, Erik D; Kronstein, Alexandra T; Hufnagle, Daniel G

2013-07-01

The term statistical learning in infancy research originally referred to sensitivity to transitional probabilities. Subsequent research has demonstrated that statistical learning contributes to infant development in a wide array of domains. The range of statistical learning phenomena necessitates a broader view of the processes underlying statistical learning. Learners are sensitive to a much wider range of statistical information than the conditional relations indexed by transitional probabilities, including distributional and cue-based statistics. We propose a novel framework that unifies learning about all of these kinds of statistical structure. From our perspective, learning about conditional relations outputs discrete representations (such as words). Integration across these discrete representations yields sensitivity to cues and distributional information. To achieve sensitivity to all of these kinds of statistical structure, our framework combines processes that extract segments of the input with processes that compare across these extracted items. In this framework, the items extracted from the input serve as exemplars in long-term memory. The similarity structure of those exemplars in long-term memory leads to the discovery of cues and categorical structure, which guides subsequent extraction. The extraction and integration framework provides a way to explain sensitivity to both conditional statistical structure (such as transitional probabilities) and distributional statistical structure (such as item frequency and variability), and also a framework for thinking about how these different aspects of statistical learning influence each other. 2013 APA, all rights reserved

The Probable Prevalence and Sociodemographic Characteristics of Specific Learning Disorder in Primary School Children in Edirne.

PubMed

Görker, Işık; Bozatli, Leyla; Korkmazlar, Ümran; Yücel Karadağ, Meltem; Ceylan, Cansın; Söğüt, Ceren; Aykutlu, Hasan Cem; Subay, Büşra; Turan, Nesrin

2017-12-01

The aim of this study was to research the probable prevalence of Specific Learning Disorder (SLD) in primary school children in Edirne City and the relationships with their sociodemographic characteristics. The sample of our study was composed of 2,174 children who were educated in primary schools in second, third, and fourth grades in the academic year 2013-2014 in Edirne City. The teachers and parents of these children were given Specific Learning Difficulties Symptom Scale, Learning Disabilities Symptoms Checklist (teacher and parent forms), and sociodemographic data forms to fill in. Binary logistic regression analysis was used to assess the risk factors for SLD. Our study revealed that the probable prevalence of SLD was 13.6%; 17% for boys and 10.4% for girls. Reading impairment was 3.6%, writing impairment was 6.9%, and mathematic impairment was 6.5%. We determined that consanguineous marriages, low income, history of neonatal jaundice were found as risks for SLD; born by caesarean, developmental delay of walking, and history of neonatal jaundice were found as risks for mathematic impairment. A history of learning difficulties of parents was a risk factor for forming SLD and subtypes. Our findings were consistent with other study results about the prevalence of SLD. The relationships between the probable prevalence rates and sociodemographic data were discussed.

The Probable Prevalence and Sociodemographic Characteristics of Specific Learning Disorder in Primary School Children in Edirne

PubMed Central

GÖRKER, Işık; BOZATLI, Leyla; KORKMAZLAR, Ümran; YÜCEL KARADAĞ, Meltem; CEYLAN, Cansın; SÖĞÜT, Ceren; AYKUTLU, Hasan Cem; SUBAY, Büşra; TURAN, Nesrin

2017-01-01

Introduction The aim of this study was to research the probable prevalence of Specific Learning Disorder (SLD) in primary school children in Edirne City and the relationships with their sociodemographic characteristics. Methods The sample of our study was composed of 2,174 children who were educated in primary schools in second, third, and fourth grades in the academic year 2013–2014 in Edirne City. The teachers and parents of these children were given Specific Learning Difficulties Symptom Scale, Learning Disabilities Symptoms Checklist (teacher and parent forms), and sociodemographic data forms to fill in. Binary logistic regression analysis was used to assess the risk factors for SLD. Results Our study revealed that the probable prevalence of SLD was 13.6%; 17% for boys and 10.4% for girls. Reading impairment was 3.6%, writing impairment was 6.9%, and mathematic impairment was 6.5%. We determined that consanguineous marriages, low income, history of neonatal jaundice were found as risks for SLD; born by caesarean, developmental delay of walking, and history of neonatal jaundice were found as risks for mathematic impairment. A history of learning difficulties of parents was a risk factor for forming SLD and subtypes. Conclusion Our findings were consistent with other study results about the prevalence of SLD. The relationships between the probable prevalence rates and sociodemographic data were discussed. PMID:29321709

Effect of Phonotactic Probability and Neighborhood Density on Word-Learning Configuration by Preschoolers with Typical Development and Specific Language Impairment

ERIC Educational Resources Information Center

Gray, Shelley; Pittman, Andrea; Weinhold, Juliet

2014-01-01

Purpose: In this study, the authors assessed the effects of phonotactic probability and neighborhood density on word-learning configuration by preschoolers with specific language impairment (SLI) and typical language development (TD). Method: One hundred thirty-one children participated: 48 with SLI, 44 with TD matched on age and gender, and 39…

The Effect of Phonotactic Probability and Neighbourhood Density on Pseudoword Learning in 6- and 7-Year-Old Children

ERIC Educational Resources Information Center

van der Kleij, Sanne W.; Rispens, Judith E.; Scheper, Annette R.

2016-01-01

The aim of this study was to examine the influence of phonotactic probability (PP) and neighbourhood density (ND) on pseudoword learning in 17 Dutch-speaking typically developing children (mean age 7;2). They were familiarized with 16 one-syllable pseudowords varying in PP (high vs low) and ND (high vs low) via a storytelling procedure. The…

Dopamine neurons learn relative chosen value from probabilistic rewards

PubMed Central

Lak, Armin; Stauffer, William R; Schultz, Wolfram

2016-01-01

Economic theories posit reward probability as one of the factors defining reward value. Individuals learn the value of cues that predict probabilistic rewards from experienced reward frequencies. Building on the notion that responses of dopamine neurons increase with reward probability and expected value, we asked how dopamine neurons in monkeys acquire this value signal that may represent an economic decision variable. We found in a Pavlovian learning task that reward probability-dependent value signals arose from experienced reward frequencies. We then assessed neuronal response acquisition during choices among probabilistic rewards. Here, dopamine responses became sensitive to the value of both chosen and unchosen options. Both experiments showed also the novelty responses of dopamine neurones that decreased as learning advanced. These results show that dopamine neurons acquire predictive value signals from the frequency of experienced rewards. This flexible and fast signal reflects a specific decision variable and could update neuronal decision mechanisms. DOI: http://dx.doi.org/10.7554/eLife.18044.001 PMID:27787196

Time-course variation of statistics embedded in music: Corpus study on implicit learning and knowledge.

PubMed

Daikoku, Tatsuya

2018-01-01

Learning and knowledge of transitional probability in sequences like music, called statistical learning and knowledge, are considered implicit processes that occur without intention to learn and awareness of what one knows. This implicit statistical knowledge can be alternatively expressed via abstract medium such as musical melody, which suggests this knowledge is reflected in melodies written by a composer. This study investigates how statistics in music vary over a composer's lifetime. Transitional probabilities of highest-pitch sequences in Ludwig van Beethoven's Piano Sonata were calculated based on different hierarchical Markov models. Each interval pattern was ordered based on the sonata opus number. The transitional probabilities of sequential patterns that are musical universal in music gradually decreased, suggesting that time-course variations of statistics in music reflect time-course variations of a composer's statistical knowledge. This study sheds new light on novel methodologies that may be able to evaluate the time-course variation of composer's implicit knowledge using musical scores.

Prostate Cancer Probability Prediction By Machine Learning Technique.

PubMed

Jović, Srđan; Miljković, Milica; Ivanović, Miljan; Šaranović, Milena; Arsić, Milena

2017-11-26

The main goal of the study was to explore possibility of prostate cancer prediction by machine learning techniques. In order to improve the survival probability of the prostate cancer patients it is essential to make suitable prediction models of the prostate cancer. If one make relevant prediction of the prostate cancer it is easy to create suitable treatment based on the prediction results. Machine learning techniques are the most common techniques for the creation of the predictive models. Therefore in this study several machine techniques were applied and compared. The obtained results were analyzed and discussed. It was concluded that the machine learning techniques could be used for the relevant prediction of prostate cancer.

Unders and Overs: Using a Dice Game to Illustrate Basic Probability Concepts

ERIC Educational Resources Information Center

McPherson, Sandra Hanson

2015-01-01

In this paper, the dice game "Unders and Overs" is described and presented as an active learning exercise to introduce basic probability concepts. The implementation of the exercise is outlined and the resulting presentation of various probability concepts are described.

Learning in a Changing Environment

ERIC Educational Resources Information Center

Speekenbrink, Maarten; Shanks, David R.

2010-01-01

Multiple cue probability learning studies have typically focused on stationary environments. We present 3 experiments investigating learning in changing environments. A fine-grained analysis of the learning dynamics shows that participants were responsive to both abrupt and gradual changes in cue-outcome relations. We found no evidence that…

Quantum reinforcement learning.

PubMed

Dong, Daoyi; Chen, Chunlin; Li, Hanxiong; Tarn, Tzyh-Jong

2008-10-01

The key approaches for machine learning, particularly learning in unknown probabilistic environments, are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of a value-updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL. The state (action) set can be represented with a quantum superposition state, and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement. The probability of the eigen action is determined by the probability amplitude, which is updated in parallel according to rewards. Some related characteristics of QRL such as convergence, optimality, and balancing between exploration and exploitation are also analyzed, which shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speedup learning through the quantum parallelism. To evaluate the performance and practicability of QRL, several simulated experiments are given, and the results demonstrate the effectiveness and superiority of the QRL algorithm for some complex problems. This paper is also an effective exploration on the application of quantum computation to artificial intelligence.

Learning predictive statistics from temporal sequences: Dynamics and strategies

PubMed Central

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E.; Kourtzi, Zoe

2017-01-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics—that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments. PMID:28973111

Learning predictive statistics from temporal sequences: Dynamics and strategies.

PubMed

Wang, Rui; Shen, Yuan; Tino, Peter; Welchman, Andrew E; Kourtzi, Zoe

2017-10-01

Human behavior is guided by our expectations about the future. Often, we make predictions by monitoring how event sequences unfold, even though such sequences may appear incomprehensible. Event structures in the natural environment typically vary in complexity, from simple repetition to complex probabilistic combinations. How do we learn these structures? Here we investigate the dynamics of structure learning by tracking human responses to temporal sequences that change in structure unbeknownst to the participants. Participants were asked to predict the upcoming item following a probabilistic sequence of symbols. Using a Markov process, we created a family of sequences, from simple frequency statistics (e.g., some symbols are more probable than others) to context-based statistics (e.g., symbol probability is contingent on preceding symbols). We demonstrate the dynamics with which individuals adapt to changes in the environment's statistics-that is, they extract the behaviorally relevant structures to make predictions about upcoming events. Further, we show that this structure learning relates to individual decision strategy; faster learning of complex structures relates to selection of the most probable outcome in a given context (maximizing) rather than matching of the exact sequence statistics. Our findings provide evidence for alternate routes to learning of behaviorally relevant statistics that facilitate our ability to predict future events in variable environments.

Hold it! The influence of lingering rewards on choice diversification and persistence.

PubMed

Schulze, Christin; van Ravenzwaaij, Don; Newell, Ben R

2017-11-01

Learning to choose adaptively when faced with uncertain and variable outcomes is a central challenge for decision makers. This study examines repeated choice in dynamic probability learning tasks in which outcome probabilities changed either as a function of the choices participants made or independently of those choices. This presence/absence of sequential choice-outcome dependencies was implemented by manipulating a single task aspect between conditions: the retention/withdrawal of reward across individual choice trials. The study addresses how people adapt to these learning environments and to what extent they engage in 2 choice strategies often contrasted as paradigmatic examples of striking violation of versus nominal adherence to rational choice: diversification and persistent probability maximizing, respectively. Results show that decisions approached adaptive choice diversification and persistence when sufficient feedback was provided on the dynamic rules of the probabilistic environments. The findings of divergent behavior in the 2 environments indicate that diversified choices represented a response to the reward retention manipulation rather than to the mere variability of outcome probabilities. Choice in both environments was well accounted for by the generalized matching law, and computational modeling-based strategy analyses indicated that adaptive choice arose mainly from reliance on reinforcement learning strategies. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Deep learning of support vector machines with class probability output networks.

PubMed

Kim, Sangwook; Yu, Zhibin; Kil, Rhee Man; Lee, Minho

2015-04-01

Deep learning methods endeavor to learn features automatically at multiple levels and allow systems to learn complex functions mapping from the input space to the output space for the given data. The ability to learn powerful features automatically is increasingly important as the volume of data and range of applications of machine learning methods continues to grow. This paper proposes a new deep architecture that uses support vector machines (SVMs) with class probability output networks (CPONs) to provide better generalization power for pattern classification problems. As a result, deep features are extracted without additional feature engineering steps, using multiple layers of the SVM classifiers with CPONs. The proposed structure closely approaches the ideal Bayes classifier as the number of layers increases. Using a simulation of classification problems, the effectiveness of the proposed method is demonstrated. Copyright © 2014 Elsevier Ltd. All rights reserved.

Predicting Robust Learning with the Visual Form of the Moment-by-Moment Learning Curve

ERIC Educational Resources Information Center

Baker, Ryan S.; Hershkovitz, Arnon; Rossi, Lisa M.; Goldstein, Adam B.; Gowda, Sujith M.

2013-01-01

We present a new method for analyzing a student's learning over time for a specific skill: analysis of the graph of the student's moment-by-moment learning over time. Moment-by-moment learning is calculated using a data-mined model that assesses the probability that a student learned a skill or concept at a specific time during learning (Baker,…

Task specificity of attention training: the case of probability cuing

PubMed Central

Jiang, Yuhong V.; Swallow, Khena M.; Won, Bo-Yeong; Cistera, Julia D.; Rosenbaum, Gail M.

2014-01-01

Statistical regularities in our environment enhance perception and modulate the allocation of spatial attention. Surprisingly little is known about how learning-induced changes in spatial attention transfer across tasks. In this study, we investigated whether a spatial attentional bias learned in one task transfers to another. Most of the experiments began with a training phase in which a search target was more likely to be located in one quadrant of the screen than in the other quadrants. An attentional bias toward the high-probability quadrant developed during training (probability cuing). In a subsequent, testing phase, the target's location distribution became random. In addition, the training and testing phases were based on different tasks. Probability cuing did not transfer between visual search and a foraging-like task. However, it did transfer between various types of visual search tasks that differed in stimuli and difficulty. These data suggest that different visual search tasks share a common and transferrable learned attentional bias. However, this bias is not shared by high-level, decision-making tasks such as foraging. PMID:25113853

Learning Problem-Solving Rules as Search Through a Hypothesis Space.

PubMed

Lee, Hee Seung; Betts, Shawn; Anderson, John R

2016-07-01

Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem property such as computational difficulty of the rules biased the search process and so affected learning. Experiment 2 examined the impact of examples as instructional tools and found that their effectiveness was determined by whether they uniquely pointed to the correct rule. Experiment 3 compared verbal directions with examples and found that both could guide search. The final experiment tried to improve learning by using more explicit verbal directions or by adding scaffolding to the example. While both manipulations improved learning, learning still took the form of a search through a hypothesis space of possible rules. We describe a model that embodies two assumptions: (1) the instruction can bias the rules participants hypothesize rather than directly be encoded into a rule; (2) participants do not have memory for past wrong hypotheses and are likely to retry them. These assumptions are realized in a Markov model that fits all the data by estimating two sets of probabilities. First, the learning condition induced one set of Start probabilities of trying various rules. Second, should this first hypothesis prove wrong, the learning condition induced a second set of Choice probabilities of considering various rules. These findings broaden our understanding of effective instruction and provide implications for instructional design. Copyright © 2015 Cognitive Science Society, Inc.

Using Rasch Analysis to Explore What Students Learn about Probability Concepts

ERIC Educational Resources Information Center

Mahmud, Zamalia; Porter, Anne

2015-01-01

Students' understanding of probability concepts have been investigated from various different perspectives. This study was set out to investigate perceived understanding of probability concepts of forty-four students from the STAT131 Understanding Uncertainty and Variation course at the University of Wollongong, NSW. Rasch measurement which is…

Active Learning? Not with My Syllabus!

ERIC Educational Resources Information Center

Ernst, Michael D.

2012-01-01

We describe an approach to teaching probability that minimizes the amount of class time spent on the topic while also providing a meaningful (dice-rolling) activity to get students engaged. The activity, which has a surprising outcome, illustrates the basic ideas of informal probability and how probability is used in statistical inference.…

Visualizing and Understanding Probability and Statistics: Graphical Simulations Using Excel

ERIC Educational Resources Information Center

Gordon, Sheldon P.; Gordon, Florence S.

2009-01-01

The authors describe a collection of dynamic interactive simulations for teaching and learning most of the important ideas and techniques of introductory statistics and probability. The modules cover such topics as randomness, simulations of probability experiments such as coin flipping, dice rolling and general binomial experiments, a simulation…

A "Virtual Spin" on the Teaching of Probability

ERIC Educational Resources Information Center

Beck, Shari A.; Huse, Vanessa E.

2007-01-01

This article, which describes integrating virtual manipulatives with the teaching of probability at the elementary level, puts a "virtual spin" on the teaching of probability to provide more opportunities for students to experience successful learning. The traditional use of concrete manipulatives is enhanced with virtual coins and spinners from…

Towards an Emergent View of Learning Work

ERIC Educational Resources Information Center

Johnsson, Mary C.; Boud, David

2010-01-01

The purpose of this paper is to challenge models of workplace learning that seek to isolate or manipulate a limited set of features to increase the probability of learning. Such models typically attribute learning (or its absence) to individual engagement, manager expectations or organizational affordances and are therefore at least implicitly…

Neural dynamics of reward probability coding: a Magnetoencephalographic study in humans

PubMed Central

Thomas, Julie; Vanni-Mercier, Giovanna; Dreher, Jean-Claude

2013-01-01

Prediction of future rewards and discrepancy between actual and expected outcomes (prediction error) are crucial signals for adaptive behavior. In humans, a number of fMRI studies demonstrated that reward probability modulates these two signals in a large brain network. Yet, the spatio-temporal dynamics underlying the neural coding of reward probability remains unknown. Here, using magnetoencephalography, we investigated the neural dynamics of prediction and reward prediction error computations while subjects learned to associate cues of slot machines with monetary rewards with different probabilities. We showed that event-related magnetic fields (ERFs) arising from the visual cortex coded the expected reward value 155 ms after the cue, demonstrating that reward value signals emerge early in the visual stream. Moreover, a prediction error was reflected in ERF peaking 300 ms after the rewarded outcome and showing decreasing amplitude with higher reward probability. This prediction error signal was generated in a network including the anterior and posterior cingulate cortex. These findings pinpoint the spatio-temporal characteristics underlying reward probability coding. Together, our results provide insights into the neural dynamics underlying the ability to learn probabilistic stimuli-reward contingencies. PMID:24302894

Changing viewer perspectives reveals constraints to implicit visual statistical learning.

PubMed

Jiang, Yuhong V; Swallow, Khena M

2014-10-07

Statistical learning-learning environmental regularities to guide behavior-likely plays an important role in natural human behavior. One potential use is in search for valuable items. Because visual statistical learning can be acquired quickly and without intention or awareness, it could optimize search and thereby conserve energy. For this to be true, however, visual statistical learning needs to be viewpoint invariant, facilitating search even when people walk around. To test whether implicit visual statistical learning of spatial information is viewpoint independent, we asked participants to perform a visual search task from variable locations around a monitor placed flat on a stand. Unbeknownst to participants, the target was more often in some locations than others. In contrast to previous research on stationary observers, visual statistical learning failed to produce a search advantage for targets in high-probable regions that were stable within the environment but variable relative to the viewer. This failure was observed even when conditions for spatial updating were optimized. However, learning was successful when the rich locations were referenced relative to the viewer. We conclude that changing viewer perspective disrupts implicit learning of the target's location probability. This form of learning shows limited integration with spatial updating or spatiotopic representations. © 2014 ARVO.

Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets.

PubMed

Gruber, Susan; Logan, Roger W; Jarrín, Inmaculada; Monge, Susana; Hernán, Miguel A

2015-01-15

Inverse probability weights used to fit marginal structural models are typically estimated using logistic regression. However, a data-adaptive procedure may be able to better exploit information available in measured covariates. By combining predictions from multiple algorithms, ensemble learning offers an alternative to logistic regression modeling to further reduce bias in estimated marginal structural model parameters. We describe the application of two ensemble learning approaches to estimating stabilized weights: super learning (SL), an ensemble machine learning approach that relies on V-fold cross validation, and an ensemble learner (EL) that creates a single partition of the data into training and validation sets. Longitudinal data from two multicenter cohort studies in Spain (CoRIS and CoRIS-MD) were analyzed to estimate the mortality hazard ratio for initiation versus no initiation of combined antiretroviral therapy among HIV positive subjects. Both ensemble approaches produced hazard ratio estimates further away from the null, and with tighter confidence intervals, than logistic regression modeling. Computation time for EL was less than half that of SL. We conclude that ensemble learning using a library of diverse candidate algorithms offers an alternative to parametric modeling of inverse probability weights when fitting marginal structural models. With large datasets, EL provides a rich search over the solution space in less time than SL with comparable results. Copyright © 2014 John Wiley & Sons, Ltd.

Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets

PubMed Central

Gruber, Susan; Logan, Roger W.; Jarrín, Inmaculada; Monge, Susana; Hernán, Miguel A.

2014-01-01

Inverse probability weights used to fit marginal structural models are typically estimated using logistic regression. However a data-adaptive procedure may be able to better exploit information available in measured covariates. By combining predictions from multiple algorithms, ensemble learning offers an alternative to logistic regression modeling to further reduce bias in estimated marginal structural model parameters. We describe the application of two ensemble learning approaches to estimating stabilized weights: super learning (SL), an ensemble machine learning approach that relies on V -fold cross validation, and an ensemble learner (EL) that creates a single partition of the data into training and validation sets. Longitudinal data from two multicenter cohort studies in Spain (CoRIS and CoRIS-MD) were analyzed to estimate the mortality hazard ratio for initiation versus no initiation of combined antiretroviral therapy among HIV positive subjects. Both ensemble approaches produced hazard ratio estimates further away from the null, and with tighter confidence intervals, than logistic regression modeling. Computation time for EL was less than half that of SL. We conclude that ensemble learning using a library of diverse candidate algorithms offers an alternative to parametric modeling of inverse probability weights when fitting marginal structural models. With large datasets, EL provides a rich search over the solution space in less time than SL with comparable results. PMID:25316152

The first cycle of the reflective pedagogical paradigm implementation in the introduction probability theory course

NASA Astrophysics Data System (ADS)

Julie, Hongki

2017-08-01

One of purposes of this study was describing the steps of the teaching and learning process if the teacher in the Introduction Probability Theory course wanted to teach about the event probability by using the reflective pedagogical paradigm (RPP) and describing the results achieved by the students. The study consisted of three cycles, but the results would be presented in this paper was limited to the results obtained in the first cycle. Stages conducted by the researcher in the first cycle could be divided into five stages, namely (1) to know the students' context, (2) to plan and provide student learning experiences, (3) to facilitate students in actions, (4) to ask students to make a reflection and (5) to evaluate. The type of research used in this research was descriptive qualitative and quantitative research. The students' learning experience, the students' action, and the students' reflection would be described qualitatively. The student evaluation results would be described quantitatively. The research subject in this study was 38 students taking the introduction probability theory course in class C. From the students' reflection, still quite a lot of students were not complete in writing concepts that they have learned and / or have not been precise in describing the relationships between concepts that they have learned. From the students' evaluation, 85.29% students got score under 7. If examined more deeply, the most difficulty of students were in the mathematical horizontal process. As a result, they had difficulty in performing the mathematical vertical process.

Technology-enhanced Interactive Teaching of Marginal, Joint and Conditional Probabilities: The Special Case of Bivariate Normal Distribution

PubMed Central

Dinov, Ivo D.; Kamino, Scott; Bhakhrani, Bilal; Christou, Nicolas

2014-01-01

Summary Data analysis requires subtle probability reasoning to answer questions like What is the chance of event A occurring, given that event B was observed? This generic question arises in discussions of many intriguing scientific questions such as What is the probability that an adolescent weighs between 120 and 140 pounds given that they are of average height? and What is the probability of (monetary) inflation exceeding 4% and housing price index below 110? To address such problems, learning some applied, theoretical or cross-disciplinary probability concepts is necessary. Teaching such courses can be improved by utilizing modern information technology resources. Students’ understanding of multivariate distributions, conditional probabilities, correlation and causation can be significantly strengthened by employing interactive web-based science educational resources. Independent of the type of a probability course (e.g. majors, minors or service probability course, rigorous measure-theoretic, applied or statistics course) student motivation, learning experiences and knowledge retention may be enhanced by blending modern technological tools within the classical conceptual pedagogical models. We have designed, implemented and disseminated a portable open-source web-application for teaching multivariate distributions, marginal, joint and conditional probabilities using the special case of bivariate Normal distribution. A real adolescent height and weight dataset is used to demonstrate the classroom utilization of the new web-application to address problems of parameter estimation, univariate and multivariate inference. PMID:25419016

Technology-enhanced Interactive Teaching of Marginal, Joint and Conditional Probabilities: The Special Case of Bivariate Normal Distribution.

PubMed

Dinov, Ivo D; Kamino, Scott; Bhakhrani, Bilal; Christou, Nicolas

2013-01-01

Data analysis requires subtle probability reasoning to answer questions like What is the chance of event A occurring, given that event B was observed? This generic question arises in discussions of many intriguing scientific questions such as What is the probability that an adolescent weighs between 120 and 140 pounds given that they are of average height? and What is the probability of (monetary) inflation exceeding 4% and housing price index below 110? To address such problems, learning some applied, theoretical or cross-disciplinary probability concepts is necessary. Teaching such courses can be improved by utilizing modern information technology resources. Students' understanding of multivariate distributions, conditional probabilities, correlation and causation can be significantly strengthened by employing interactive web-based science educational resources. Independent of the type of a probability course (e.g. majors, minors or service probability course, rigorous measure-theoretic, applied or statistics course) student motivation, learning experiences and knowledge retention may be enhanced by blending modern technological tools within the classical conceptual pedagogical models. We have designed, implemented and disseminated a portable open-source web-application for teaching multivariate distributions, marginal, joint and conditional probabilities using the special case of bivariate Normal distribution. A real adolescent height and weight dataset is used to demonstrate the classroom utilization of the new web-application to address problems of parameter estimation, univariate and multivariate inference.

Individual Values, Learning Routines and Academic Procrastination

ERIC Educational Resources Information Center

Dietz, Franziska; Hofer, Manfred; Fries, Stefan

2007-01-01

Background: Academic procrastination, the tendency to postpone learning activities, is regarded as a consequence of postmodern values that are prominent in post-industrialized societies. When students strive for leisure goals and have no structured routines for academic tasks, delaying strenuous learning activities becomes probable. Aims: The…

A Semantic Web-Based Authoring Tool to Facilitate the Planning of Collaborative Learning Scenarios Compliant with Learning Theories

ERIC Educational Resources Information Center

Isotani, Seiji; Mizoguchi, Riichiro; Isotani, Sadao; Capeli, Olimpio M.; Isotani, Naoko; de Albuquerque, Antonio R. P. L.; Bittencourt, Ig. I.; Jaques, Patricia

2013-01-01

When the goal of group activities is to support long-term learning, the task of designing well-thought-out collaborative learning (CL) scenarios is an important key to success. To help students adequately acquire and develop their knowledge and skills, a teacher can plan a scenario that increases the probability for learning to occur. Such a…

Computational Modeling of Statistical Learning: Effects of Transitional Probability versus Frequency and Links to Word Learning

ERIC Educational Resources Information Center

Mirman, Daniel; Estes, Katharine Graf; Magnuson, James S.

2010-01-01

Statistical learning mechanisms play an important role in theories of language acquisition and processing. Recurrent neural network models have provided important insights into how these mechanisms might operate. We examined whether such networks capture two key findings in human statistical learning. In Simulation 1, a simple recurrent network…

The Relationship between Socio-Economic Status, General Language Learning Outcome, and Beliefs about Language Learning

ERIC Educational Resources Information Center

Ariani, Mohsen Ghasemi; Ghafournia, Narjes

2016-01-01

The objective of this study is to explore the probable relationship between Iranian students' socioeconomic status, general language learning outcome, and their beliefs about language learning. To this end, 350 postgraduate students, doing English for specific courses at Islamic Azad University of Neyshabur participated in this study. They were…

Risk estimation using probability machines

PubMed Central

2014-01-01

Background Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. Results We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. Conclusions The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a “risk machine”, will share properties from the statistical machine that it is derived from. PMID:24581306

Risk estimation using probability machines.

PubMed

Dasgupta, Abhijit; Szymczak, Silke; Moore, Jason H; Bailey-Wilson, Joan E; Malley, James D

2014-03-01

Logistic regression has been the de facto, and often the only, model used in the description and analysis of relationships between a binary outcome and observed features. It is widely used to obtain the conditional probabilities of the outcome given predictors, as well as predictor effect size estimates using conditional odds ratios. We show how statistical learning machines for binary outcomes, provably consistent for the nonparametric regression problem, can be used to provide both consistent conditional probability estimation and conditional effect size estimates. Effect size estimates from learning machines leverage our understanding of counterfactual arguments central to the interpretation of such estimates. We show that, if the data generating model is logistic, we can recover accurate probability predictions and effect size estimates with nearly the same efficiency as a correct logistic model, both for main effects and interactions. We also propose a method using learning machines to scan for possible interaction effects quickly and efficiently. Simulations using random forest probability machines are presented. The models we propose make no assumptions about the data structure, and capture the patterns in the data by just specifying the predictors involved and not any particular model structure. So they do not run the same risks of model mis-specification and the resultant estimation biases as a logistic model. This methodology, which we call a "risk machine", will share properties from the statistical machine that it is derived from.

Last-position elimination-based learning automata.

PubMed

Zhang, Junqi; Wang, Cheng; Zhou, MengChu

2014-12-01

An update scheme of the state probability vector of actions is critical for learning automata (LA). The most popular is the pursuit scheme that pursues the estimated optimal action and penalizes others. This paper proposes a reverse philosophy that leads to last-position elimination-based learning automata (LELA). The action graded last in terms of the estimated performance is penalized by decreasing its state probability and is eliminated when its state probability becomes zero. All active actions, that is, actions with nonzero state probability, equally share the penalized state probability from the last-position action at each iteration. The proposed LELA is characterized by the relaxed convergence condition for the optimal action, the accelerated step size of the state probability update scheme for the estimated optimal action, and the enriched sampling for the estimated nonoptimal actions. The proof of the ϵ-optimal property for the proposed algorithm is presented. Last-position elimination is a widespread philosophy in the real world and has proved to be also helpful for the update scheme of the learning automaton via the simulations of well-known benchmark environments. In the simulations, two versions of the LELA, using different selection strategies of the last action, are compared with the classical pursuit algorithms Discretized Pursuit Reward-Inaction (DP(RI)) and Discretized Generalized Pursuit Algorithm (DGPA). Simulation results show that the proposed schemes achieve significantly faster convergence and higher accuracy than the classical ones. Specifically, the proposed schemes reduce the interval to find the best parameter for a specific environment in the classical pursuit algorithms. Thus, they can have their parameter tuning easier to perform and can save much more time when applied to a practical case. Furthermore, the convergence curves and the corresponding variance coefficient curves of the contenders are illustrated to characterize their essential differences and verify the analysis results of the proposed algorithms.

Situated Learning in Young Romanian Roma Successful Learning Biographies

ERIC Educational Resources Information Center

Nistor, Nicolae; Stanciu, Dorin; Vanea, Cornelia; Sasu, Virginia Maria; Dragota, Maria

2014-01-01

European Roma are often associated with social problems and conflicts due to poverty and low formal education. Nevertheless, Roma communities traditionally develop expertise in ethnically specific domains, probably by alternative, informal ways, such as situated learning in communities of practice. Although predictable, empirical evidence of…

Modeling Women's Menstrual Cycles using PICI Gates in Bayesian Network.

PubMed

Zagorecki, Adam; Łupińska-Dubicka, Anna; Voortman, Mark; Druzdzel, Marek J

2016-03-01

A major difficulty in building Bayesian network (BN) models is the size of conditional probability tables, which grow exponentially in the number of parents. One way of dealing with this problem is through parametric conditional probability distributions that usually require only a number of parameters that is linear in the number of parents. In this paper, we introduce a new class of parametric models, the Probabilistic Independence of Causal Influences (PICI) models, that aim at lowering the number of parameters required to specify local probability distributions, but are still capable of efficiently modeling a variety of interactions. A subset of PICI models is decomposable and this leads to significantly faster inference as compared to models that cannot be decomposed. We present an application of the proposed method to learning dynamic BNs for modeling a woman's menstrual cycle. We show that PICI models are especially useful for parameter learning from small data sets and lead to higher parameter accuracy than when learning CPTs.

Learning to avoid spiders: fear predicts performance, not competence.

PubMed

Luo, Xijia; Becker, Eni S; Rinck, Mike

2018-01-05

We used an immersive virtual environment to examine avoidance learning in spider-fearful participants. In 3 experiments, participants were asked to repeatedly lift one of 3 virtual boxes, under which either a toy car or a spider appeared and then approached the participant. Participants were not told that the probability of encountering a spider differed across boxes. When the difference was large (Exps. 1 and 2), spider-fearfuls learned to avoid spiders by lifting the few-spiders-box more often and the many-spiders-box less often than non-fearful controls did. However, they hardly managed to do so when the probability differences were small (Exp. 3), and they did not escape from threat more quickly (Exp. 2). In contrast to the observed performance differences, spider-fearfuls and non-fearfuls showed equal competence, that is comparable post-experimental knowledge about the probability to encounter spiders under the 3 boxes. The limitations and implications of the present study are discussed.

Hypergame theory applied to cyber attack and defense

NASA Astrophysics Data System (ADS)

House, James Thomas; Cybenko, George

2010-04-01

This work concerns cyber attack and defense in the context of game theory--specifically hypergame theory. Hypergame theory extends classical game theory with the ability to deal with differences in players' expertise, differences in their understanding of game rules, misperceptions, and so forth. Each of these different sub-scenarios, or subgames, is associated with a probability--representing the likelihood that the given subgame is truly "in play" at a given moment. In order to form an optimal attack or defense policy, these probabilities must be learned if they're not known a-priori. We present hidden Markov model and maximum entropy approaches for accurately learning these probabilities through multiple iterations of both normal and modified game play. We also give a widely-applicable approach for the analysis of cases where an opponent is aware that he is being studied, and intentionally plays to spoil the process of learning and thereby obfuscate his attributes. These are considered in the context of a generic, abstract cyber attack example. We demonstrate that machine learning efficacy can be heavily dependent on the goals and styles of participant behavior. To this end detailed simulation results under various combinations of attacker and defender behaviors are presented and analyzed.

Explorations in Statistics: Power

ERIC Educational Resources Information Center

Curran-Everett, Douglas

2010-01-01

Learning about statistics is a lot like learning about science: the learning is more meaningful if you can actively explore. This fifth installment of "Explorations in Statistics" revisits power, a concept fundamental to the test of a null hypothesis. Power is the probability that we reject the null hypothesis when it is false. Four…

New Zealand and Queensland Teachers' Conceptions of Learning: Transforming More than Reproducing

ERIC Educational Resources Information Center

Brown, Gavin T. L.; Lake, Robert; Matters, Gabrielle

2008-01-01

Background: Two major conceptions of learning exist: reproducing new material and transforming material to make meaning. Teachers' understandings of what learning is probably influence their teaching practices and student academic performance. Aims: To validate a short scale derived from Tait, Entwistle, & McCune's (1998) ASSIST inventory and…

Beyond Nonutilization: Irrelevant Cues Can Gate Learning in Probabilistic Categorization

ERIC Educational Resources Information Center

Little, Daniel R.; Lewandowsky, Stephan

2009-01-01

In probabilistic categorization, also known as multiple cue probability learning (MCPL), people learn to predict a discrete outcome on the basis of imperfectly valid cues. In MCPL, normatively irrelevant cues are usually ignored, which stands in apparent conflict with recent research in deterministic categorization that has shown that people…

Impact of Learning Organization Culture on Performance in Higher Education Institutions

ERIC Educational Resources Information Center

Ponnuswamy, Indra; Manohar, Hansa Lysander

2016-01-01

In this paper, an adapted version of the Dimensions of Learning Organization Questionnaire (DLOQ) was employed to investigate the perception of academic staff on learning organization culture in Indian higher education institutions. The questionnaire was sent to 700 faculty members of different universities using a non-probability purposive…

What Do We Learn from Binding Features? Evidence for Multilevel Feature Integration

ERIC Educational Resources Information Center

Colzato, Lorenza S.; Raffone, Antonino; Hommel, Bernhard

2006-01-01

Four experiments were conducted to investigate the relationship between the binding of visual features (as measured by their after-effects on subsequent binding) and the learning of feature-conjunction probabilities. Both binding and learning effects were obtained, but they did not interact. Interestingly, (shape-color) binding effects…

Test-Potentiated Learning: Distinguishing Between Direct and Indirect Effects of Tests

PubMed Central

Arnold, Kathleen M.; McDermott, Kathleen B.

2013-01-01

The facilitative effect of retrieval practice, or testing, on the probability of later retrieval has been the focus of much recent empirical research. A lesser-known benefit of retrieval practice is that it may also enhance the ability of a learner to benefit from a subsequent restudy opportunity. This facilitative effect of retrieval practice on subsequent encoding is known as test-potentiated learning. Thus far, however, the literature has not isolated the indirect effect of retrieval practice on subsequent memory (via enhancing the effectiveness of restudy) from the direct effects of retrieval on subsequent memory. The experiment presented here uses conditional probability to disentangle test-potentiated learning from the direct effects of retrieval practice. The results indicate that unsuccessful retrieval attempts enhance the effectiveness of subsequent restudy, demonstrating that tests do potentiate subsequent learning. PMID:22774852

A Model-Free Machine Learning Method for Risk Classification and Survival Probability Prediction.

PubMed

Geng, Yuan; Lu, Wenbin; Zhang, Hao Helen

2014-01-01

Risk classification and survival probability prediction are two major goals in survival data analysis since they play an important role in patients' risk stratification, long-term diagnosis, and treatment selection. In this article, we propose a new model-free machine learning framework for risk classification and survival probability prediction based on weighted support vector machines. The new procedure does not require any specific parametric or semiparametric model assumption on data, and is therefore capable of capturing nonlinear covariate effects. We use numerous simulation examples to demonstrate finite sample performance of the proposed method under various settings. Applications to a glioma tumor data and a breast cancer gene expression survival data are shown to illustrate the new methodology in real data analysis.

Fast adaptation of the internal model of gravity for manual interceptions: evidence for event-dependent learning.

PubMed

Zago, Myrka; Bosco, Gianfranco; Maffei, Vincenzo; Iosa, Marco; Ivanenko, Yuri P; Lacquaniti, Francesco

2005-02-01

We studied how subjects learn to deal with two conflicting sensory environments as a function of the probability of each environment and the temporal distance between repeated events. Subjects were asked to intercept a visual target moving downward on a screen with randomized laws of motion. We compared five protocols that differed in the probability of constant speed (0g) targets and accelerated (1g) targets. Probability ranged from 9 to 100%, and the time interval between consecutive repetitions of the same target ranged from about 1 to 20 min. We found that subjects systematically timed their responses consistent with the assumption of gravity effects, for both 1 and 0g trials. With training, subjects rapidly adapted to 0g targets by shifting the time of motor activation. Surprisingly, the adaptation rate was independent of both the probability of 0g targets and their temporal distance. Very few 0g trials sporadically interspersed as catch trials during immersive practice with 1g trials were sufficient for learning and consolidation in long-term memory, as verified by retesting after 24 h. We argue that the memory store for adapted states of the internal gravity model is triggered by individual events and can be sustained for prolonged periods of time separating sporadic repetitions. This form of event-related learning could depend on multiple-stage memory, with exponential rise and decay in the initial stages followed by a sample-and-hold module.

Electrophysiological responses to feedback during the application of abstract rules.

PubMed

Walsh, Matthew M; Anderson, John R

2013-11-01

Much research focuses on how people acquire concrete stimulus-response associations from experience; however, few neuroscientific studies have examined how people learn about and select among abstract rules. To address this issue, we recorded ERPs as participants performed an abstract rule-learning task. In each trial, they viewed a sample number and two test numbers. Participants then chose a test number using one of three abstract mathematical rules they freely selected from: greater than the sample number, less than the sample number, or equal to the sample number. No one rule was always rewarded, but some rules were rewarded more frequently than others. To maximize their earnings, participants needed to learn which rules were rewarded most frequently. All participants learned to select the best rules for repeating and novel stimulus sets that obeyed the overall reward probabilities. Participants differed, however, in the extent to which they overgeneralized those rules to repeating stimulus sets that deviated from the overall reward probabilities. The feedback-related negativity (FRN), an ERP component thought to reflect reward prediction error, paralleled behavior. The FRN was sensitive to item-specific reward probabilities in participants who detected the deviant stimulus set, and the FRN was sensitive to overall reward probabilities in participants who did not. These results show that the FRN is sensitive to the utility of abstract rules and that the individual's representation of a task's states and actions shapes behavior as well as the FRN.

Electrophysiological Responses to Feedback during the Application of Abstract Rules

PubMed Central

Walsh, Matthew M.; Anderson, John R.

2017-01-01

Much research focuses on how people acquire concrete stimulus–response associations from experience; however, few neuroscientific studies have examined how people learn about and select among abstract rules. To address this issue, we recorded ERPs as participants performed an abstract rule-learning task. In each trial, they viewed a sample number and two test numbers. Participants then chose a test number using one of three abstract mathematical rules they freely selected from: greater than the sample number, less than the sample number, or equal to the sample number. No one rule was always rewarded, but some rules were rewarded more frequently than others. To maximize their earnings, participants needed to learn which rules were rewarded most frequently. All participants learned to select the best rules for repeating and novel stimulus sets that obeyed the overall reward probabilities. Participants differed, however, in the extent to which they overgeneralized those rules to repeating stimulus sets that deviated from the overall reward probabilities. The feedback-related negativity (FRN), an ERP component thought to reflect reward prediction error, paralleled behavior. The FRN was sensitive to item-specific reward probabilities in participants who detected the deviant stimulus set, and the FRN was sensitive to overall reward probabilities in participants who did not. These results show that the FRN is sensitive to the utility of abstract rules and that the individualʼs representation of a taskʼs states and actions shapes behavior as well as the FRN. PMID:23915052

MRI Brain Tumor Segmentation and Necrosis Detection Using Adaptive Sobolev Snakes.

PubMed

Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen

2014-03-21

Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at different points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D diffusion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.

MRI brain tumor segmentation and necrosis detection using adaptive Sobolev snakes

NASA Astrophysics Data System (ADS)

Nakhmani, Arie; Kikinis, Ron; Tannenbaum, Allen

2014-03-01

Brain tumor segmentation in brain MRI volumes is used in neurosurgical planning and illness staging. It is important to explore the tumor shape and necrosis regions at di erent points of time to evaluate the disease progression. We propose an algorithm for semi-automatic tumor segmentation and necrosis detection. Our algorithm consists of three parts: conversion of MRI volume to a probability space based on the on-line learned model, tumor probability density estimation, and adaptive segmentation in the probability space. We use manually selected acceptance and rejection classes on a single MRI slice to learn the background and foreground statistical models. Then, we propagate this model to all MRI slices to compute the most probable regions of the tumor. Anisotropic 3D di usion is used to estimate the probability density. Finally, the estimated density is segmented by the Sobolev active contour (snake) algorithm to select smoothed regions of the maximum tumor probability. The segmentation approach is robust to noise and not very sensitive to the manual initialization in the volumes tested. Also, it is appropriate for low contrast imagery. The irregular necrosis regions are detected by using the outliers of the probability distribution inside the segmented region. The necrosis regions of small width are removed due to a high probability of noisy measurements. The MRI volume segmentation results obtained by our algorithm are very similar to expert manual segmentation.

Infants' statistical learning: 2- and 5-month-olds' segmentation of continuous visual sequences.

PubMed

Slone, Lauren Krogh; Johnson, Scott P

2015-05-01

Past research suggests that infants have powerful statistical learning abilities; however, studies of infants' visual statistical learning offer differing accounts of the developmental trajectory of and constraints on this learning. To elucidate this issue, the current study tested the hypothesis that young infants' segmentation of visual sequences depends on redundant statistical cues to segmentation. A sample of 20 2-month-olds and 20 5-month-olds observed a continuous sequence of looming shapes in which unit boundaries were defined by both transitional probability and co-occurrence frequency. Following habituation, only 5-month-olds showed evidence of statistically segmenting the sequence, looking longer to a statistically improbable shape pair than to a probable pair. These results reaffirm the power of statistical learning in infants as young as 5 months but also suggest considerable development of statistical segmentation ability between 2 and 5 months of age. Moreover, the results do not support the idea that infants' ability to segment visual sequences based on transitional probabilities and/or co-occurrence frequencies is functional at the onset of visual experience, as has been suggested previously. Rather, this type of statistical segmentation appears to be constrained by the developmental state of the learner. Factors contributing to the development of statistical segmentation ability during early infancy, including memory and attention, are discussed. Copyright © 2015 Elsevier Inc. All rights reserved.

Supervised learning of probability distributions by neural networks

NASA Technical Reports Server (NTRS)

Baum, Eric B.; Wilczek, Frank

1988-01-01

Supervised learning algorithms for feedforward neural networks are investigated analytically. The back-propagation algorithm described by Werbos (1974), Parker (1985), and Rumelhart et al. (1986) is generalized by redefining the values of the input and output neurons as probabilities. The synaptic weights are then varied to follow gradients in the logarithm of likelihood rather than in the error. This modification is shown to provide a more rigorous theoretical basis for the algorithm and to permit more accurate predictions. A typical application involving a medical-diagnosis expert system is discussed.

Toward User Interfaces and Data Visualization Criteria for Learning Design of Digital Textbooks

ERIC Educational Resources Information Center

Railean, Elena

2014-01-01

User interface and data visualisation criteria are central issues in digital textbooks design. However, when applying mathematical modelling of learning process to the analysis of the possible solutions, it could be observed that results differ. Mathematical learning views cognition in on the base on statistics and probability theory, graph…

Guidance of Spatial Attention by Incidental Learning and Endogenous Cuing

ERIC Educational Resources Information Center

Jiang, Yuhong V.; Swallow, Khena M.; Rosenbaum, Gail M.

2013-01-01

Our visual system is highly sensitive to regularities in the environment. Locations that were important in one's previous experience are often prioritized during search, even though observers may not be aware of the learning. In this study we characterized the guidance of spatial attention by incidental learning of a target's spatial probability,…

Improving History Learning through Cultural Heritage, Local History and Technology

ERIC Educational Resources Information Center

Magro, Graça; de Carvalho, Joaquim Ramos; Marcelino, Maria José

2014-01-01

History learning is many times considered dull and demotivating by young students. Probably this is due because the learning process is disconnected from these students' reality and experience. One possible way to overcome this state of matters is to use technology like mobile devices with georeferencing software and local history and heritage…

Language experience changes subsequent learning

PubMed Central

Onnis, Luca; Thiessen, Erik

2013-01-01

What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual non-linguistic (nonsense shapes), and auditory non-linguistic (pure tones). The forward and backward probabilities between adjacent elements generated two equally probable and orthogonal perceptual parses of the elements, such that any significant preference at test must be due to either general cognitive biases, or prior language-induced biases. We found that language modulated parsing preferences with the linguistic stimuli only. Intriguingly, these preferences are congruent with the dominant word order patterns of each language, as corroborated by corpus analyses, and are driven by probabilistic preferences. Furthermore, although the Korean individuals had received extensive formal explicit training in English and lived in an English-speaking environment, they exhibited statistical learning biases congruent with their native language. Our findings suggest that mechanisms of statistical sequential learning are implicated in language across the lifespan, and experience with language may affect cognitive processes and later learning. PMID:23200510

A Generalized Mechanism for Perception of Pitch Patterns

PubMed Central

Loui, Psyche; Wu, Elaine H.; Wessel, David L.; Knight, Robert T.

2009-01-01

Surviving in a complex and changeable environment relies upon the ability to extract probable recurring patterns. Here we report a neurophysiological mechanism for rapid probabilistic learning of a new system of music. Participants listened to different combinations of tones from a previously-unheard system of pitches based on the Bohlen-Pierce scale, with chord progressions that form 3:1 ratios in frequency, notably different from 2:1 frequency ratios in existing musical systems. Event-related brain potentials elicited by improbable sounds in the new music system showed emergence over a one-hour period of physiological signatures known to index sound expectation in standard Western music. These indices of expectation learning were eliminated when sound patterns were played equiprobably, and co-varied with individual behavioral differences in learning. These results demonstrate that humans utilize a generalized probability-based perceptual learning mechanism to process novel sound patterns in music. PMID:19144845

Learning Probabilistic Logic Models from Probabilistic Examples

PubMed Central

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2009-01-01

Abstract We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples. PMID:19888348

Learning Probabilistic Logic Models from Probabilistic Examples.

PubMed

Chen, Jianzhong; Muggleton, Stephen; Santos, José

2008-10-01

We revisit an application developed originally using abductive Inductive Logic Programming (ILP) for modeling inhibition in metabolic networks. The example data was derived from studies of the effects of toxins on rats using Nuclear Magnetic Resonance (NMR) time-trace analysis of their biofluids together with background knowledge representing a subset of the Kyoto Encyclopedia of Genes and Genomes (KEGG). We now apply two Probabilistic ILP (PILP) approaches - abductive Stochastic Logic Programs (SLPs) and PRogramming In Statistical modeling (PRISM) to the application. Both approaches support abductive learning and probability predictions. Abductive SLPs are a PILP framework that provides possible worlds semantics to SLPs through abduction. Instead of learning logic models from non-probabilistic examples as done in ILP, the PILP approach applied in this paper is based on a general technique for introducing probability labels within a standard scientific experimental setting involving control and treated data. Our results demonstrate that the PILP approach provides a way of learning probabilistic logic models from probabilistic examples, and the PILP models learned from probabilistic examples lead to a significant decrease in error accompanied by improved insight from the learned results compared with the PILP models learned from non-probabilistic examples.

Understanding Probabilistic Interpretations of Physical Systems: A Prerequisite to Learning Quantum Physics.

ERIC Educational Resources Information Center

Bao, Lei; Redish, Edward F.

2002-01-01

Explains the critical role of probability in making sense of quantum physics and addresses the difficulties science and engineering undergraduates experience in helping students build a model of how to think about probability in physical systems. (Contains 17 references.) (Author/YDS)

Proactivity and Reinforcement: The Contingency of Social Behavior

ERIC Educational Resources Information Center

Williams, J. Sherwood; And Others

1976-01-01

This paper analyzes development of group structure in terms of the stimulus-sampling perspective. Learning is the continual sampling of possibilities, with those reinforced possibilities increasing in probability of occurance. This contingency learning approach is tested experimentally. (NG)

Sparse Learning with Stochastic Composite Optimization.

PubMed

Zhang, Weizhong; Zhang, Lijun; Jin, Zhongming; Jin, Rong; Cai, Deng; Li, Xuelong; Liang, Ronghua; He, Xiaofei

2017-06-01

In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate O(1/λT), but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain O(√{log(1/δ)/T}) with δ is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately O(log(log(T)/δ)/λT).

Play to learn, teach by play.

PubMed

Palagi, Elisabetta; Stanyon, Roscoe; Demuru, Elisa

2015-01-01

The synthesis provided by Kline in the target article is noteworthy, but ignores the inseparable role of play in the evolution of learning and teaching in both humans and other animals. Play is distinguished and advantaged by its positive feedback reinforcement through pleasure. Play, especially between adults and infants, is probably the platform from which human learning and teaching evolved.

Learning about Probability from Text and Tables: Do Color Coding and Labeling through an Interactive-User Interface Help?

ERIC Educational Resources Information Center

Clinton, Virginia; Morsanyi, Kinga; Alibali, Martha W.; Nathan, Mitchell J.

2016-01-01

Learning from visual representations is enhanced when learners appropriately integrate corresponding visual and verbal information. This study examined the effects of two methods of promoting integration, color coding and labeling, on learning about probabilistic reasoning from a table and text. Undergraduate students (N = 98) were randomly…

Crib Work--An Evaluation of a Problem-Based Learning Experiment: Preliminary Results

ERIC Educational Resources Information Center

Walsh, Vonda K.; Bush, H. Francis

2013-01-01

Problem-based learning has been proven to be successful in both medical colleges and physics classes, but not uniformly across all disciplines. A college course in probability and statistics was used as a setting to test the effectiveness of problem-based learning when applied to homework. This paper compares the performances of the students from…

A fast elitism Gaussian estimation of distribution algorithm and application for PID optimization.

PubMed

Xu, Qingyang; Zhang, Chengjin; Zhang, Li

2014-01-01

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA.

A Fast Elitism Gaussian Estimation of Distribution Algorithm and Application for PID Optimization

PubMed Central

Xu, Qingyang; Zhang, Chengjin; Zhang, Li

2014-01-01

Estimation of distribution algorithm (EDA) is an intelligent optimization algorithm based on the probability statistics theory. A fast elitism Gaussian estimation of distribution algorithm (FEGEDA) is proposed in this paper. The Gaussian probability model is used to model the solution distribution. The parameters of Gaussian come from the statistical information of the best individuals by fast learning rule. A fast learning rule is used to enhance the efficiency of the algorithm, and an elitism strategy is used to maintain the convergent performance. The performances of the algorithm are examined based upon several benchmarks. In the simulations, a one-dimensional benchmark is used to visualize the optimization process and probability model learning process during the evolution, and several two-dimensional and higher dimensional benchmarks are used to testify the performance of FEGEDA. The experimental results indicate the capability of FEGEDA, especially in the higher dimensional problems, and the FEGEDA exhibits a better performance than some other algorithms and EDAs. Finally, FEGEDA is used in PID controller optimization of PMSM and compared with the classical-PID and GA. PMID:24892059

A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

PubMed Central

Ye, Qing; Pan, Hao; Liu, Changhua

2015-01-01

This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F 1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach. PMID:25722717

A Dicey Strategy To Get Your M&Ms.

ERIC Educational Resources Information Center

Nisbet, Steven; Jones, Graham; Langrall, Cynthia; Thornton, Carol

2000-01-01

Describes and analyzes a learning episode in which two children in year 4 interact with each other and their teacher while playing a probability game involving chocolate M&Ms. Children developed key ideas in probability from a game that was designed to produce cognitive conflict. (ASK)

A Vehicle for Bivariate Data Analysis

ERIC Educational Resources Information Center

Roscoe, Matt B.

2016-01-01

Instead of reserving the study of probability and statistics for special fourth-year high school courses, the Common Core State Standards for Mathematics (CCSSM) takes a "statistics for all" approach. The standards recommend that students in grades 6-8 learn to summarize and describe data distributions, understand probability, draw…

Chance and Probability: What Do They Mean to University Engineering Students?

ERIC Educational Resources Information Center

Barragues, J. I.; Guisasola, J.; Morais, A.

2006-01-01

The great interest aroused by the incorporation of Statistics and Probability into curricular projects has been accompanied by considerable evidence of significant difficulties in the meaningful learning and application of the concepts. These difficulties have been the subject of many studies, mostly concerning secondary school students. This…

Learning in Reverse: Eight-Month-Old Infants Track Backward Transitional Probabilities

ERIC Educational Resources Information Center

Pelucchi, Bruna; Hay, Jessica F.; Saffran, Jenny R.

2009-01-01

Numerous recent studies suggest that human learners, including both infants and adults, readily track sequential statistics computed between adjacent elements. One such statistic, transitional probability, is typically calculated as the likelihood that one element predicts another. However, little is known about whether listeners are sensitive to…

Probability differently modulating the effects of reward and punishment on visuomotor adaptation.

PubMed

Song, Yanlong; Smiley-Oyen, Ann L

2017-12-01

Recent human motor learning studies revealed that punishment seemingly accelerated motor learning but reward enhanced consolidation of motor memory. It is not evident how intrinsic properties of reward and punishment modulate the potentially dissociable effects of reward and punishment on motor learning and motor memory. It is also not clear what causes the dissociation of the effects of reward and punishment. By manipulating probability of distribution, a critical property of reward and punishment, the present study demonstrated that probability had distinct modulation on the effects of reward and punishment in adapting to a sudden visual rotation and consolidation of the adaptation memory. Specifically, two probabilities of monetary reward and punishment distribution, 50 and 100%, were applied during young adult participants adapting to a sudden visual rotation. Punishment and reward showed distinct effects on motor adaptation and motor memory. The group that received punishments in 100% of the adaptation trials adapted significantly faster than the other three groups, but the group that received rewards in 100% of the adaptation trials showed marked savings in re-adapting to the same rotation. In addition, the group that received punishments in 50% of the adaptation trials that were randomly selected also had savings in re-adapting to the same rotation. Sensitivity to sensory prediction error or difference in explicit process induced by reward and punishment may likely contribute to the distinct effects of reward and punishment.

"Probably True" Says the Expert: How Two Types of Lexical Hedges Influence Students' Evaluation of Scientificness

ERIC Educational Resources Information Center

Thiebach, Monja; Mayweg-Paus, Elisabeth; Jucks, Regina

2015-01-01

Contemporary school learning typically includes the processing of popular scientific information as found in journals, magazines, and/or the WWW. The German high school curriculum emphasizes that students should have achieved science literacy and have learned to evaluate the substance of text-based learning content by the end of high school.…

Language experience changes subsequent learning.

PubMed

Onnis, Luca; Thiessen, Erik

2013-02-01

What are the effects of experience on subsequent learning? We explored the effects of language-specific word order knowledge on the acquisition of sequential conditional information. Korean and English adults were engaged in a sequence learning task involving three different sets of stimuli: auditory linguistic (nonsense syllables), visual non-linguistic (nonsense shapes), and auditory non-linguistic (pure tones). The forward and backward probabilities between adjacent elements generated two equally probable and orthogonal perceptual parses of the elements, such that any significant preference at test must be due to either general cognitive biases, or prior language-induced biases. We found that language modulated parsing preferences with the linguistic stimuli only. Intriguingly, these preferences are congruent with the dominant word order patterns of each language, as corroborated by corpus analyses, and are driven by probabilistic preferences. Furthermore, although the Korean individuals had received extensive formal explicit training in English and lived in an English-speaking environment, they exhibited statistical learning biases congruent with their native language. Our findings suggest that mechanisms of statistical sequential learning are implicated in language across the lifespan, and experience with language may affect cognitive processes and later learning. Copyright © 2012 Elsevier B.V. All rights reserved.

The nature of the language input affects brain activation during learning from a natural language

PubMed Central

Plante, Elena; Patterson, Dianne; Gómez, Rebecca; Almryde, Kyle R.; White, Milo G.; Asbjørnsen, Arve E.

2015-01-01

Artificial language studies have demonstrated that learners are able to segment individual word-like units from running speech using the transitional probability information. However, this skill has rarely been examined in the context of natural languages, where stimulus parameters can be quite different. In this study, two groups of English-speaking learners were exposed to Norwegian sentences over the course of three fMRI scans. One group was provided with input in which transitional probabilities predicted the presence of target words in the sentences. This group quickly learned to identify the target words and fMRI data revealed an extensive and highly dynamic learning network. These results were markedly different from activation seen for a second group of participants. This group was provided with highly similar input that was modified so that word learning based on syllable co-occurrences was not possible. These participants showed a much more restricted network. The results demonstrate that the nature of the input strongly influenced the nature of the network that learners employ to learn the properties of words in a natural language. PMID:26257471

Discriminative Bayesian Dictionary Learning for Classification.

PubMed

Akhtar, Naveed; Shafait, Faisal; Mian, Ajmal

2016-12-01

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a finite approximation of Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.

Sex and boldness explain individual differences in spatial learning in a lizard.

PubMed

Carazo, Pau; Noble, Daniel W A; Chandrasoma, Dani; Whiting, Martin J

2014-05-07

Understanding individual differences in cognitive performance is a major challenge to animal behaviour and cognition studies. We used the Eastern water skink (Eulamprus quoyii) to examine associations between exploration, boldness and individual variability in spatial learning, a dimension of lizard cognition with important bearing on fitness. We show that males perform better than females in a biologically relevant spatial learning task. This is the first evidence for sex differences in learning in a reptile, and we argue that it is probably owing to sex-specific selective pressures that may be widespread in lizards. Across the sexes, we found a clear association between boldness after a simulated predatory attack and the probability of learning the spatial task. In contrast to previous studies, we found a nonlinear association between boldness and learning: both 'bold' and 'shy' behavioural types were more successful learners than intermediate males. Our results do not fit with recent predictions suggesting that individual differences in learning may be linked with behavioural types via high-low-risk/reward trade-offs. We suggest the possibility that differences in spatial cognitive performance may arise in lizards as a consequence of the distinct environmental variability and complexity experienced by individuals as a result of their sex and social tactics.

Intentionality and Wisdom in Language, Information, and Technology

ERIC Educational Resources Information Center

Lin, Lin; Ross, Haj; O'Connor, Brian; Spector, J. Michael

2015-01-01

An interdisciplinary approach from linguistics, information sciences, learning sciences, and educational technology is used to explore the concept of information. Several key issues are highlighted, including: (1) learning language through meaning or probability; (2) the situational difference between message and meaning; (3) relationship between…

From Movements to Actions: Two Mechanisms for Learning Action Sequences

ERIC Educational Resources Information Center

Endress, Ansgar D.; Wood, Justin N.

2011-01-01

When other individuals move, we interpret their movements as discrete, hierarchically-organized, goal-directed actions. However, the mechanisms that integrate visible movement features into actions are poorly understood. Here, we consider two sequence learning mechanisms--transitional probability-based (TP) and position-based encoding…

How well do elderly people cope with uncertainty in a learning task?

PubMed

Chasseigne, G; Grau, S; Mullet, E; Cama, V

1999-11-01

The relation between age, task complexity and learning performance in a Multiple Cue Probability Learning task was studied by systematically varying the level of uncertainty present in the task, keeping constant the direction of relationships. Four age groups were constituted: young adults (mean age = 21), middle-aged adults (45), elderly people (69) and very elderly people (81). Five uncertainty levels were considered: predictability = 0.96, 0.80, 0.64, 0.48, and 0.32. All relationships involved were direct ones. A strong effect of uncertainty on 'control', a measure of the subject's consistency with respect to a linear model, was found. This effect was essentially a linear one. To each decrement in predictability of the task corresponded an equal decrement in participants' level of control. This level of decrement was the same, regardless of the age of the participant. It can be concluded that elderly people cope with uncertainty in probability learning tasks as well as young adults.

Assessing the effectiveness of a hybrid-flipped model of learning on fluid mechanics instruction: overall course performance, homework, and far- and near-transfer of learning

NASA Astrophysics Data System (ADS)

Harrison, David J.; Saito, Laurel; Markee, Nancy; Herzog, Serge

2017-11-01

To examine the impact of a hybrid-flipped model utilising active learning techniques, the researchers inverted one section of an undergraduate fluid mechanics course, reduced seat time, and engaged in active learning sessions in the classroom. We compared this model to the traditional section on four performance measures. We employed a propensity score method entailing a two-stage regression analysis that considered eight covariates to address the potential bias of treatment selection. First, we estimated the probability score based on the eight covariates, and second, we used the inverse of the probability score as a regression weight on the performance of learners who did not select into the hybrid course. Results suggest that enrolment in the hybrid-flipped section had a marginally significant negative impact on the total course score and a significant negative impact on homework performance, possibly because of poor video usage by the hybrid-flipped learners. Suggested considerations are also discussed.

Impact of statistical learning methods on the predictive power of multivariate normal tissue complication probability models.

PubMed

Xu, Cheng-Jian; van der Schaaf, Arjen; Schilstra, Cornelis; Langendijk, Johannes A; van't Veld, Aart A

2012-03-15

To study the impact of different statistical learning methods on the prediction performance of multivariate normal tissue complication probability (NTCP) models. In this study, three learning methods, stepwise selection, least absolute shrinkage and selection operator (LASSO), and Bayesian model averaging (BMA), were used to build NTCP models of xerostomia following radiotherapy treatment for head and neck cancer. Performance of each learning method was evaluated by a repeated cross-validation scheme in order to obtain a fair comparison among methods. It was found that the LASSO and BMA methods produced models with significantly better predictive power than that of the stepwise selection method. Furthermore, the LASSO method yields an easily interpretable model as the stepwise method does, in contrast to the less intuitive BMA method. The commonly used stepwise selection method, which is simple to execute, may be insufficient for NTCP modeling. The LASSO method is recommended. Copyright © 2012 Elsevier Inc. All rights reserved.

Nonparametric Density Estimation Based on Self-Organizing Incremental Neural Network for Large Noisy Data.

PubMed

Nakamura, Yoshihiro; Hasegawa, Osamu

2017-01-01

With the ongoing development and expansion of communication networks and sensors, massive amounts of data are continuously generated in real time from real environments. Beforehand, prediction of a distribution underlying such data is difficult; furthermore, the data include substantial amounts of noise. These factors make it difficult to estimate probability densities. To handle these issues and massive amounts of data, we propose a nonparametric density estimator that rapidly learns data online and has high robustness. Our approach is an extension of both kernel density estimation (KDE) and a self-organizing incremental neural network (SOINN); therefore, we call our approach KDESOINN. An SOINN provides a clustering method that learns about the given data as networks of prototype of data; more specifically, an SOINN can learn the distribution underlying the given data. Using this information, KDESOINN estimates the probability density function. The results of our experiments show that KDESOINN outperforms or achieves performance comparable to the current state-of-the-art approaches in terms of robustness, learning time, and accuracy.

Teaching Probability with the Support of the R Statistical Software

ERIC Educational Resources Information Center

dos Santos Ferreira, Robson; Kataoka, Verônica Yumi; Karrer, Monica

2014-01-01

The objective of this paper is to discuss aspects of high school students' learning of probability in a context where they are supported by the statistical software R. We report on the application of a teaching experiment, constructed using the perspective of Gal's probabilistic literacy and Papert's constructionism. The results show improvement…

Probability & Perception: The Representativeness Heuristic in Action

ERIC Educational Resources Information Center

Lu, Yun; Vasko, Francis J.; Drummond, Trevor J.; Vasko, Lisa E.

2014-01-01

If the prospective students of probability lack a background in mathematical proofs, hands-on classroom activities may work well to help them to learn to analyze problems correctly. For example, students may physically roll a die twice to count and compare the frequency of the sequences. Tools such as graphing calculators or Microsoft Excel®…

Just-in-Time Support

ERIC Educational Resources Information Center

Rollins, Suzy Pepper

2016-01-01

Most students have gaps in their background knowledge and basic skills-gaps that can stand in the way of learning new concepts. For example, a student may be excited about studying probability--until he realizes that today's lesson on probability will require him to use fractions. As his brain searches frantically for his dim recollection of the…

Probability & Statistics: Modular Learning Exercises. Student Edition

ERIC Educational Resources Information Center

Actuarial Foundation, 2012

2012-01-01

The purpose of these modules is to provide an introduction to the world of probability and statistics to accelerated mathematics students at the high school level. The materials are centered on the fictional town of Happy Shores, a coastal community which is at risk for hurricanes. Actuaries at an insurance company figure out the risks and…

The Independent Effects of Phonotactic Probability and Neighbourhood Density on Lexical Acquisition by Preschool Children

ERIC Educational Resources Information Center

Storkel, Holly L.; Lee, Su-Yeon

2011-01-01

The goal of this research was to disentangle effects of phonotactic probability, the likelihood of occurrence of a sound sequence, and neighbourhood density, the number of phonologically similar words, in lexical acquisition. Two-word learning experiments were conducted with 4-year-old children. Experiment 1 manipulated phonotactic probability…

A Modified Moore Approach to Teaching Mathematical Statistics: An Inquiry Based Learning Technique to Teaching Mathematical Statistics

ERIC Educational Resources Information Center

McLoughlin, M. Padraig M. M.

2008-01-01

The author of this paper submits the thesis that learning requires doing; only through inquiry is learning achieved, and hence this paper proposes a programme of use of a modified Moore method in a Probability and Mathematical Statistics (PAMS) course sequence to teach students PAMS. Furthermore, the author of this paper opines that set theory…

LEAP: biomarker inference through learning and evaluating association patterns.

PubMed

Jiang, Xia; Neapolitan, Richard E

2015-03-01

Single nucleotide polymorphism (SNP) high-dimensional datasets are available from Genome Wide Association Studies (GWAS). Such data provide researchers opportunities to investigate the complex genetic basis of diseases. Much of genetic risk might be due to undiscovered epistatic interactions, which are interactions in which combination of several genes affect disease. Research aimed at discovering interacting SNPs from GWAS datasets proceeded in two directions. First, tools were developed to evaluate candidate interactions. Second, algorithms were developed to search over the space of candidate interactions. Another problem when learning interacting SNPs, which has not received much attention, is evaluating how likely it is that the learned SNPs are associated with the disease. A complete system should provide this information as well. We develop such a system. Our system, called LEAP, includes a new heuristic search algorithm for learning interacting SNPs, and a Bayesian network based algorithm for computing the probability of their association. We evaluated the performance of LEAP using 100 1,000-SNP simulated datasets, each of which contains 15 SNPs involved in interactions. When learning interacting SNPs from these datasets, LEAP outperformed seven others methods. Furthermore, only SNPs involved in interactions were found to be probable. We also used LEAP to analyze real Alzheimer's disease and breast cancer GWAS datasets. We obtained interesting and new results from the Alzheimer's dataset, but limited results from the breast cancer dataset. We conclude that our results support that LEAP is a useful tool for extracting candidate interacting SNPs from high-dimensional datasets and determining their probability. © 2015 The Authors. *Genetic Epidemiology published by Wiley Periodicals, Inc.

Linking sounds to meanings: infant statistical learning in a natural language.

PubMed

Hay, Jessica F; Pelucchi, Bruna; Graf Estes, Katharine; Saffran, Jenny R

2011-09-01

The processes of infant word segmentation and infant word learning have largely been studied separately. However, the ease with which potential word forms are segmented from fluent speech seems likely to influence subsequent mappings between words and their referents. To explore this process, we tested the link between the statistical coherence of sequences presented in fluent speech and infants' subsequent use of those sequences as labels for novel objects. Notably, the materials were drawn from a natural language unfamiliar to the infants (Italian). The results of three experiments suggest that there is a close relationship between the statistics of the speech stream and subsequent mapping of labels to referents. Mapping was facilitated when the labels contained high transitional probabilities in the forward and/or backward direction (Experiment 1). When no transitional probability information was available (Experiment 2), or when the internal transitional probabilities of the labels were low in both directions (Experiment 3), infants failed to link the labels to their referents. Word learning appears to be strongly influenced by infants' prior experience with the distribution of sounds that make up words in natural languages. Copyright © 2011 Elsevier Inc. All rights reserved.

Autonomous learning derived from experimental modeling of physical laws.

PubMed

Grabec, Igor

2013-05-01

This article deals with experimental description of physical laws by probability density function of measured data. The Gaussian mixture model specified by representative data and related probabilities is utilized for this purpose. The information cost function of the model is described in terms of information entropy by the sum of the estimation error and redundancy. A new method is proposed for searching the minimum of the cost function. The number of the resulting prototype data depends on the accuracy of measurement. Their adaptation resembles a self-organized, highly non-linear cooperation between neurons in an artificial NN. A prototype datum corresponds to the memorized content, while the related probability corresponds to the excitability of the neuron. The method does not include any free parameters except objectively determined accuracy of the measurement system and is therefore convenient for autonomous execution. Since representative data are generally less numerous than the measured ones, the method is applicable for a rather general and objective compression of overwhelming experimental data in automatic data-acquisition systems. Such compression is demonstrated on analytically determined random noise and measured traffic flow data. The flow over a day is described by a vector of 24 components. The set of 365 vectors measured over one year is compressed by autonomous learning to just 4 representative vectors and related probabilities. These vectors represent the flow in normal working days and weekends or holidays, while the related probabilities correspond to relative frequencies of these days. This example reveals that autonomous learning yields a new basis for interpretation of representative data and the optimal model structure. Copyright © 2012 Elsevier Ltd. All rights reserved.

Robust location and spread measures for nonparametric probability density function estimation.

PubMed

López-Rubio, Ezequiel

2009-10-01

Robustness against outliers is a desirable property of any unsupervised learning scheme. In particular, probability density estimators benefit from incorporating this feature. A possible strategy to achieve this goal is to substitute the sample mean and the sample covariance matrix by more robust location and spread estimators. Here we use the L1-median to develop a nonparametric probability density function (PDF) estimator. We prove its most relevant properties, and we show its performance in density estimation and classification applications.

Adaptive Learning and Risk Taking

ERIC Educational Resources Information Center

Denrell, Jerker

2007-01-01

Humans and animals learn from experience by reducing the probability of sampling alternatives with poor past outcomes. Using simulations, J. G. March (1996) illustrated how such adaptive sampling could lead to risk-averse as well as risk-seeking behavior. In this article, the author develops a formal theory of how adaptive sampling influences risk…

Ability Level Estimation of Students on Probability Unit via Computerized Adaptive Testing

ERIC Educational Resources Information Center

Özyurt, Hacer; Özyurt, Özcan

2015-01-01

Problem Statement: Learning-teaching activities bring along the need to determine whether they achieve their goals. Thus, multiple choice tests addressing the same set of questions to all are frequently used. However, this traditional assessment and evaluation form contrasts with modern education, where individual learning characteristics are…

Strengthening the Student Toolbox: Study Strategies to Boost Learning

ERIC Educational Resources Information Center

Dunlosky, John

2013-01-01

Before the "big test" did you use the following study strategies: highlighting, rereading, and cramming? As students many of us probably did, yet research shows that while these three strategies are commonly used, they have been ineffective in retaining information. Learning strategies have been discussed in almost every textbook on…

Infants Segment Continuous Events Using Transitional Probabilities

ERIC Educational Resources Information Center

Stahl, Aimee E.; Romberg, Alexa R.; Roseberry, Sarah; Golinkoff, Roberta Michnick; Hirsh-Pasek, Kathryn

2014-01-01

Throughout their 1st year, infants adeptly detect statistical structure in their environment. However, little is known about whether statistical learning is a primary mechanism for event segmentation. This study directly tests whether statistical learning alone is sufficient to segment continuous events. Twenty-eight 7- to 9-month-old infants…

Adaptive Feedback Improving Learningful Conversations at Workplace

ERIC Educational Resources Information Center

Gaeta, Matteo; Mangione, Giuseppina Rita; Miranda, Sergio; Orciuoli, Francesco

2013-01-01

This work proposes the definition of an Adaptive Conversation-based Learning System (ACLS) able to foster computer-mediated tutorial dialogues at the workplace in order to increase the probability to generate meaningful learning during conversations. ACLS provides a virtual assistant selecting the best partner to involve in the conversation and…

Using High-Probability Instructional Sequences and Explicit Instruction to Teach Multiplication Facts

ERIC Educational Resources Information Center

Leach, Debra

2016-01-01

Students with learning disabilities often struggle with math fact fluency and require specialized interventions to recall basic facts. Deficits in math fact fluency can result in later difficulties when learning higher-level mathematical computation, concepts, and problem solving. The response-to-intervention (RTI) and…

Does my high blood pressure improve your survival? Overall and subgroup learning curves in health.

PubMed

Van Gestel, Raf; Müller, Tobias; Bosmans, Johan

2017-09-01

Learning curves in health are of interest for a wide range of medical disciplines, healthcare providers, and policy makers. In this paper, we distinguish between three types of learning when identifying overall learning curves: economies of scale, learning from cumulative experience, and human capital depreciation. In addition, we approach the question of how treating more patients with specific characteristics predicts provider performance. To soften collinearity problems, we explore the use of least absolute shrinkage and selection operator regression as a variable selection method and Theil-Goldberger mixed estimation to augment the available information. We use data from the Belgian Transcatheter Aorta Valve Implantation (TAVI) registry, containing information on the first 860 TAVI procedures in Belgium. We find that treating an additional TAVI patient is associated with an increase in the probability of 2-year survival by about 0.16%-points. For adverse events like renal failure and stroke, we find that an extra day between procedures is associated with an increase in the probability for these events by 0.12%-points and 0.07%-points, respectively. Furthermore, we find evidence for positive learning effects from physicians' experience with defibrillation, treating patients with hypertension, and the use of certain types of replacement valves during the TAVI procedure. Copyright © 2017 John Wiley & Sons, Ltd.

Impaired Statistical Learning in Developmental Dyslexia

PubMed Central

Thiessen, Erik D.; Holt, Lori L.

2015-01-01

Purpose Developmental dyslexia (DD) is commonly thought to arise from phonological impairments. However, an emerging perspective is that a more general procedural learning deficit, not specific to phonological processing, may underlie DD. The current study examined if individuals with DD are capable of extracting statistical regularities across sequences of passively experienced speech and nonspeech sounds. Such statistical learning is believed to be domain-general, to draw upon procedural learning systems, and to relate to language outcomes. Method DD and control groups were familiarized with a continuous stream of syllables or sine-wave tones, the ordering of which was defined by high or low transitional probabilities across adjacent stimulus pairs. Participants subsequently judged two 3-stimulus test items with either high or low statistical coherence as being the most similar to the sounds heard during familiarization. Results As with control participants, the DD group was sensitive to the transitional probability structure of the familiarization materials as evidenced by above-chance performance. However, the performance of participants with DD was significantly poorer than controls across linguistic and nonlinguistic stimuli. In addition, reading-related measures were significantly correlated with statistical learning performance of both speech and nonspeech material. Conclusion Results are discussed in light of procedural learning impairments among participants with DD. PMID:25860795

Comparison of Content Structure and Cognitive Structure in the Learning of Probability.

ERIC Educational Resources Information Center

Geeslin, William E.

Digraphs, graphs, and task analysis were used to map out the content structure of a programed text (SMSG) in elementary probability. Mathematical structure was defined as the relationship between concepts within a set of abstract systems. The word association technique was used to measure the existing relations (cognitive structure) in S's memory…

Promoting Active Learning When Teaching Introductory Statistics and Probability Using a Portfolio Curriculum Approach

ERIC Educational Resources Information Center

Adair, Desmond; Jaeger, Martin; Price, Owen M.

2018-01-01

The use of a portfolio curriculum approach, when teaching a university introductory statistics and probability course to engineering students, is developed and evaluated. The portfolio curriculum approach, so called, as the students need to keep extensive records both as hard copies and digitally of reading materials, interactions with faculty,…

A Computational Model of Word Segmentation from Continuous Speech Using Transitional Probabilities of Atomic Acoustic Events

ERIC Educational Resources Information Center

Rasanen, Okko

2011-01-01

Word segmentation from continuous speech is a difficult task that is faced by human infants when they start to learn their native language. Several studies indicate that infants might use several different cues to solve this problem, including intonation, linguistic stress, and transitional probabilities between subsequent speech sounds. In this…

Family History as an Indicator of Risk for Reading Disability.

ERIC Educational Resources Information Center

Volger, George P.; And Others

1984-01-01

Self-reported reading ability of parents of 174 reading-disabled children and of 182 controls was used to estimate the probability that a child will become reading disabled. Using Bayesian inverse probability analysis, it was found that the risk for reading disability is increased substantially if either parent has had difficulty in learning to…

The Classicist and the Frequentist Approach to Probability within a "TinkerPlots2" Combinatorial Problem

ERIC Educational Resources Information Center

Prodromou, Theodosia

2012-01-01

This article seeks to address a pedagogical theory of introducing the classicist and the frequentist approach to probability, by investigating important elements in 9th grade students' learning process while working with a "TinkerPlots2" combinatorial problem. Results from this research study indicate that, after the students had seen…

Probability & Statistics: Modular Learning Exercises. Teacher Edition

ERIC Educational Resources Information Center

Actuarial Foundation, 2012

2012-01-01

The purpose of these modules is to provide an introduction to the world of probability and statistics to accelerated mathematics students at the high school level. The modules also introduce students to real world math concepts and problems that property and casualty actuaries come across in their work. They are designed to be used by teachers and…

Come on Down ... The Prize Is Right in Your Classroom

ERIC Educational Resources Information Center

Butterworth, William T.; Coe, Paul R.

2004-01-01

"The Price is Right" ("TPIR") is a rich source of examples of applied probability, combinatorics, and game theory. While some of the games played on stage by individual contestants stress a knowledge of pricing, many are also heavily based on probability. "TPIR" stage games are a treasury of interesting modules that can be effective learning tools…

Neurophysiological Markers of Statistical Learning in Music and Language: Hierarchy, Entropy, and Uncertainty.

PubMed

Daikoku, Tatsuya

2018-06-19

Statistical learning (SL) is a method of learning based on the transitional probabilities embedded in sequential phenomena such as music and language. It has been considered an implicit and domain-general mechanism that is innate in the human brain and that functions independently of intention to learn and awareness of what has been learned. SL is an interdisciplinary notion that incorporates information technology, artificial intelligence, musicology, and linguistics, as well as psychology and neuroscience. A body of recent study has suggested that SL can be reflected in neurophysiological responses based on the framework of information theory. This paper reviews a range of work on SL in adults and children that suggests overlapping and independent neural correlations in music and language, and that indicates disability of SL. Furthermore, this article discusses the relationships between the order of transitional probabilities (TPs) (i.e., hierarchy of local statistics) and entropy (i.e., global statistics) regarding SL strategies in human's brains; claims importance of information-theoretical approaches to understand domain-general, higher-order, and global SL covering both real-world music and language; and proposes promising approaches for the application of therapy and pedagogy from various perspectives of psychology, neuroscience, computational studies, musicology, and linguistics.

Unsupervised Learning and Pattern Recognition of Biological Data Structures with Density Functional Theory and Machine Learning.

PubMed

Chen, Chien-Chang; Juan, Hung-Hui; Tsai, Meng-Yuan; Lu, Henry Horng-Shing

2018-01-11

By introducing the methods of machine learning into the density functional theory, we made a detour for the construction of the most probable density function, which can be estimated by learning relevant features from the system of interest. Using the properties of universal functional, the vital core of density functional theory, the most probable cluster numbers and the corresponding cluster boundaries in a studying system can be simultaneously and automatically determined and the plausibility is erected on the Hohenberg-Kohn theorems. For the method validation and pragmatic applications, interdisciplinary problems from physical to biological systems were enumerated. The amalgamation of uncharged atomic clusters validated the unsupervised searching process of the cluster numbers and the corresponding cluster boundaries were exhibited likewise. High accurate clustering results of the Fisher's iris dataset showed the feasibility and the flexibility of the proposed scheme. Brain tumor detections from low-dimensional magnetic resonance imaging datasets and segmentations of high-dimensional neural network imageries in the Brainbow system were also used to inspect the method practicality. The experimental results exhibit the successful connection between the physical theory and the machine learning methods and will benefit the clinical diagnoses.

ENKI - A tool for analysing the learning efficiency

NASA Astrophysics Data System (ADS)

Simona, Dudáková; Boris, Lacsný; Aba, Teleki

2017-01-01

Long-term memory plays a crucial role in learning mechanisms. We start to build up a probability model of learning (ENKI) ten years ago based on findings of micro genetics published in [1]. We accomplished a number of experiments in our department to testify the validity of the model with success. We described ENKI in detail here, giving the general mathematical formula of the learning curve. This paper pointed out that the model ENKI can detect its own strategy of learning in the brain as well as the simulation of the process of learning that will lead to the development of this method using its own strategy.

On the asymptotic improvement of supervised learning by utilizing additional unlabeled samples - Normal mixture density case

NASA Technical Reports Server (NTRS)

Shahshahani, Behzad M.; Landgrebe, David A.

1992-01-01

The effect of additional unlabeled samples in improving the supervised learning process is studied in this paper. Three learning processes. supervised, unsupervised, and combined supervised-unsupervised, are compared by studying the asymptotic behavior of the estimates obtained under each process. Upper and lower bounds on the asymptotic covariance matrices are derived. It is shown that under a normal mixture density assumption for the probability density function of the feature space, the combined supervised-unsupervised learning is always superior to the supervised learning in achieving better estimates. Experimental results are provided to verify the theoretical concepts.

Couple Graph Based Label Propagation Method for Hyperspectral Remote Sensing Data Classification

NASA Astrophysics Data System (ADS)

Wang, X. P.; Hu, Y.; Chen, J.

2018-04-01

Graph based semi-supervised classification method are widely used for hyperspectral image classification. We present a couple graph based label propagation method, which contains both the adjacency graph and the similar graph. We propose to construct the similar graph by using the similar probability, which utilize the label similarity among examples probably. The adjacency graph was utilized by a common manifold learning method, which has effective improve the classification accuracy of hyperspectral data. The experiments indicate that the couple graph Laplacian which unite both the adjacency graph and the similar graph, produce superior classification results than other manifold Learning based graph Laplacian and Sparse representation based graph Laplacian in label propagation framework.

Probabilistic reversal learning is impaired in Parkinson's disease

PubMed Central

Peterson, David A.; Elliott, Christian; Song, David D.; Makeig, Scott; Sejnowski, Terrence J.; Poizner, Howard

2009-01-01

In many everyday settings, the relationship between our choices and their potentially rewarding outcomes is probabilistic and dynamic. In addition, the difficulty of the choices can vary widely. Although a large body of theoretical and empirical evidence suggests that dopamine mediates rewarded learning, the influence of dopamine in probabilistic and dynamic rewarded learning remains unclear. We adapted a probabilistic rewarded learning task originally used to study firing rates of dopamine cells in primate substantia nigra pars compacta (Morris et al. 2006) for use as a reversal learning task with humans. We sought to investigate how the dopamine depletion in Parkinson's disease (PD) affects probabilistic reward learning and adaptation to a reversal in reward contingencies. Over the course of 256 trials subjects learned to choose the more favorable from among pairs of images with small or large differences in reward probabilities. During a subsequent otherwise identical reversal phase, the reward probability contingencies for the stimuli were reversed. Seventeen Parkinson's disease (PD) patients of mild to moderate severity were studied off of their dopaminergic medications and compared to 15 age-matched controls. Compared to controls, PD patients had distinct pre- and post-reversal deficiencies depending upon the difficulty of the choices they had to learn. The patients also exhibited compromised adaptability to the reversal. A computational model of the subjects’ trial-by-trial choices demonstrated that the adaptability was sensitive to the gain with which patients weighted pre-reversal feedback. Collectively, the results implicate the nigral dopaminergic system in learning to make choices in environments with probabilistic and dynamic reward contingencies. PMID:19628022

Uncertainty in action-value estimation affects both action choice and learning rate of the choice behaviors of rats

PubMed Central

Funamizu, Akihiro; Ito, Makoto; Doya, Kenji; Kanzaki, Ryohei; Takahashi, Hirokazu

2012-01-01

The estimation of reward outcomes for action candidates is essential for decision making. In this study, we examined whether and how the uncertainty in reward outcome estimation affects the action choice and learning rate. We designed a choice task in which rats selected either the left-poking or right-poking hole and received a reward of a food pellet stochastically. The reward probabilities of the left and right holes were chosen from six settings (high, 100% vs. 66%; mid, 66% vs. 33%; low, 33% vs. 0% for the left vs. right holes, and the opposites) in every 20–549 trials. We used Bayesian Q-learning models to estimate the time course of the probability distribution of action values and tested if they better explain the behaviors of rats than standard Q-learning models that estimate only the mean of action values. Model comparison by cross-validation revealed that a Bayesian Q-learning model with an asymmetric update for reward and non-reward outcomes fit the choice time course of the rats best. In the action-choice equation of the Bayesian Q-learning model, the estimated coefficient for the variance of action value was positive, meaning that rats were uncertainty seeking. Further analysis of the Bayesian Q-learning model suggested that the uncertainty facilitated the effective learning rate. These results suggest that the rats consider uncertainty in action-value estimation and that they have an uncertainty-seeking action policy and uncertainty-dependent modulation of the effective learning rate. PMID:22487046

Interaction in Asynchronous Web-Based Learning Environments

ERIC Educational Resources Information Center

Woo, Younghee; Reeves, Thomas C.

2008-01-01

Because of the perceived advantages and the promotion of Web-based learning environments (WBLEs) by commercial interests as well as educational technologists, knowing how to develop and implement WBLEs will probably not be a choice, but a necessity for most educators and trainers in the future. However, many instructors still don't understand the…

Aha Malawi! Envisioning Field Experiences That Nurture Cultural Competencies for Preservice Teachers

ERIC Educational Resources Information Center

Talbot, Patricia A.

2011-01-01

This theoretical study uses the context of the writer's personal encounters in Malawi, Africa, to propose a conceptual model for creating diverse field experiences based on best practices in critical pedagogy, service learning, and the underpinnings of transformational learning theory, for the purpose of increasing the probability of meaningful…

Effects of the Application of Graphing Calculator on Students' Probability Achievement

ERIC Educational Resources Information Center

Tan, Choo-Kim

2012-01-01

A Graphing Calculator (GC) is one of the most portable and affordable technology in mathematics education. It quickens the mechanical procedure in solving mathematical problems and creates a highly interactive learning environment, which makes learning a seemingly difficult subject, easy. Since research on the use of GCs for the teaching and…

Learning from Our Students

ERIC Educational Resources Information Center

Noddings, Nel

2004-01-01

Most teachers have been good students. Some students are fast learners and attain the required knowledge and skills easily; others are obedient, hard workers. In either case, teachers are likely to believe that if students really try, they will do well. Listening to students over many years, the author has learned that this is probably not true.…

Learning across Languages: Bilingual Experience Supports Dual Language Statistical Word Segmentation

ERIC Educational Resources Information Center

Antovich, Dylan M.; Graf Estes, Katharine

2018-01-01

Bilingual acquisition presents learning challenges beyond those found in monolingual environments, including the need to segment speech in two languages. Infants may use statistical cues, such as syllable-level transitional probabilities, to segment words from fluent speech. In the present study we assessed monolingual and bilingual 14-month-olds'…

Validating Teacher Performativity through Lifelong School-University Collaboration

ERIC Educational Resources Information Center

Lewis, Theodore

2013-01-01

The main point of this article is that more credence should be given in teacher education to performative dimensions of teaching. I agree with David Carr (1999) that the requisite capabilities are probably best learned in actual schools. I employ Turnbull's (2000) conception of performativity, which speaks of tacit cultural learning. Following…

What Software to Use in the Teaching of Mathematical Subjects?

ERIC Educational Resources Information Center

Berežný, Štefan

2015-01-01

We can consider two basic views, when using mathematical software in the teaching of mathematical subjects. First: How to learn to use specific software for the specific tasks, e. g., software Statistica for the subjects of Applied statistics, probability and mathematical statistics, or financial mathematics. Second: How to learn to use the…

Exploring Dynamical Assessments of Affect, Behavior, and Cognition and Math State Test Achievement

ERIC Educational Resources Information Center

San Pedro, Maria Ofelia Z.; Snow, Erica L.; Baker, Ryan S.; McNamara, Danielle S.; Heffernan, Neil T.

2015-01-01

There is increasing evidence that fine-grained aspects of student performance and interaction within educational software are predictive of long-term learning. Machine learning models have been used to provide assessments of affect, behavior, and cognition based on analyses of system log data, estimating the probability of a student's particular…

Pretrial Hippocampal ?-State Differentiates Single-Unit Response Profiles during Rabbit Trace Eyeblink Conditioning

ERIC Educational Resources Information Center

Cicchese, Joseph J.; Darling, Ryan D.; Berry, Stephen D.

2015-01-01

Eyeblink conditioning given in the explicit presence of hippocampal ? results in accelerated learning and enhanced multiple-unit responses, with slower learning and suppression of unit activity under non-? conditions. Recordings from putative pyramidal cells during ?-contingent training show that pretrial ?-state is linked to the probability of…

Soldier Development Following Negative Cross-Cultural Experiences: An Integrated Review of the Literature

DTIC Science & Technology

2014-03-01

and Practice, 10, 125-143. Bandura , A., (1997). Self-efficacy: Toward a unifying theory of behavioral social change. Psychology Review, 84, 191-21...external stimuli from interactions with people or in particular situations can also promote learning that results in social or procedural outcomes, such...behavior and can reduce the probability of learning from social interaction. Control. Learning from experience as a team depends heavily on the degree

Refining Markov state models for conformational dynamics using ensemble-averaged data and time-series trajectories

NASA Astrophysics Data System (ADS)

Matsunaga, Y.; Sugita, Y.

2018-06-01

A data-driven modeling scheme is proposed for conformational dynamics of biomolecules based on molecular dynamics (MD) simulations and experimental measurements. In this scheme, an initial Markov State Model (MSM) is constructed from MD simulation trajectories, and then, the MSM parameters are refined using experimental measurements through machine learning techniques. The second step can reduce the bias of MD simulation results due to inaccurate force-field parameters. Either time-series trajectories or ensemble-averaged data are available as a training data set in the scheme. Using a coarse-grained model of a dye-labeled polyproline-20, we compare the performance of machine learning estimations from the two types of training data sets. Machine learning from time-series data could provide the equilibrium populations of conformational states as well as their transition probabilities. It estimates hidden conformational states in more robust ways compared to that from ensemble-averaged data although there are limitations in estimating the transition probabilities between minor states. We discuss how to use the machine learning scheme for various experimental measurements including single-molecule time-series trajectories.

Developing a Model and Applications for Probabilities of Student Success: A Case Study of Predictive Analytics

ERIC Educational Resources Information Center

Calvert, Carol Elaine

2014-01-01

This case study relates to distance learning students on open access courses. It demonstrates the use of predictive analytics to generate a model of the probabilities of success and retention at different points, or milestones, in a student journey. A core set of explanatory variables has been established and their varying relative importance at…

Chances Are...Making Probability and Statistics Fun To Learn and Easy To Teach.

ERIC Educational Resources Information Center

Pfenning, Nancy

Probability and statistics may be the horror of many college students, but if these subjects are trimmed to include only the essential symbols, they are easily within the grasp of interested middle school or even elementary school students. This book can serve as an introduction for any beginner, from gifted students who would like to broaden…

Math Academy: Are You Game? Explorations in Probability. Supplemental Math Materials for Grades 3-6

ERIC Educational Resources Information Center

Rimbey, Kimberly

2007-01-01

Created by teachers for teachers, the Math Academy tools and activities included in this booklet were designed to create hands-on activities and a fun learning environment for the teaching of mathematics to the students. This booklet contains the themed program "Are You Game? Math Academy--Explorations in Probability," which teachers can use to…

Prediction of fatty acid-binding residues on protein surfaces with three-dimensional probability distributions of interacting atoms.

PubMed

Mahalingam, Rajasekaran; Peng, Hung-Pin; Yang, An-Suei

2014-08-01

Protein-fatty acid interaction is vital for many cellular processes and understanding this interaction is important for functional annotation as well as drug discovery. In this work, we present a method for predicting the fatty acid (FA)-binding residues by using three-dimensional probability density distributions of interacting atoms of FAs on protein surfaces which are derived from the known protein-FA complex structures. A machine learning algorithm was established to learn the characteristic patterns of the probability density maps specific to the FA-binding sites. The predictor was trained with five-fold cross validation on a non-redundant training set and then evaluated with an independent test set as well as on holo-apo pair's dataset. The results showed good accuracy in predicting the FA-binding residues. Further, the predictor developed in this study is implemented as an online server which is freely accessible at the following website, http://ismblab.genomics.sinica.edu.tw/. Copyright © 2014 Elsevier B.V. All rights reserved.

Tier-Adjacency Is Not a Necessary Condition for Learning Phonotactic Dependencies

ERIC Educational Resources Information Center

Koo, Hahn; Callahan, Lydia

2012-01-01

One hypothesis raised by Newport and Aslin to explain how speakers learn dependencies between nonadjacent phonemes is that speakers track bigram probabilities between two segments that are adjacent to each other within a tier of their own. The hypothesis predicts that a dependency between segments separated from each other at the tier level cannot…

A Model for the Transfer of Perceptual-Motor Skill Learning in Human Behaviors

ERIC Educational Resources Information Center

Rosalie, Simon M.; Muller, Sean

2012-01-01

This paper presents a preliminary model that outlines the mechanisms underlying the transfer of perceptual-motor skill learning in sport and everyday tasks. Perceptual-motor behavior is motivated by performance demands and evolves over time to increase the probability of success through adaptation. Performance demands at the time of an event…

Teaching Naked Techniques: Leveraging Research on Learning to Improve the Effectiveness of Teaching

ERIC Educational Resources Information Center

Bowen, José Antonio; Watson, C. Edward

2017-01-01

Ultimately, the overarching purpose of each faculty member in the classroom is to increase the probability that their students will do the work to learn the material on their own, appreciate new perspectives, and, ideally, make connections between one's course, other courses, and the real world. This article discusses how "Teaching Naked…

Patterns, Probabilities, and People: Making Sense of Quantitative Change in Complex Systems

ERIC Educational Resources Information Center

Wilkerson-Jerde, Michelle Hoda; Wilensky, Uri J.

2015-01-01

The learning sciences community has made significant progress in understanding how people think and learn about complex systems. But less is known about how people make sense of the quantitative patterns and mathematical formalisms often used to study these systems. In this article, we make a case for attending to and supporting connections…

Context Switch Effects on Acquisition and Extinction in Human Predictive Learning

ERIC Educational Resources Information Center

Rosas, Juan M.; Callejas-Aguilera, Jose E.

2006-01-01

Four experiments tested context switch effects on acquisition and extinction in human predictive learning. A context switch impaired probability judgments about a cue-outcome relationship when the cue was trained in a context in which a different cue underwent extinction. The context switch also impaired judgments about a cue trained in a context…

The Law Review Approach: What the Humanities Can Learn

ERIC Educational Resources Information Center

Mendenhall, Allen

2013-01-01

Readers of this journal probably know how the peer review process works in the humanities disciplines and at various journals. Therefore the author explains how the law review process generally works and then what the humanities can learn and borrow from the law review process. He ends by advocating for a hybrid law review/peer review approach to…

Through Efficient Use of LORs: Prospective Teachers' Views on Operational Aspects of Learning Object Repositories

ERIC Educational Resources Information Center

Yalcinalp, Serpil; Emiroglu, Bulent

2012-01-01

Although many developments have been made in the design and development of learning object repositories (LORs), the efficient use of such systems is still questionable. Without realising the functional use of such systems or considering the involvement of their dynamic users, these systems would probably become obsolete. This study includes both…

Perceiving Permutations as Distinct Outcomes: The Accommodation of a Complex Knowledge System

ERIC Educational Resources Information Center

Kapon, Shulamit; Ron, Gila; Hershkowitz, Rina; Dreyfus, Tommy

2015-01-01

There is ample evidence that reasoning about stochastic phenomena is often subject to systematic bias even after instruction. Few studies have examined the detailed learning processes involved in learning probability. This paper examines a case study drawn from a large corpus of data collected as part of a research project that dealt with the…

Impact of Bilingualism on Infants' Ability to Learn from Talking and Nontalking Faces

ERIC Educational Resources Information Center

Fort, Mathilde; Ayneto-Gimeno, Alba; Escrichs, Anira; Sebastian-Galles, Nuria

2018-01-01

To probably overcome the challenge of learning two languages at the same time, infants raised in a bilingual environment pay more attention to the mouth of talking faces than same-age monolinguals. Here we examined the consequences of such preference for monolingual and bilingual infants' ability to perceive nonspeech information coming from the…

Using the van Hiele K-12 Geometry Learning Theory to Modify Engineering Mechanics Instruction

ERIC Educational Resources Information Center

Sharp, Janet M.; Zachary, Loren W.

2004-01-01

Engineering students use spatial thinking when examining diagrams or models to study structure design. It is expected that most engineering students have solidified spatial thinking skills during K-12 schooling. However, according to what we know about geometry learning and teaching, spatial thinking probably needs to be explicitly taught within…

Learning Strategies in Proficient and Less Proficient Readers in Medicine

ERIC Educational Resources Information Center

Nemati, Majid; Nodoushan, Mohammad Ali Salmani; Ashrafzadeh, Anis

2010-01-01

The current study aimed to diagnose the probable significant differences in the use of language learning strategies among medical-text readers of opposite sex from different levels of proficiency. 120 (N = 120) participants were randomly selected from Azad Medical University of Mashhad: 60 medical students (age range 23-25; 30 = male and 30 =…

Learning Strategies in Proficient and Less Proficient Readers in Medicine

ERIC Educational Resources Information Center

Nemati, Majid; Nodoushan, Mohammad Ali Salmani; Ashrafzadeh, Anis

2010-01-01

The current study aimed to diagnose the probable significant differences in the use of language learning strategies among medical-text readers of opposite sex from different levels of proficiency. 120 (N=120) participants were randomly selected from Azad Medical University of Mashhad: 60 medical students (age range 23-25; 30=male and 30=female)…

Evaluation of a Digital Learning Object for the Monty Hall Dilemma

ERIC Educational Resources Information Center

DiBattista, David

2011-01-01

The Monty Hall dilemma (MHD) is a remarkably difficult probability problem with a counterintuitive solution. Undergraduate students used an interactive digital learning object that provided a set-based, animated explanation of the solution to the MHD and let them play games designed to increase understanding of the solution. More than 60% of users…

Tuned by experience: How orientation probability modulates early perceptual processing.

PubMed

Jabar, Syaheed B; Filipowicz, Alex; Anderson, Britt

2017-09-01

Probable stimuli are more often and more quickly detected. While stimulus probability is known to affect decision-making, it can also be explained as a perceptual phenomenon. Using spatial gratings, we have previously shown that probable orientations are also more precisely estimated, even while participants remained naive to the manipulation. We conducted an electrophysiological study to investigate the effect that probability has on perception and visual-evoked potentials. In line with previous studies on oddballs and stimulus prevalence, low-probability orientations were associated with a greater late positive 'P300' component which might be related to either surprise or decision-making. However, the early 'C1' component, thought to reflect V1 processing, was dampened for high-probability orientations while later P1 and N1 components were unaffected. Exploratory analyses revealed a participant-level correlation between C1 and P300 amplitudes, suggesting a link between perceptual processing and decision-making. We discuss how these probability effects could be indicative of sharpening of neurons preferring the probable orientations, due either to perceptual learning, or to feature-based attention. Copyright © 2017 Elsevier Ltd. All rights reserved.

There Once Was a 9-Block ...--A Middle-School Design for Probability and Statistics

ERIC Educational Resources Information Center

Abrahamson, Dor; Janusz, Ruth M.; Wilensky, Uri

2006-01-01

ProbLab is a probability-and-statistics unit developed at the Center for Connected Learning and Computer-Based Modeling, Northwestern University. Students analyze the combinatorial space of the 9-block, a 3-by-3 grid of squares, in which each square can be either green or blue. All 512 possible 9-blocks are constructed and assembled in a "bar…

Teaching Galileo? Get to Know Riccioli! What a Forgotten Italian Astronomer Can Teach Students about How Science Works

ERIC Educational Resources Information Center

Graney, Christopher M.

2012-01-01

What can physics students learn about science from those scientists who got the answers wrong? Your students probably have encountered little science history. What they have encountered probably has portrayed scientists as "The People with the Right Answers." But those who got the wrong answers can teach students that in science, answers are often…

Social learning by following: an analysis1

PubMed Central

Bullock, Daniel; Neuringer, Allen

1977-01-01

Learning by “following”, probably a common means by which behaviors are socially transmitted from adults to young in many species, was analyzed. Pigeons first learned to eat from a human hand. When the hand then approached an operant key and pecked it, the pigeons followed and quickly learned to do the same, thereby demonstrating social learning. When the hand only led the birds to the area of the key, without demonstrating the key-peck response, the birds learned as rapidly as with a key-peck demonstration. Birds also learned, but less reliably and more slowly, when they could observe the hand's responses but were constrained and unable to follow. “Following” was also shown to engender very rapid learning of a more complex, two-member response chain. PMID:16811970

Real-time Mainshock Forecast by Statistical Discrimination of Foreshock Clusters

NASA Astrophysics Data System (ADS)

Nomura, S.; Ogata, Y.

2016-12-01

Foreshock discremination is one of the most effective ways for short-time forecast of large main shocks. Though many large earthquakes accompany their foreshocks, discreminating them from enormous small earthquakes is difficult and only probabilistic evaluation from their spatio-temporal features and magnitude evolution may be available. Logistic regression is the statistical learning method best suited to such binary pattern recognition problems where estimates of a-posteriori probability of class membership are required. Statistical learning methods can keep learning discreminating features from updating catalog and give probabilistic recognition of forecast in real time. We estimated a non-linear function of foreshock proportion by smooth spline bases and evaluate the possibility of foreshocks by the logit function. In this study, we classified foreshocks from earthquake catalog by the Japan Meteorological Agency by single-link clustering methods and learned spatial and temporal features of foreshocks by the probability density ratio estimation. We use the epicentral locations, time spans and difference in magnitudes for learning and forecasting. Magnitudes of main shocks are also predicted our method by incorporating b-values into our method. We discuss the spatial pattern of foreshocks from the classifier composed by our model. We also implement a back test to validate predictive performance of the model by this catalog.

Dynamic Encoding of Speech Sequence Probability in Human Temporal Cortex

PubMed Central

Leonard, Matthew K.; Bouchard, Kristofer E.; Tang, Claire

2015-01-01

Sensory processing involves identification of stimulus features, but also integration with the surrounding sensory and cognitive context. Previous work in animals and humans has shown fine-scale sensitivity to context in the form of learned knowledge about the statistics of the sensory environment, including relative probabilities of discrete units in a stream of sequential auditory input. These statistics are a defining characteristic of one of the most important sequential signals humans encounter: speech. For speech, extensive exposure to a language tunes listeners to the statistics of sound sequences. To address how speech sequence statistics are neurally encoded, we used high-resolution direct cortical recordings from human lateral superior temporal cortex as subjects listened to words and nonwords with varying transition probabilities between sound segments. In addition to their sensitivity to acoustic features (including contextual features, such as coarticulation), we found that neural responses dynamically encoded the language-level probability of both preceding and upcoming speech sounds. Transition probability first negatively modulated neural responses, followed by positive modulation of neural responses, consistent with coordinated predictive and retrospective recognition processes, respectively. Furthermore, transition probability encoding was different for real English words compared with nonwords, providing evidence for online interactions with high-order linguistic knowledge. These results demonstrate that sensory processing of deeply learned stimuli involves integrating physical stimulus features with their contextual sequential structure. Despite not being consciously aware of phoneme sequence statistics, listeners use this information to process spoken input and to link low-level acoustic representations with linguistic information about word identity and meaning. PMID:25948269

A Simple Artificial Life Model Explains Irrational Behavior in Human Decision-Making

PubMed Central

Feher da Silva, Carolina; Baldo, Marcus Vinícius Chrysóstomo

2012-01-01

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats’ neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments. PMID:22563454

Altering spatial priority maps via statistical learning of target selection and distractor filtering.

PubMed

Ferrante, Oscar; Patacca, Alessia; Di Caro, Valeria; Della Libera, Chiara; Santandrea, Elisa; Chelazzi, Leonardo

2018-05-01

The cognitive system has the capacity to learn and make use of environmental regularities - known as statistical learning (SL), including for the implicit guidance of attention. For instance, it is known that attentional selection is biased according to the spatial probability of targets; similarly, changes in distractor filtering can be triggered by the unequal spatial distribution of distractors. Open questions remain regarding the cognitive/neuronal mechanisms underlying SL of target selection and distractor filtering. Crucially, it is unclear whether the two processes rely on shared neuronal machinery, with unavoidable cross-talk, or they are fully independent, an issue that we directly addressed here. In a series of visual search experiments, participants had to discriminate a target stimulus, while ignoring a task-irrelevant salient distractor (when present). We systematically manipulated spatial probabilities of either one or the other stimulus, or both. We then measured performance to evaluate the direct effects of the applied contingent probability distribution (e.g., effects on target selection of the spatial imbalance in target occurrence across locations) as well as its indirect or "transfer" effects (e.g., effects of the same spatial imbalance on distractor filtering across locations). By this approach, we confirmed that SL of both target and distractor location implicitly bias attention. Most importantly, we described substantial indirect effects, with the unequal spatial probability of the target affecting filtering efficiency and, vice versa, the unequal spatial probability of the distractor affecting target selection efficiency across locations. The observed cross-talk demonstrates that SL of target selection and distractor filtering are instantiated via (at least partly) shared neuronal machinery, as further corroborated by strong correlations between direct and indirect effects at the level of individual participants. Our findings are compatible with the notion that both kinds of SL adjust the priority of specific locations within attentional priority maps of space. Copyright © 2017 Elsevier Ltd. All rights reserved.

A simple artificial life model explains irrational behavior in human decision-making.

PubMed

Feher da Silva, Carolina; Baldo, Marcus Vinícius Chrysóstomo

2012-01-01

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.

Uncertainty in action-value estimation affects both action choice and learning rate of the choice behaviors of rats.

PubMed

Funamizu, Akihiro; Ito, Makoto; Doya, Kenji; Kanzaki, Ryohei; Takahashi, Hirokazu

2012-04-01

The estimation of reward outcomes for action candidates is essential for decision making. In this study, we examined whether and how the uncertainty in reward outcome estimation affects the action choice and learning rate. We designed a choice task in which rats selected either the left-poking or right-poking hole and received a reward of a food pellet stochastically. The reward probabilities of the left and right holes were chosen from six settings (high, 100% vs. 66%; mid, 66% vs. 33%; low, 33% vs. 0% for the left vs. right holes, and the opposites) in every 20-549 trials. We used Bayesian Q-learning models to estimate the time course of the probability distribution of action values and tested if they better explain the behaviors of rats than standard Q-learning models that estimate only the mean of action values. Model comparison by cross-validation revealed that a Bayesian Q-learning model with an asymmetric update for reward and non-reward outcomes fit the choice time course of the rats best. In the action-choice equation of the Bayesian Q-learning model, the estimated coefficient for the variance of action value was positive, meaning that rats were uncertainty seeking. Further analysis of the Bayesian Q-learning model suggested that the uncertainty facilitated the effective learning rate. These results suggest that the rats consider uncertainty in action-value estimation and that they have an uncertainty-seeking action policy and uncertainty-dependent modulation of the effective learning rate. © 2012 The Authors. European Journal of Neuroscience © 2012 Federation of European Neuroscience Societies and Blackwell Publishing Ltd.

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation

PubMed Central

Kong, Zehui; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control. PMID:28671967

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

PubMed

Kong, Zehui; Zou, Yuan; Liu, Teng

2017-01-01

To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

Effect of noopept and afobazole on the development of neurosis of learned helplessness in rats.

PubMed

Uyanaev, A A; Fisenko, V P; Khitrov, N K

2003-08-01

We studied the effects of new psychotropic preparations noopept and afobazole on acquisition of the conditioned active avoidance response and development of neurosis of learned helplessness in rats. Noopept in doses of 0.05-0.10 mg/kg accelerated acquisition of conditioned active avoidance response and reduced the incidence of learned helplessness in rats. Afobazole in a dose of 5 mg/kg produced an opposite effect, which is probably related to high selective anxiolytic activity of this preparation.

Vehicle Steering control: A model of learning

NASA Technical Reports Server (NTRS)

Smiley, A.; Reid, L.; Fraser, M.

1978-01-01

A hierarchy of strategies were postulated to describe the process of learning steering control. Vehicle motion and steering control data were recorded for twelve novices who drove an instrumented car twice a week during and after a driver training course. Car-driver describing functions were calculated, the probable control structure determined, and the driver-alone transfer function modelled. The data suggested that the largest changes in steering control with learning were in the way the driver used the lateral position cue.

A Collaborative 20 Questions Model for Target Search with Human-Machine Interaction

DTIC Science & Technology

2013-05-01

optimal policies for entropy loss,” Journal of Applied Probability, vol. 49, pp. 114–136, 2012. [2] R. Castro and R. Nowak, “ Active learning and...vol. 10, pp. 223231, 1974. [8] R. Castro, Active Learning and Adaptive Sampling for Non- parametric Inference, Ph.D. thesis, Rice University, August...2007. [9] R. Castro and R. D. Nowak, “Upper and lower bounds for active learning ,” in 44th Annual Allerton Conference on Communica- tion, Control and Computing, 2006.

Structuring an Adult Learning Environment. Part IV: Establishing an Environment for Problem Solving.

ERIC Educational Resources Information Center

Frankel, Alan; Brennan, James

Through the years, many researchers have advanced theories of problem solving. Probably the best definition of problem solving to apply to adult learning programs is Wallas' (1926) four-stage theory. The stages are (1) a preparation, (2) an incubation period, (3) a moment of illumination, and (4) final application or verification of the solution.…

Teacher and Student Based Instructions on Probability Achievement Outcomes and Attitudes of Secondary School Students in Bungoma North, Kenya

ERIC Educational Resources Information Center

Pale, Joseph W.

2016-01-01

Teacher based is the usual instructional method used by most teachers in high school. Traditionally, teachers direct the learning and students work individually and assume a receptive role in their education. Student based learning approach is an instructional use of small groups of students working together to accomplish shared goals to increase…

An Information Analysis of 2-, 3-, and 4-Word Verbal Discrimination Learning.

ERIC Educational Resources Information Center

Arima, James K.; Gray, Francis D.

Information theory was used to qualify the difficulty of verbal discrimination (VD) learning tasks and to measure VD performance. Words for VD items were selected with high background frequency and equal a priori probabilities of being selected as a first response. Three VD lists containing only 2-, 3-, or 4-word items were created and equated for…

Building Commitment: An Examination of Learning Climate Congruence and the Affective Commitment of Academics in an Australian University

ERIC Educational Resources Information Center

Southcombe, Amie; Fulop, Liz; Carter, Geoff; Cavanagh, Jillian

2015-01-01

The purpose of this study is to explore the relationship between learning climate congruence and the affective commitment of university academics. The strategy of inquiry for this research is quantitative, involving a non-experimental design for the survey research. A non-probability sample of 900 academics from a large Australian university was…

Functional Reorganization in the Developing Lexicon: Separable and Changing Influences of Lexical and Phonological Variables on Children's Fast-Mapping

ERIC Educational Resources Information Center

McKean, Cristina; Letts, Carolyn; Howard, David

2013-01-01

Neighbourhood Density (ND) and Phonotactic Probability (PP) influence word learning in children. This influence appears to change over development but the separate developmental trajectories of influence of PP and ND on word learning have not previously been mapped. This study examined the cross-sectional developmental trajectories of influence of…

When Mommy Comes to the Rescue of Statistics: Infants Combine Top-Down and Bottom-Up Cues to Segment Speech

ERIC Educational Resources Information Center

Mersad, Karima; Nazzi, Thierry

2012-01-01

Transitional Probability (TP) computations are regarded as a powerful learning mechanism that is functional early in development and has been proposed as an initial bootstrapping device for speech segmentation. However, a recent study casts doubt on the robustness of early statistical word-learning. Johnson and Tyler (2010) showed that when…

Using Multiple Indicators of Cognitive State in Logistic Models that Predict Individual Performance in Machine-Mediated Learning Environments.

ERIC Educational Resources Information Center

Hancock, Thomas E.; And Others

1995-01-01

In machine-mediated learning environments, there is a need for more reliable methods of calculating the probability that a learner's response will be correct in future trials. A combination of domain-independent response-state measures of cognition along with two instructional variables for maximum predictive ability are demonstrated. (Author/LRW)

The Relationship between Socioeconomic Status and Beliefs about Language Learning: A Study of Iranian Postgraduate EAP Students

ERIC Educational Resources Information Center

Ariani, Mohsen Ghasemi; Ghafournia, Narjes

2015-01-01

This study explored the probable interaction between Iranian language students' beliefs about language learning and their socio-economic status. To this end, 350 postgraduate students, doing English courses at Islamic Azad University of Neyshabur participated in this study. They were grouped in terms of their socio-economic status. They answered a…

Stimulus probability effects in absolute identification.

PubMed

Kent, Christopher; Lamberts, Koen

2016-05-01

This study investigated the effect of stimulus presentation probability on accuracy and response times in an absolute identification task. Three schedules of presentation were used to investigate the interaction between presentation probability and stimulus position within the set. Data from individual participants indicated strong effects of presentation probability on both proportion correct and response times. The effects were moderated by the ubiquitous stimulus position effect. The accuracy and response time data were predicted by an exemplar-based model of perceptual cognition (Kent & Lamberts, 2005). The bow in discriminability was also attenuated when presentation probability for middle items was relatively high, an effect that will constrain future model development. The study provides evidence for item-specific learning in absolute identification. Implications for other theories of absolute identification are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The revival of the Baldwin effect

NASA Astrophysics Data System (ADS)

Fontanari, José F.; Santos, Mauro

2017-10-01

The idea that a genetically fixed behavior evolved from the once differential learning ability of individuals that performed the behavior is known as the Baldwin effect. A highly influential paper [G.E. Hinton, S.J. Nowlan, Complex Syst. 1, 495 (1987)] claimed that this effect can be observed in silico, but here we argue that what was actually shown is that the learning ability is easily selected for. Then we demonstrate the Baldwin effect to happen in the in silico scenario by estimating the probability and waiting times for the learned behavior to become innate. Depending on parameter values, we find that learning can increase the chance of fixation of the learned behavior by several orders of magnitude compared with the non-learning situation.

Preserved learning of novel information in amnesia: evidence for multiple memory systems.

PubMed

Gordon, B

1988-06-01

Four of five patients with marked global amnesia, and others with new learning impairments, showed normal processing facilitation for novel stimuli (nonwords) and/or for familiar stimuli (words) on a word/nonword (lexical) decision task. The data are interpreted as a reflection of the learning capabilities of in-line neural processing stages with multiple, distinct, informational codes. These in-line learning processes are separate from the recognition/recall memory impaired by amygdalohippocampal/dosomedial thalamic damage, but probably supplement such memory in some tasks in normal individuals. Preserved learning of novel information seems incompatible with explanations of spared learning in amnesia that are based on the episodic/semantic or memory/habit distinctions, but is consistent with the procedural/declarative hypothesis.

Musical Experience Influences Statistical Learning of a Novel Language

PubMed Central

Shook, Anthony; Marian, Viorica; Bartolotti, James; Schroeder, Scott R.

2014-01-01

Musical experience may benefit learning a new language by enhancing the fidelity with which the auditory system encodes sound. In the current study, participants with varying degrees of musical experience were exposed to two statistically-defined languages consisting of auditory Morse-code sequences which varied in difficulty. We found an advantage for highly-skilled musicians, relative to less-skilled musicians, in learning novel Morse-code based words. Furthermore, in the more difficult learning condition, performance of lower-skilled musicians was mediated by their general cognitive abilities. We suggest that musical experience may lead to enhanced processing of statistical information and that musicians’ enhanced ability to learn statistical probabilities in a novel Morse-code language may extend to natural language learning. PMID:23505962

Risk-sensitive reinforcement learning.

PubMed

Shen, Yun; Tobia, Michael J; Sommer, Tobias; Obermayer, Klaus

2014-07-01

We derive a family of risk-sensitive reinforcement learning methods for agents, who face sequential decision-making tasks in uncertain environments. By applying a utility function to the temporal difference (TD) error, nonlinear transformations are effectively applied not only to the received rewards but also to the true transition probabilities of the underlying Markov decision process. When appropriate utility functions are chosen, the agents' behaviors express key features of human behavior as predicted by prospect theory (Kahneman & Tversky, 1979 ), for example, different risk preferences for gains and losses, as well as the shape of subjective probability curves. We derive a risk-sensitive Q-learning algorithm, which is necessary for modeling human behavior when transition probabilities are unknown, and prove its convergence. As a proof of principle for the applicability of the new framework, we apply it to quantify human behavior in a sequential investment task. We find that the risk-sensitive variant provides a significantly better fit to the behavioral data and that it leads to an interpretation of the subject's responses that is indeed consistent with prospect theory. The analysis of simultaneously measured fMRI signals shows a significant correlation of the risk-sensitive TD error with BOLD signal change in the ventral striatum. In addition we find a significant correlation of the risk-sensitive Q-values with neural activity in the striatum, cingulate cortex, and insula that is not present if standard Q-values are used.

Microstimulation of the Human Substantia Nigra Alters Reinforcement Learning

PubMed Central

Ramayya, Ashwin G.; Misra, Amrit

2014-01-01

Animal studies have shown that substantia nigra (SN) dopaminergic (DA) neurons strengthen action–reward associations during reinforcement learning, but their role in human learning is not known. Here, we applied microstimulation in the SN of 11 patients undergoing deep brain stimulation surgery for the treatment of Parkinson's disease as they performed a two-alternative probability learning task in which rewards were contingent on stimuli, rather than actions. Subjects demonstrated decreased learning from reward trials that were accompanied by phasic SN microstimulation compared with reward trials without stimulation. Subjects who showed large decreases in learning also showed an increased bias toward repeating actions after stimulation trials; therefore, stimulation may have decreased learning by strengthening action–reward associations rather than stimulus–reward associations. Our findings build on previous studies implicating SN DA neurons in preferentially strengthening action–reward associations during reinforcement learning. PMID:24828643

Macaque monkeys can learn token values from human models through vicarious reward.

PubMed

Bevacqua, Sara; Cerasti, Erika; Falcone, Rossella; Cervelloni, Milena; Brunamonti, Emiliano; Ferraina, Stefano; Genovesio, Aldo

2013-01-01

Monkeys can learn the symbolic meaning of tokens, and exchange them to get a reward. Monkeys can also learn the symbolic value of a token by observing conspecifics but it is not clear if they can learn passively by observing other actors, e.g., humans. To answer this question, we tested two monkeys in a token exchange paradigm in three experiments. Monkeys learned token values through observation of human models exchanging them. We used, after a phase of object familiarization, different sets of tokens. One token of each set was rewarded with a bit of apple. Other tokens had zero value (neutral tokens). Each token was presented only in one set. During the observation phase, monkeys watched the human model exchange tokens and watched them consume rewards (vicarious rewards). In the test phase, the monkeys were asked to exchange one of the tokens for food reward. Sets of three tokens were used in the first experiment and sets of two tokens were used in the second and third experiments. The valuable token was presented with different probabilities in the observation phase during the first and second experiments in which the monkeys exchanged the valuable token more frequently than any of the neutral tokens. The third experiments examined the effect of unequal probabilities. Our results support the view that monkeys can learn from non-conspecific actors through vicarious reward, even a symbolic task like the token-exchange task.

Stimulating investment in energy materials and technologies to combat climate change: an overview of learning curve analysis and niche market support.

PubMed

Foxon, Timothy J

2010-07-28

This paper addresses the probable levels of investment needed in new technologies for energy conversion and storage that are essential to address climate change, drawing on past evidence on the rate of cost improvements in energy technologies. A range of energy materials and technologies with lower carbon emissions over their life cycle are being developed, including fuel cells (FCs), hydrogen storage, batteries, supercapacitors, solar energy and nuclear power, and it is probable that most, if not all, of these technologies will be needed to mitigate climate change. High rates of innovation and deployment will be needed to meet targets such as the UK's goal of reducing its greenhouse gas emissions by 80 per cent by 2050, which will require significant levels of investment. Learning curves observed for reductions in unit costs of energy technologies, such as photovoltaics and FCs, can provide evidence on the probable future levels of investment needed. The paper concludes by making recommendations for policy measures to promote such investment from both the public and private sectors.

Spatial working memory interferes with explicit, but not probabilistic cuing of spatial attention.

PubMed

Won, Bo-Yeong; Jiang, Yuhong V

2015-05-01

Recent empirical and theoretical work has depicted a close relationship between visual attention and visual working memory. For example, rehearsal in spatial working memory depends on spatial attention, whereas adding a secondary spatial working memory task impairs attentional deployment in visual search. These findings have led to the proposal that working memory is attention directed toward internal representations. Here, we show that the close relationship between these 2 constructs is limited to some but not all forms of spatial attention. In 5 experiments, participants held color arrays, dot locations, or a sequence of dots in working memory. During the memory retention interval, they performed a T-among-L visual search task. Crucially, the probable target location was cued either implicitly through location probability learning or explicitly with a central arrow or verbal instruction. Our results showed that whereas imposing a visual working memory load diminished the effectiveness of explicit cuing, it did not interfere with probability cuing. We conclude that spatial working memory shares similar mechanisms with explicit, goal-driven attention but is dissociated from implicitly learned attention. (c) 2015 APA, all rights reserved).

Spatial working memory interferes with explicit, but not probabilistic cuing of spatial attention

PubMed Central

Won, Bo-Yeong; Jiang, Yuhong V.

2014-01-01

Recent empirical and theoretical work has depicted a close relationship between visual attention and visual working memory. For example, rehearsal in spatial working memory depends on spatial attention, whereas adding a secondary spatial working memory task impairs attentional deployment in visual search. These findings have led to the proposal that working memory is attention directed toward internal representations. Here we show that the close relationship between these two constructs is limited to some but not all forms of spatial attention. In five experiments, participants held color arrays, dot locations, or a sequence of dots in working memory. During the memory retention interval they performed a T-among-L visual search task. Crucially, the probable target location was cued either implicitly through location probability learning, or explicitly with a central arrow or verbal instruction. Our results showed that whereas imposing a visual working memory load diminished the effectiveness of explicit cuing, it did not interfere with probability cuing. We conclude that spatial working memory shares similar mechanisms with explicit, goal-driven attention but is dissociated from implicitly learned attention. PMID:25401460

Machine Learning Principles Can Improve Hip Fracture Prediction.

PubMed

Kruse, Christian; Eiken, Pia; Vestergaard, Peter

2017-04-01

Apply machine learning principles to predict hip fractures and estimate predictor importance in Dual-energy X-ray absorptiometry (DXA)-scanned men and women. Dual-energy X-ray absorptiometry data from two Danish regions between 1996 and 2006 were combined with national Danish patient data to comprise 4722 women and 717 men with 5 years of follow-up time (original cohort n = 6606 men and women). Twenty-four statistical models were built on 75% of data points through k-5, 5-repeat cross-validation, and then validated on the remaining 25% of data points to calculate area under the curve (AUC) and calibrate probability estimates. The best models were retrained with restricted predictor subsets to estimate the best subsets. For women, bootstrap aggregated flexible discriminant analysis ("bagFDA") performed best with a test AUC of 0.92 [0.89; 0.94] and well-calibrated probabilities following Naïve Bayes adjustments. A "bagFDA" model limited to 11 predictors (among them bone mineral densities (BMD), biochemical glucose measurements, general practitioner and dentist use) achieved a test AUC of 0.91 [0.88; 0.93]. For men, eXtreme Gradient Boosting ("xgbTree") performed best with a test AUC of 0.89 [0.82; 0.95], but with poor calibration in higher probabilities. A ten predictor subset (BMD, biochemical cholesterol and liver function tests, penicillin use and osteoarthritis diagnoses) achieved a test AUC of 0.86 [0.78; 0.94] using an "xgbTree" model. Machine learning can improve hip fracture prediction beyond logistic regression using ensemble models. Compiling data from international cohorts of longer follow-up and performing similar machine learning procedures has the potential to further improve discrimination and calibration.

New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis

Treesearch

L.R. Iverson; A.M. Prasad; A. Liaw

2004-01-01

More and better machine learning tools are becoming available for landscape ecologists to aid in understanding species-environment relationships and to map probable species occurrence now and potentially into the future. To thal end, we evaluated three statistical models: Regression Tree Analybib (RTA), Bagging Trees (BT) and Random Forest (RF) for their utility in...

Anatomy of a Decision: Striato-Orbitofrontal Interactions in Reinforcement Learning, Decision Making, and Reversal

ERIC Educational Resources Information Center

Frank, Michael J.; Claus, Eric D.

2006-01-01

The authors explore the division of labor between the basal ganglia-dopamine (BG-DA) system and the orbitofrontal cortex (OFC) in decision making. They show that a primitive neural network model of the BG-DA system slowly learns to make decisions on the basis of the relative probability of rewards but is not as sensitive to (a) recency or (b) the…

The Role of Cooperative Learning Type Team Assisted Individualization to Improve the Students' Mathematics Communication Ability in the Subject of Probability Theory

ERIC Educational Resources Information Center

Tinungki, Georgina Maria

2015-01-01

The importance of learning mathematics can not be separated from its role in all aspects of life. Communicating ideas by using mathematics language is even more practical, systematic, and efficient. In order to overcome the difficulties of students who have insufficient understanding of mathematics material, good communications should be built in…

Artificial Intelligence-Based Student Learning Evaluation: A Concept Map-Based Approach for Analyzing a Student's Understanding of a Topic

ERIC Educational Resources Information Center

Jain, G. Panka; Gurupur, Varadraj P.; Schroeder, Jennifer L.; Faulkenberry, Eileen D.

2014-01-01

In this paper, we describe a tool coined as artificial intelligence-based student learning evaluation tool (AISLE). The main purpose of this tool is to improve the use of artificial intelligence techniques in evaluating a student's understanding of a particular topic of study using concept maps. Here, we calculate the probability distribution of…

Usefulness of video-laryngoscopy with the Airway Scope for intubation performance and learning: an experimental manikin controlled study.

PubMed

Declercq, Pierre-Louis; Bubenheim, Michael; Gelinotte, Stéphanie; Guernon, Kévin; Michot, Jean-Baptiste; Royon, Vincent; Carpentier, Dorothée; Béduneau, Gaëtan; Tamion, Fabienne; Girault, Christophe

2016-12-01

Different video-laryngoscopes (VDLs) for endotracheal intubation (ETI) have recently been developed. We compared the performance of the VDL Airway Scope (AWS) with the direct laryngoscopy by Macintosh (DLM) for ETI success, time and learning. We performed an experimental manikin controlled study. Twenty experienced (experts) and 40 inexperienced operators (novices) for DLM-ETI were enrolled. None of them had experience with the use of AWS-VDL. Novices were assigned to start learning with DLM or AWS, and two sub-groups of 20 novices were formed. Experts group constituted the control group. Each participant performed 10 ETI attempts with each device on the same standard manikin. The primary endpoint was the ETI success probability. Secondary endpoints were ETI time, technical validity and qualitative evaluation for each technique. We also assessed the learning order and the successive attempts effects for these parameters. Overall, 1200 ETI attempts were performed. ETI success probability was higher with the AWS than with the DLM for all operators (98 vs. 81 %; p < 0.0001) and for experts compared to novices using devices in the same order (97 vs. 83 %; p = 0.0002). Overall ETI time was shorter with the AWS than with the DLM (13 vs. 20 s; p < 0.0001) and for experts compared to novices using devices in the same order (11 vs. 21 s; p < 0.0001). Among novices, those starting learning with AWS had higher ETI success probability (89 vs. 83 %; p = 0.03) and shorter ETI time (18 vs. 21 s; p = 0.02). Technical validity was found better with the AWS than DLM for all operators. Novices expressed global satisfaction and device preference for the AWS, whereas experts were indifferent. AWS-VDL permits faster, easier and more reliable ETI compared to the DLM whatever the previous airway ETI experience and could be a useful device for DLM-ETI learning.

War Termination Concepts and Political, Economic and Military Targeting

DTIC Science & Technology

1978-03-01

imposed by the level of military technology, also served to reduce the probability of transcultural conflict; thus shared value systems probably...from the Berlin Blockade in 1948 through the Cuban Missile Crisis , and, utilizing the "lessons learned" approach, reformulates deterrence theory on...aspect of which is the "general crisis of capitalism," presently said to be in its most severe phase since the 1930s. The present crisis , Soviet

Storm-based Cloud-to-Ground Lightning Probabilities and Warnings

NASA Astrophysics Data System (ADS)

Calhoun, K. M.; Meyer, T.; Kingfield, D.

2017-12-01

A new cloud-to-ground (CG) lightning probability algorithm has been developed using machine-learning methods. With storm-based inputs of Earth Networks' in-cloud lightning, Vaisala's CG lightning, multi-radar/multi-sensor (MRMS) radar derived products including the Maximum Expected Size of Hail (MESH) and Vertically Integrated Liquid (VIL), and near storm environmental data including lapse rate and CAPE, a random forest algorithm was trained to produce probabilities of CG lightning up to one-hour in advance. As part of the Prototype Probabilistic Hazard Information experiment in the Hazardous Weather Testbed in 2016 and 2017, National Weather Service forecasters were asked to use this CG lightning probability guidance to create rapidly updating probability grids and warnings for the threat of CG lightning for 0-60 minutes. The output from forecasters was shared with end-users, including emergency managers and broadcast meteorologists, as part of an integrated warning team.

Interactive and collaborative learning in the classroom at the medical school Automated response systems and team-based learning.

PubMed

Nasr, Rihab; Antoun, Jumana; Sabra, Ramzi; Zgheib, Nathalie K

2016-01-01

There has been a pedagogic shift in higher education from the traditional teacher centered to the student centered approach in teaching, necessitating a change in the role of the teacher from a supplier of information to passive receptive students into a more facilitative role. Active learning activities are based on various learning theories such as self-directed learning, cooperative learning and adult learning. There exist many instructional activities that enhance active and collaborative learning. The aim of this manuscript is to describe two methods of interactive and collaborative learning in the classroom, automated response systems (ARS) and team-based learning (TBL), and to list some of their applications and advantages. The success of these innovative teaching and learning methods at a large scale depends on few elements, probably the most important of which is the support of the higher administration and leadership in addition to the availability of “champions” who are committed to lead the change.

Microstimulation of the human substantia nigra alters reinforcement learning.

PubMed

Ramayya, Ashwin G; Misra, Amrit; Baltuch, Gordon H; Kahana, Michael J

2014-05-14

Animal studies have shown that substantia nigra (SN) dopaminergic (DA) neurons strengthen action-reward associations during reinforcement learning, but their role in human learning is not known. Here, we applied microstimulation in the SN of 11 patients undergoing deep brain stimulation surgery for the treatment of Parkinson's disease as they performed a two-alternative probability learning task in which rewards were contingent on stimuli, rather than actions. Subjects demonstrated decreased learning from reward trials that were accompanied by phasic SN microstimulation compared with reward trials without stimulation. Subjects who showed large decreases in learning also showed an increased bias toward repeating actions after stimulation trials; therefore, stimulation may have decreased learning by strengthening action-reward associations rather than stimulus-reward associations. Our findings build on previous studies implicating SN DA neurons in preferentially strengthening action-reward associations during reinforcement learning. Copyright © 2014 the authors 0270-6474/14/346887-09$15.00/0.

Learning and dynamics in social systems. Comment on "Collective learning modeling based on the kinetic theory of active particles" by D. Burini et al.

NASA Astrophysics Data System (ADS)

Dolfin, Marina

2016-03-01

The interesting novelty of the paper by Burini et al. [1] is that the authors present a survey and a new approach of collective learning based on suitable development of methods of the kinetic theory [2] and theoretical tools of evolutionary game theory [3]. Methods of statistical dynamics and kinetic theory lead naturally to stochastic and collective dynamics. Indeed, the authors propose the use of games where the state of the interacting entities is delivered by probability distributions.

Decentralized learning in Markov games.

PubMed

Vrancx, Peter; Verbeeck, Katja; Nowé, Ann

2008-08-01

Learning automata (LA) were recently shown to be valuable tools for designing multiagent reinforcement learning algorithms. One of the principal contributions of the LA theory is that a set of decentralized independent LA is able to control a finite Markov chain with unknown transition probabilities and rewards. In this paper, we propose to extend this algorithm to Markov games--a straightforward extension of single-agent Markov decision problems to distributed multiagent decision problems. We show that under the same ergodic assumptions of the original theorem, the extended algorithm will converge to a pure equilibrium point between agent policies.

An Analysis of the Relationship between the Learning Process and Learning Motivation Profiles of Japanese Pharmacy Students Using Structural Equation Modeling.

PubMed

Yamamura, Shigeo; Takehira, Rieko

2018-04-23

Pharmacy students in Japan have to maintain strong motivation to learn for six years during their education. The authors explored the students’ learning structure. All pharmacy students in their 4th through to 6th year at Josai International University participated in the survey. The revised two factor study process questionnaire and science motivation questionnaire II were used to assess their learning process and learning motivation profiles, respectively. Structural equation modeling (SEM) was used to examine a causal relationship between the latent variables in the learning process and those in the learning motivation profile. The learning structure was modeled on the idea that the learning process affects the learning motivation profile of respondents. In the multi-group SEM, the estimated mean of the deep learning to learning motivation profile increased just after their clinical clerkship for 6th year students. This indicated that the clinical experience benefited students’ deep learning, which is probably because the experience of meeting with real patients encourages meaningful learning in pharmacy studies.

"Electronium": A Quantum Atomic Teaching Model.

ERIC Educational Resources Information Center

Budde, Marion; Niedderer, Hans; Scott, Philip; Leach, John

2002-01-01

Outlines an alternative atomic model to the probability model, the descriptive quantum atomic model Electronium. Discusses the way in which it is intended to support students in learning quantum-mechanical concepts. (Author/MM)

Representing Learning With Graphical Models

NASA Technical Reports Server (NTRS)

Buntine, Wray L.; Lum, Henry, Jr. (Technical Monitor)

1994-01-01

Probabilistic graphical models are being used widely in artificial intelligence, for instance, in diagnosis and expert systems, as a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. Their development and use spans several fields including artificial intelligence, decision theory and statistics, and provides an important bridge between these communities. This paper shows by way of example that these models can be extended to machine learning, neural networks and knowledge discovery by representing the notion of a sample on the graphical model. Not only does this allow a flexible variety of learning problems to be represented, it also provides the means for representing the goal of learning and opens the way for the automatic development of learning algorithms from specifications.

The probability of reinforcement per trial affects posttrial responding and subsequent extinction but not within-trial responding.

PubMed

Harris, Justin A; Kwok, Dorothy W S

2018-01-01

During magazine approach conditioning, rats do not discriminate between a conditional stimulus (CS) that is consistently reinforced with food and a CS that is occasionally (partially) reinforced, as long as the CSs have the same overall reinforcement rate per second. This implies that rats are indifferent to the probability of reinforcement per trial. However, in the same rats, the per-trial reinforcement rate will affect subsequent extinction-responding extinguishes more rapidly for a CS that was consistently reinforced than for a partially reinforced CS. Here, we trained rats with consistently and partially reinforced CSs that were matched for overall reinforcement rate per second. We measured conditioned responding both during and immediately after the CSs. Differences in the per-trial probability of reinforcement did not affect the acquisition of responding during the CS but did affect subsequent extinction of that responding, and also affected the post-CS response rates during conditioning. Indeed, CSs with the same probability of reinforcement per trial evoked the same amount of post-CS responding even when they differed in overall reinforcement rate and thus evoked different amounts of responding during the CS. We conclude that reinforcement rate per second controls rats' acquisition of responding during the CS, but at the same time, rats also learn specifically about the probability of reinforcement per trial. The latter learning affects the rats' expectation of reinforcement as an outcome of the trial, which influences their ability to detect retrospectively that an opportunity for reinforcement was missed, and, in turn, drives extinction. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Evaluation of the probability of arrester failure in a high-voltage transmission line using a Q learning artificial neural network model

NASA Astrophysics Data System (ADS)

Ekonomou, L.; Karampelas, P.; Vita, V.; Chatzarakis, G. E.

2011-04-01

One of the most popular methods of protecting high voltage transmission lines against lightning strikes and internal overvoltages is the use of arresters. The installation of arresters in high voltage transmission lines can prevent or even reduce the lines' failure rate. Several studies based on simulation tools have been presented in order to estimate the critical currents that exceed the arresters' rated energy stress and to specify the arresters' installation interval. In this work artificial intelligence, and more specifically a Q-learning artificial neural network (ANN) model, is addressed for evaluating the arresters' failure probability. The aims of the paper are to describe in detail the developed Q-learning ANN model and to compare the results obtained by its application in operating 150 kV Greek transmission lines with those produced using a simulation tool. The satisfactory and accurate results of the proposed ANN model can make it a valuable tool for designers of electrical power systems seeking more effective lightning protection, reducing operational costs and better continuity of service.

Conservative forgetful scholars: How people learn causal structure through sequences of interventions.

PubMed

Bramley, Neil R; Lagnado, David A; Speekenbrink, Maarten

2015-05-01

Interacting with a system is key to uncovering its causal structure. A computational framework for interventional causal learning has been developed over the last decade, but how real causal learners might achieve or approximate the computations entailed by this framework is still poorly understood. Here we describe an interactive computer task in which participants were incentivized to learn the structure of probabilistic causal systems through free selection of multiple interventions. We develop models of participants' intervention choices and online structure judgments, using expected utility gain, probability gain, and information gain and introducing plausible memory and processing constraints. We find that successful participants are best described by a model that acts to maximize information (rather than expected score or probability of being correct); that forgets much of the evidence received in earlier trials; but that mitigates this by being conservative, preferring structures consistent with earlier stated beliefs. We explore 2 heuristics that partly explain how participants might be approximating these models without explicitly representing or updating a hypothesis space. (c) 2015 APA, all rights reserved).

Synaptic and nonsynaptic plasticity approximating probabilistic inference

PubMed Central

Tully, Philip J.; Hennig, Matthias H.; Lansner, Anders

2014-01-01

Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert. PMID:24782758

Machine learning approaches to the social determinants of health in the health and retirement study.

PubMed

Seligman, Benjamin; Tuljapurkar, Shripad; Rehkopf, David

2018-04-01

Social and economic factors are important predictors of health and of recognized importance for health systems. However, machine learning, used elsewhere in the biomedical literature, has not been extensively applied to study relationships between society and health. We investigate how machine learning may add to our understanding of social determinants of health using data from the Health and Retirement Study. A linear regression of age and gender, and a parsimonious theory-based regression additionally incorporating income, wealth, and education, were used to predict systolic blood pressure, body mass index, waist circumference, and telomere length. Prediction, fit, and interpretability were compared across four machine learning methods: linear regression, penalized regressions, random forests, and neural networks. All models had poor out-of-sample prediction. Most machine learning models performed similarly to the simpler models. However, neural networks greatly outperformed the three other methods. Neural networks also had good fit to the data ( R 2 between 0.4-0.6, versus <0.3 for all others). Across machine learning models, nine variables were frequently selected or highly weighted as predictors: dental visits, current smoking, self-rated health, serial-seven subtractions, probability of receiving an inheritance, probability of leaving an inheritance of at least $10,000, number of children ever born, African-American race, and gender. Some of the machine learning methods do not improve prediction or fit beyond simpler models, however, neural networks performed well. The predictors identified across models suggest underlying social factors that are important predictors of biological indicators of chronic disease, and that the non-linear and interactive relationships between variables fundamental to the neural network approach may be important to consider.

Learning to play Go using recursive neural networks.

PubMed

Wu, Lin; Baldi, Pierre

2008-11-01

Go is an ancient board game that poses unique opportunities and challenges for artificial intelligence. Currently, there are no computer Go programs that can play at the level of a good human player. However, the emergence of large repositories of games is opening the door for new machine learning approaches to address this challenge. Here we develop a machine learning approach to Go, and related board games, focusing primarily on the problem of learning a good evaluation function in a scalable way. Scalability is essential at multiple levels, from the library of local tactical patterns, to the integration of patterns across the board, to the size of the board itself. The system we propose is capable of automatically learning the propensity of local patterns from a library of games. Propensity and other local tactical information are fed into recursive neural networks, derived from a probabilistic Bayesian network architecture. The recursive neural networks in turn integrate local information across the board in all four cardinal directions and produce local outputs that represent local territory ownership probabilities. The aggregation of these probabilities provides an effective strategic evaluation function that is an estimate of the expected area at the end, or at various other stages, of the game. Local area targets for training can be derived from datasets of games played by human players. In this approach, while requiring a learning time proportional to N(4), skills learned on a board of size N(2) can easily be transferred to boards of other sizes. A system trained using only 9 x 9 amateur game data performs surprisingly well on a test set derived from 19 x 19 professional game data. Possible directions for further improvements are briefly discussed.

Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification

NASA Astrophysics Data System (ADS)

Tesoriero, Anthony J.; Gronberg, Jo Ann; Juckem, Paul F.; Miller, Matthew P.; Austin, Brian P.

2017-08-01

Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide-bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions.

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

Predicting redox-sensitive contaminant concentrations in groundwater using random forest classification

USGS Publications Warehouse

Tesoriero, Anthony J.; Gronberg, Jo Ann M.; Juckem, Paul F.; Miller, Matthew P.; Austin, Brian P.

2017-01-01

Machine learning techniques were applied to a large (n > 10,000) compliance monitoring database to predict the occurrence of several redox-active constituents in groundwater across a large watershed. Specifically, random forest classification was used to determine the probabilities of detecting elevated concentrations of nitrate, iron, and arsenic in the Fox, Wolf, Peshtigo, and surrounding watersheds in northeastern Wisconsin. Random forest classification is well suited to describe the nonlinear relationships observed among several explanatory variables and the predicted probabilities of elevated concentrations of nitrate, iron, and arsenic. Maps of the probability of elevated nitrate, iron, and arsenic can be used to assess groundwater vulnerability and the vulnerability of streams to contaminants derived from groundwater. Processes responsible for elevated concentrations are elucidated using partial dependence plots. For example, an increase in the probability of elevated iron and arsenic occurred when well depths coincided with the glacial/bedrock interface, suggesting a bedrock source for these constituents. Furthermore, groundwater in contact with Ordovician bedrock has a higher likelihood of elevated iron concentrations, which supports the hypothesis that groundwater liberates iron from a sulfide-bearing secondary cement horizon of Ordovician age. Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broadly assess aquifer and stream vulnerability at regional and national scales and to better understand geochemical processes responsible for observed conditions.

Generative adversarial networks for brain lesion detection

NASA Astrophysics Data System (ADS)

Alex, Varghese; Safwan, K. P. Mohammed; Chennamsetty, Sai Saketh; Krishnamurthi, Ganapathy

2017-02-01

Manual segmentation of brain lesions from Magnetic Resonance Images (MRI) is cumbersome and introduces errors due to inter-rater variability. This paper introduces a semi-supervised technique for detection of brain lesion from MRI using Generative Adversarial Networks (GANs). GANs comprises of a Generator network and a Discriminator network which are trained simultaneously with the objective of one bettering the other. The networks were trained using non lesion patches (n=13,000) from 4 different MR sequences. The network was trained on BraTS dataset and patches were extracted from regions excluding tumor region. The Generator network generates data by modeling the underlying probability distribution of the training data, (PData). The Discriminator learns the posterior probability P (Label Data) by classifying training data and generated data as "Real" or "Fake" respectively. The Generator upon learning the joint distribution, produces images/patches such that the performance of the Discriminator on them are random, i.e. P (Label Data = GeneratedData) = 0.5. During testing, the Discriminator assigns posterior probability values close to 0.5 for patches from non lesion regions, while patches centered on lesion arise from a different distribution (PLesion) and hence are assigned lower posterior probability value by the Discriminator. On the test set (n=14), the proposed technique achieves whole tumor dice score of 0.69, sensitivity of 91% and specificity of 59%. Additionally the generator network was capable of generating non lesion patches from various MR sequences.

Lessons Learned From The EMU Fire and How It Impacts CxP Suit Element Development and Testing

NASA Technical Reports Server (NTRS)

Metts, Jonathan; Hill, Terry

2008-01-01

During testing a Space Shuttle Extravehicular Mobility Unit (EMU) pressure garment and life-support backpack was destroyed in a flash fire in the Johnson Space Center's Crew systems laboratory. This slide presentation reviews the accident, probable causes, the lessons learned and the effect this has on the testing and the environment for testing of the Space Suit for the Constellation Program.

Context-Based Intent Understanding for Autonomous Systems in Naval and Collaborative Robot Applications

DTIC Science & Technology

2013-10-29

COVERED (From - To) 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d...based on contextual information, 3) develop vision-based techniques for learning of contextual information, and detection and identification of...that takes into account many possible contexts. The probability distributions of these contexts will be learned from existing databases on common sense

Developing a Hypothetical Learning Trajectory for the Sampling Distribution of the Sample Means

NASA Astrophysics Data System (ADS)

Syafriandi

2018-04-01

Special types of probability distribution are sampling distributions that are important in hypothesis testing. The concept of a sampling distribution may well be the key concept in understanding how inferential procedures work. In this paper, we will design a hypothetical learning trajectory (HLT) for the sampling distribution of the sample mean, and we will discuss how the sampling distribution is used in hypothesis testing.

"The Errors Were the Results of Errors": Promoting Good Writing by Bad Example

ERIC Educational Resources Information Center

Korsunsky, Boris

2010-01-01

We learn best by example--this adage is probably as old as teaching itself. In my own classroom, I have found that very often the students learn best from the "negative" examples. Perhaps, this shouldn't come as a surprise at all. After all, we don't react strongly to the norm--but an obvious deviation from the norm may attract our attention and…

How Much Comprehensible Input Did Heinrich Schliemann Get?

ERIC Educational Resources Information Center

Krashen, Stephen D.

1991-01-01

Examines Heinrich Schliemann's method of acquiring a second language primarily by means of conscious learning. It is revealed that Schliemann probably obtained a great deal of comprehensible input in English. (nine references) (GLR)

Learning Probabilities From Random Observables in High Dimensions: The Maximum Entropy Distribution and Others

NASA Astrophysics Data System (ADS)

Obuchi, Tomoyuki; Cocco, Simona; Monasson, Rémi

2015-11-01

We consider the problem of learning a target probability distribution over a set of N binary variables from the knowledge of the expectation values (with this target distribution) of M observables, drawn uniformly at random. The space of all probability distributions compatible with these M expectation values within some fixed accuracy, called version space, is studied. We introduce a biased measure over the version space, which gives a boost increasing exponentially with the entropy of the distributions and with an arbitrary inverse `temperature' Γ . The choice of Γ allows us to interpolate smoothly between the unbiased measure over all distributions in the version space (Γ =0) and the pointwise measure concentrated at the maximum entropy distribution (Γ → ∞ ). Using the replica method we compute the volume of the version space and other quantities of interest, such as the distance R between the target distribution and the center-of-mass distribution over the version space, as functions of α =(log M)/N and Γ for large N. Phase transitions at critical values of α are found, corresponding to qualitative improvements in the learning of the target distribution and to the decrease of the distance R. However, for fixed α the distance R does not vary with Γ which means that the maximum entropy distribution is not closer to the target distribution than any other distribution compatible with the observable values. Our results are confirmed by Monte Carlo sampling of the version space for small system sizes (N≤ 10).

Examining risk in mineral exploration

USGS Publications Warehouse

Singer, Donald A.; Kouda, Ryoichi

1999-01-01

Successful mineral exploration strategy requires identification of some of the risk sources and considering them in the decision-making process so that controllable risk can be reduced. Risk is defined as chance of failure or loss. Exploration is an economic activity involving risk and uncertainty, so risk also must be defined in an economic context. Risk reduction can be addressed in three fundamental ways: (1) increasing the number of examinations; (2) increasing success probabilities; and (3) changing success probabilities per test by learning. These provide the framework for examining exploration risk. First, the number of prospects examined is increased, such as by joint venturing, thereby reducing chance of gambler's ruin. Second, success probability is increased by exploring for deposit types more likely to be economic, such as those with a high proportion of world-class deposits. For example, in looking for 100+ ton (>3 million oz) Au deposits, porphyry Cu-Au, or epithermal quartz alunite Au types require examining fewer deposits than Comstock epithermal vein and most other deposit types. For porphyry copper exploration, a strong positive relationship between area of sulfide minerals and deposits' contained Cu can be used to reduce exploration risk by only examining large sulfide systems. In some situations, success probabilities can be increased by examining certain geologic environments. Only 8% of kuroko massive sulfide deposits are world class, but success chances can be increased to about 15% by looking in settings containing sediments and rhyolitic rocks. It is possible to reduce risk of loss during mining by sequentially developing and expanding a mine—thus reducing capital exposed at early stages and reducing present value of risked capital. Because this strategy is easier to apply in some deposit types than in others, the strategy can affect deposit types sought. Third, risk is reduced by using prior information and by changing the independence of trials assumption, that is, by learning. Bayes' formula is used to change the probability of existence of the deposit sought on the basis of successive exploration stages. Perhaps the most important way to reduce exploration risk is to employ personnel with the appropriate experience and yet who are learning.

Competitive STDP Learning of Overlapping Spatial Patterns.

PubMed

Krunglevicius, Dalius

2015-08-01

Spike-timing-dependent plasticity (STDP) is a set of Hebbian learning rules firmly based on biological evidence. It has been demonstrated that one of the STDP learning rules is suited for learning spatiotemporal patterns. When multiple neurons are organized in a simple competitive spiking neural network, this network is capable of learning multiple distinct patterns. If patterns overlap significantly (i.e., patterns are mutually inclusive), however, competition would not preclude trained neuron's responding to a new pattern and adjusting synaptic weights accordingly. This letter presents a simple neural network that combines vertical inhibition and Euclidean distance-dependent synaptic strength factor. This approach helps to solve the problem of pattern size-dependent parameter optimality and significantly reduces the probability of a neuron's forgetting an already learned pattern. For demonstration purposes, the network was trained for the first ten letters of the Braille alphabet.

Statistical learning and language acquisition

PubMed Central

Romberg, Alexa R.; Saffran, Jenny R.

2011-01-01

Human learners, including infants, are highly sensitive to structure in their environment. Statistical learning refers to the process of extracting this structure. A major question in language acquisition in the past few decades has been the extent to which infants use statistical learning mechanisms to acquire their native language. There have been many demonstrations showing infants’ ability to extract structures in linguistic input, such as the transitional probability between adjacent elements. This paper reviews current research on how statistical learning contributes to language acquisition. Current research is extending the initial findings of infants’ sensitivity to basic statistical information in many different directions, including investigating how infants represent regularities, learn about different levels of language, and integrate information across situations. These current directions emphasize studying statistical language learning in context: within language, within the infant learner, and within the environment as a whole. PMID:21666883

The future of e-learning in healthcare professional education: some possible directions. Commentary.

PubMed

Walsh, Kieran

2014-01-01

E-learning in healthcare professional education still seems like it is a new innovation but the reality is that e-learning has been around for as long as the internet has been around. This is approximately twenty years and so it is probably appropriate to now take stock and consider what the future of e-learning in healthcare professional education might be. One likely occurrence is that there will be more formats, more interactive technology, and sometimes game-based learning. Another future of healthcare professional education will likely be in simulation. Like other forms of technology outside of medicine, the cost of e-learning in healthcare professional education will fall rapidly. E-learning will also become more adaptive in the future and so will deliver educational content based on learners' exact needs. The future of e-learning will also be mobile. Increasingly in the future e-learning will be blended with face to face education.

Diverse strategy-learning styles promote cooperation in evolutionary spatial prisoner's dilemma game

NASA Astrophysics Data System (ADS)

Liu, Run-Ran; Jia, Chun-Xiao; Rong, Zhihai

2015-11-01

Observational learning and practice learning are two important learning styles and play important roles in our information acquisition. In this paper, we study a spacial evolutionary prisoner's dilemma game, where players can choose the observational learning rule or the practice learning rule when updating their strategies. In the proposed model, we use a parameter p controlling the preference of players choosing the observational learning rule, and found that there exists an optimal value of p leading to the highest cooperation level, which indicates that the cooperation can be promoted by these two learning rules collaboratively and one single learning rule is not favor the promotion of cooperation. By analysing the dynamical behavior of the system, we find that the observational learning rule can make the players residing on cooperative clusters more easily realize the bad sequence of mutual defection. However, a too high observational learning probability suppresses the players to form compact cooperative clusters. Our results highlight the importance of a strategy-updating rule, more importantly, the observational learning rule in the evolutionary cooperation.

Improving Grasp Skills Using Schema Structured Learning

NASA Technical Reports Server (NTRS)

Platt, Robert; Grupen, ROderic A.; Fagg, Andrew H.

2006-01-01

Abstract In the control-based approach to robotics, complex behavior is created by sequencing and combining control primitives. While it is desirable for the robot to autonomously learn the correct control sequence, searching through the large number of potential solutions can be time consuming. This paper constrains this search to variations of a generalized solution encoded in a framework known as an action schema. A new algorithm, SCHEMA STRUCTURED LEARNING, is proposed that repeatedly executes variations of the generalized solution in search of instantiations that satisfy action schema objectives. This approach is tested in a grasping task where Dexter, the UMass humanoid robot, learns which reaching and grasping controllers maximize the probability of grasp success.

Teaching Galileo? Get to Know Riccioli! What a Forgotten Italian Astronomer Can Teach Students About How Science Works

NASA Astrophysics Data System (ADS)

Graney, Christopher M.

2012-01-01

What can physics students learn about science from those scientists who got the answers wrong? Your students probably have encountered little science history. What they have encountered probably has portrayed scientists as ``The People with the Right Answers.'' But those who got the wrong answers can teach students that in science, answers are often elusive--not found in the back of a book or discovered in a bold stroke of genius.

Joint Information Theoretic and Differential Geometrical Approach for Robust Automated Target Recognition

DTIC Science & Technology

2012-02-29

surface and Swiss roll) and real-world data sets (UCI Machine Learning Repository [12] and USPS digit handwriting data). In our experiments, we use...less than µn ( say µ = 0.8), we can first use screening technique to select µn candidate nodes, and then apply BIPS on them for further selection and...identified from node j to node i. So we can say the probability for the existence of this connection is approximately 82%. Given the probability matrix

An appreciation of Richard Threlkeld Cox

NASA Astrophysics Data System (ADS)

Tribus, Myron

2002-05-01

Richard T. Cox's contributions to the foundations of probability theory and inductive logic are not generally appreciated or understood. This paper reviews his life and accomplishments, especially those in his book The Algebra of Probable Inference and his final publication Inference and Inquiry which, in this author's opinion, has the potential to influence in a significant way the design and analysis of self organizing systems which learn from experience. A simple application to the simulation of a neuron is presented as an example of the power of Cox's contribution.

Quantum Inference on Bayesian Networks

NASA Astrophysics Data System (ADS)

Yoder, Theodore; Low, Guang Hao; Chuang, Isaac

2014-03-01

Because quantum physics is naturally probabilistic, it seems reasonable to expect physical systems to describe probabilities and their evolution in a natural fashion. Here, we use quantum computation to speedup sampling from a graphical probability model, the Bayesian network. A specialization of this sampling problem is approximate Bayesian inference, where the distribution on query variables is sampled given the values e of evidence variables. Inference is a key part of modern machine learning and artificial intelligence tasks, but is known to be NP-hard. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time (nmP(e) - 1 / 2) , depending critically on P(e) , the probability the evidence might occur in the first place. However, by implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking (n2m P(e) -1/2) time per sample. The speedup is the result of amplitude amplification, which is proving to be broadly applicable in sampling and machine learning tasks. In particular, we provide an explicit and efficient circuit construction that implements the algorithm without the need for oracle access.

Learning with imperfectly labeled patterns

NASA Technical Reports Server (NTRS)

Chittineni, C. B.

1979-01-01

The problem of learning in pattern recognition using imperfectly labeled patterns is considered. The performance of the Bayes and nearest neighbor classifiers with imperfect labels is discussed using a probabilistic model for the mislabeling of the training patterns. Schemes for training the classifier using both parametric and non parametric techniques are presented. Methods for the correction of imperfect labels were developed. To gain an understanding of the learning process, expressions are derived for success probability as a function of training time for a one dimensional increment error correction classifier with imperfect labels. Feature selection with imperfectly labeled patterns is described.

Feature Discovery by Competitive Learning.

DTIC Science & Technology

1984-06-01

Probably the first such attempt occurred in 1951 when Dean Edmonds and Marvin Minsky built their learning machine. The flavor of this machine and...Bernstein, J. (1961). Profiles: Al, Marvin Minsky . The New Yorker. 57, 50-126. Bienenstock, E. L., Cooper, L. N., & Munro, P. W. (1982). Theory for the...This machine actually worked and was so fascinating to watch that Minsky remembers: We sort of quit science for awhile to watch the machine. We were

Error Discounting in Probabilistic Category Learning

PubMed Central

Craig, Stewart; Lewandowsky, Stephan; Little, Daniel R.

2011-01-01

Some current theories of probabilistic categorization assume that people gradually attenuate their learning in response to unavoidable error. However, existing evidence for this error discounting is sparse and open to alternative interpretations. We report two probabilistic-categorization experiments that investigated error discounting by shifting feedback probabilities to new values after different amounts of training. In both experiments, responding gradually became less responsive to errors, and learning was slowed for some time after the feedback shift. Both results are indicative of error discounting. Quantitative modeling of the data revealed that adding a mechanism for error discounting significantly improved the fits of an exemplar-based and a rule-based associative learning model, as well as of a recency-based model of categorization. We conclude that error discounting is an important component of probabilistic learning. PMID:21355666

Network congestion control algorithm based on Actor-Critic reinforcement learning model

NASA Astrophysics Data System (ADS)

Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

2018-04-01

Aiming at the network congestion control problem, a congestion control algorithm based on Actor-Critic reinforcement learning model is designed. Through the genetic algorithm in the congestion control strategy, the network congestion problems can be better found and prevented. According to Actor-Critic reinforcement learning, the simulation experiment of network congestion control algorithm is designed. The simulation experiments verify that the AQM controller can predict the dynamic characteristics of the network system. Moreover, the learning strategy is adopted to optimize the network performance, and the dropping probability of packets is adaptively adjusted so as to improve the network performance and avoid congestion. Based on the above finding, it is concluded that the network congestion control algorithm based on Actor-Critic reinforcement learning model can effectively avoid the occurrence of TCP network congestion.

Simulation of noisy dynamical system by Deep Learning

NASA Astrophysics Data System (ADS)

Yeo, Kyongmin

2017-11-01

Deep learning has attracted huge attention due to its powerful representation capability. However, most of the studies on deep learning have been focused on visual analytics or language modeling and the capability of the deep learning in modeling dynamical systems is not well understood. In this study, we use a recurrent neural network to model noisy nonlinear dynamical systems. In particular, we use a long short-term memory (LSTM) network, which constructs internal nonlinear dynamics systems. We propose a cross-entropy loss with spatial ridge regularization to learn a non-stationary conditional probability distribution from a noisy nonlinear dynamical system. A Monte Carlo procedure to perform time-marching simulations by using the LSTM is presented. The behavior of the LSTM is studied by using noisy, forced Van der Pol oscillator and Ikeda equation.

Dynamics of EEG functional connectivity during statistical learning.

PubMed

Tóth, Brigitta; Janacsek, Karolina; Takács, Ádám; Kóbor, Andrea; Zavecz, Zsófia; Nemeth, Dezso

2017-10-01

Statistical learning is a fundamental mechanism of the brain, which extracts and represents regularities of our environment. Statistical learning is crucial in predictive processing, and in the acquisition of perceptual, motor, cognitive, and social skills. Although previous studies have revealed competitive neurocognitive processes underlying statistical learning, the neural communication of the related brain regions (functional connectivity, FC) has not yet been investigated. The present study aimed to fill this gap by investigating FC networks that promote statistical learning in humans. Young adults (N=28) performed a statistical learning task while 128-channels EEG was acquired. The task involved probabilistic sequences, which enabled to measure incidental/implicit learning of conditional probabilities. Phase synchronization in seven frequency bands was used to quantify FC between cortical regions during the first, second, and third periods of the learning task, respectively. Here we show that statistical learning is negatively correlated with FC of the anterior brain regions in slow (theta) and fast (beta) oscillations. These negative correlations increased as the learning progressed. Our findings provide evidence that dynamic antagonist brain networks serve a hallmark of statistical learning. Copyright © 2017 Elsevier Inc. All rights reserved.

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

PubMed Central

Chen, Ching-Tai; Peng, Hung-Pin; Jian, Jhih-Wei; Tsai, Keng-Chang; Chang, Jeng-Yih; Yang, Ei-Wen; Chen, Jun-Bo; Ho, Shinn-Ying; Hsu, Wen-Lian; Yang, An-Suei

2012-01-01

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors. PMID:22701576

A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA

USGS Publications Warehouse

Ransom, Katherine M.; Nolan, Bernard T.; Traum, Jonathan A.; Faunt, Claudia; Bell, Andrew M.; Gronberg, Jo Ann M.; Wheeler, David C.; Zamora, Celia; Jurgens, Bryant; Schwarz, Gregory E.; Belitz, Kenneth; Eberts, Sandra; Kourakos, George; Harter, Thomas

2017-01-01

Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500 m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50 ppb and probability of dissolved oxygen concentration to be below 0.5 ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971–2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative impact on nitrate predictions. Three-dimensional visualization indicates that nitrate predictions depend on the probability of anoxic conditions and other factors, and that nitrate predictions generally decreased with increasing groundwater age.

A population-based tissue probability map-driven level set method for fully automated mammographic density estimations.

PubMed

Kim, Youngwoo; Hong, Byung Woo; Kim, Seung Ja; Kim, Jong Hyo

2014-07-01

A major challenge when distinguishing glandular tissues on mammograms, especially for area-based estimations, lies in determining a boundary on a hazy transition zone from adipose to glandular tissues. This stems from the nature of mammography, which is a projection of superimposed tissues consisting of different structures. In this paper, the authors present a novel segmentation scheme which incorporates the learned prior knowledge of experts into a level set framework for fully automated mammographic density estimations. The authors modeled the learned knowledge as a population-based tissue probability map (PTPM) that was designed to capture the classification of experts' visual systems. The PTPM was constructed using an image database of a selected population consisting of 297 cases. Three mammogram experts extracted regions for dense and fatty tissues on digital mammograms, which was an independent subset used to create a tissue probability map for each ROI based on its local statistics. This tissue class probability was taken as a prior in the Bayesian formulation and was incorporated into a level set framework as an additional term to control the evolution and followed the energy surface designed to reflect experts' knowledge as well as the regional statistics inside and outside of the evolving contour. A subset of 100 digital mammograms, which was not used in constructing the PTPM, was used to validate the performance. The energy was minimized when the initial contour reached the boundary of the dense and fatty tissues, as defined by experts. The correlation coefficient between mammographic density measurements made by experts and measurements by the proposed method was 0.93, while that with the conventional level set was 0.47. The proposed method showed a marked improvement over the conventional level set method in terms of accuracy and reliability. This result suggests that the proposed method successfully incorporated the learned knowledge of the experts' visual systems and has potential to be used as an automated and quantitative tool for estimations of mammographic breast density levels.

Lessons Learned about Best Practices for Communicating Earthquake Forecasting and Early Warning to Non-Scientific Publics

NASA Astrophysics Data System (ADS)

Sellnow, D. D.; Sellnow, T. L.

2017-12-01

Earthquake scientists are without doubt experts in understanding earthquake probabilities, magnitudes, and intensities, as well as the potential consequences of them to community infrastructures and inhabitants. One critical challenge these scientific experts face, however, rests with communicating what they know to the people they want to help. Helping scientists translate scientific information to non-scientists is something Drs. Tim and Deanna Sellnow have been committed to for decades. As such, they have compiled a host of data-driven best practices for communicating effectively to non-scientific publics about earthquake forecasting, probabilities, and warnings. In this session, they will summarize what they have learned as it may help earthquake scientists, emergency managers, and other key spokespersons share these important messages to disparate publics in ways that result in positive outcomes, the most important of which is saving lives.

Improving deep convolutional neural networks with mixed maxout units.

PubMed

Zhao, Hui-Zhen; Liu, Fu-Xian; Li, Long-Yue

2017-01-01

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that "non-maximal features are unable to deliver" and "feature mapping subspace pooling is insufficient," we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN) model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance.

Distinct discrimination learning strategies and their relation with spatial memory and attentional control in 4- to 14-year-olds.

PubMed

Schmittmann, Verena D; van der Maas, Han L J; Raijmakers, Maartje E J

2012-04-01

Behavioral, psychophysiological, and neuropsychological studies have revealed large developmental differences in various learning paradigms where learning from positive and negative feedback is essential. The differences are possibly due to the use of distinct strategies that may be related to spatial working memory and attentional control. In this study, strategies in performing a discrimination learning task were distinguished in a cross-sectional sample of 302 children from 4 to 14 years of age. The trial-by-trial accuracy data were analyzed with mathematical learning models. The best-fitting model revealed three learning strategies: hypothesis testing, slow abrupt learning, and nonlearning. The proportion of hypothesis-testing children increased with age. Nonlearners were present only in the youngest age group. Feature preferences for the irrelevant dimension had a detrimental effect on performance in the youngest age group. The executive functions spatial working memory and attentional control significantly predicted posterior learning strategy probabilities after controlling for age. Copyright © 2011 Elsevier Inc. All rights reserved.

A Bayesian Active Learning Experimental Design for Inferring Signaling Networks.

PubMed

Ness, Robert O; Sachs, Karen; Mallick, Parag; Vitek, Olga

2018-06-21

Machine learning methods for learning network structure are applied to quantitative proteomics experiments and reverse-engineer intracellular signal transduction networks. They provide insight into the rewiring of signaling within the context of a disease or a phenotype. To learn the causal patterns of influence between proteins in the network, the methods require experiments that include targeted interventions that fix the activity of specific proteins. However, the interventions are costly and add experimental complexity. We describe an active learning strategy for selecting optimal interventions. Our approach takes as inputs pathway databases and historic data sets, expresses them in form of prior probability distributions on network structures, and selects interventions that maximize their expected contribution to structure learning. Evaluations on simulated and real data show that the strategy reduces the detection error of validated edges as compared with an unguided choice of interventions and avoids redundant interventions, thereby increasing the effectiveness of the experiment.

Development, direction, and damage limitation: social learning in domestic fowl.

PubMed

Nicol, Christine J

2004-02-01

This review highlights two areas of particular interest in the study of social learning in fowl. First, the role of social learning in the development of feeding and foraging behavior in young chicks and older birds is described. The role of the hen as a demonstrator and possible teacher is considered, and the subsequent social influence of brood mates and other companions on food avoidance and food preference learning is discussed. Second, the way in which work on domestic fowl has contributed to an understanding of the importance of directed social learning is examined. The well-characterized hierarchical social organization of small chicken flocks has been used to design studies which demonstrate that the probability of social transmission is strongly influenced by social relationships between birds. The practical implications of understanding the role of social learning in the spread of injurious behaviors in this economically important species are briefly considered.

Enhancing understanding and improving prediction of severe weather through spatiotemporal relational learning.

PubMed

McGovern, Amy; Gagne, David J; Williams, John K; Brown, Rodger A; Basara, Jeffrey B

Severe weather, including tornadoes, thunderstorms, wind, and hail annually cause significant loss of life and property. We are developing spatiotemporal machine learning techniques that will enable meteorologists to improve the prediction of these events by improving their understanding of the fundamental causes of the phenomena and by building skillful empirical predictive models. In this paper, we present significant enhancements of our Spatiotemporal Relational Probability Trees that enable autonomous discovery of spatiotemporal relationships as well as learning with arbitrary shapes. We focus our evaluation on two real-world case studies using our technique: predicting tornadoes in Oklahoma and predicting aircraft turbulence in the United States. We also discuss how to evaluate success for a machine learning algorithm in the severe weather domain, which will enable new methods such as ours to transfer from research to operations, provide a set of lessons learned for embedded machine learning applications, and discuss how to field our technique.

Rats bred for helplessness exhibit positive reinforcement learning deficits which are not alleviated by an antidepressant dose of the MAO-B inhibitor deprenyl.

PubMed

Schulz, Daniela; Henn, Fritz A; Petri, David; Huston, Joseph P

2016-08-04

Principles of negative reinforcement learning may play a critical role in the etiology and treatment of depression. We examined the integrity of positive reinforcement learning in congenitally helpless (cH) rats, an animal model of depression, using a random ratio schedule and a devaluation-extinction procedure. Furthermore, we tested whether an antidepressant dose of the monoamine oxidase (MAO)-B inhibitor deprenyl would reverse any deficits in positive reinforcement learning. We found that cH rats (n=9) were impaired in the acquisition of even simple operant contingencies, such as a fixed interval (FI) 20 schedule. cH rats exhibited no apparent deficits in appetite or reward sensitivity. They reacted to the devaluation of food in a manner consistent with a dose-response relationship. Reinforcer motivation as assessed by lever pressing across sessions with progressively decreasing reward probabilities was highest in congenitally non-helpless (cNH, n=10) rats as long as the reward probabilities remained relatively high. cNH compared to wild-type (n=10) rats were also more resistant to extinction across sessions. Compared to saline (n=5), deprenyl (n=5) reduced the duration of immobility of cH rats in the forced swimming test, indicative of antidepressant effects, but did not restore any deficits in the acquisition of a FI 20 schedule. We conclude that positive reinforcement learning was impaired in rats bred for helplessness, possibly due to motivational impairments but not deficits in reward sensitivity, and that deprenyl exerted antidepressant effects but did not reverse the deficits in positive reinforcement learning. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Statistical Regularities Attract Attention when Task-Relevant.

PubMed

Alamia, Andrea; Zénon, Alexandre

2016-01-01

Visual attention seems essential for learning the statistical regularities in our environment, a process known as statistical learning. However, how attention is allocated when exploring a novel visual scene whose statistical structure is unknown remains unclear. In order to address this question, we investigated visual attention allocation during a task in which we manipulated the conditional probability of occurrence of colored stimuli, unbeknown to the subjects. Participants were instructed to detect a target colored dot among two dots moving along separate circular paths. We evaluated implicit statistical learning, i.e., the effect of color predictability on reaction times (RTs), and recorded eye position concurrently. Attention allocation was indexed by comparing the Mahalanobis distance between the position, velocity and acceleration of the eyes and the two colored dots. We found that learning the conditional probabilities occurred very early during the course of the experiment as shown by the fact that, starting already from the first block, predictable stimuli were detected with shorter RT than unpredictable ones. In terms of attentional allocation, we found that the predictive stimulus attracted gaze only when it was informative about the occurrence of the target but not when it predicted the occurrence of a task-irrelevant stimulus. This suggests that attention allocation was influenced by regularities only when they were instrumental in performing the task. Moreover, we found that the attentional bias towards task-relevant predictive stimuli occurred at a very early stage of learning, concomitantly with the first effects of learning on RT. In conclusion, these results show that statistical regularities capture visual attention only after a few occurrences, provided these regularities are instrumental to perform the task.

Phase transition of social learning collectives and the echo chamber.

PubMed

Mori, Shintaro; Nakayama, Kazuaki; Hisakado, Masato

2016-11-01

We study a simple model for social learning agents in a restless multiarmed bandit. There are N agents, and the bandit has M good arms that change to bad with the probability q_{c}/N. If the agents do not know a good arm, they look for it by a random search (with the success probability q_{I}) or copy the information of other agents' good arms (with the success probability q_{O}) with probabilities 1-p or p, respectively. The distribution of the agents in M good arms obeys the Yule distribution with the power-law exponent 1+γ in the limit N,M→∞, and γ=1+(1-p)q_{I}/pq_{O}. The system shows a phase transition at p_{c}=q_{I}/q_{I}+q_{o}. For pp_{c}), the variance of N_{1} per agent is finite (diverges as ∝N^{2-γ} with N). There is a threshold value N_{s} for the system size that scales as lnN_{s}∝1/(γ-1). The expected value of the number of the agents with a good arm N_{1} increases with p for N>N_{s}. For p>p_{c} and N

Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural Cox model context.

PubMed

Karim, Mohammad Ehsanul; Platt, Robert W

2017-06-15

Correct specification of the inverse probability weighting (IPW) model is necessary for consistent inference from a marginal structural Cox model (MSCM). In practical applications, researchers are typically unaware of the true specification of the weight model. Nonetheless, IPWs are commonly estimated using parametric models, such as the main-effects logistic regression model. In practice, assumptions underlying such models may not hold and data-adaptive statistical learning methods may provide an alternative. Many candidate statistical learning approaches are available in the literature. However, the optimal approach for a given dataset is impossible to predict. Super learner (SL) has been proposed as a tool for selecting an optimal learner from a set of candidates using cross-validation. In this study, we evaluate the usefulness of a SL in estimating IPW in four different MSCM simulation scenarios, in which we varied the specification of the true weight model specification (linear and/or additive). Our simulations show that, in the presence of weight model misspecification, with a rich and diverse set of candidate algorithms, SL can generally offer a better alternative to the commonly used statistical learning approaches in terms of MSE as well as the coverage probabilities of the estimated effect in an MSCM. The findings from the simulation studies guided the application of the MSCM in a multiple sclerosis cohort from British Columbia, Canada (1995-2008), to estimate the impact of beta-interferon treatment in delaying disability progression. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Event-related potential studies of outcome processing and feedback-guided learning.

PubMed

San Martín, René

2012-01-01

In order to control behavior in an adaptive manner the brain has to learn how some situations and actions predict positive or negative outcomes. During the last decade cognitive neuroscientists have shown that the brain is able to evaluate and learn from outcomes within a few hundred milliseconds of their occurrence. This research has been primarily focused on the feedback-related negativity (FRN) and the P3, two event-related potential (ERP) components that are elicited by outcomes. The FRN is a frontally distributed negative-polarity ERP component that typically reaches its maximal amplitude 250 ms after outcome presentation and tends to be larger for negative than for positive outcomes. The FRN has been associated with activity in the anterior cingulate cortex (ACC). The P3 (~300-600 ms) is a parietally distributed positive-polarity ERP component that tends to be larger for large magnitude than for small magnitude outcomes. The neural sources of the P3 are probably distributed over different regions of the cortex. This paper examines the theories that have been proposed to explain the functional role of these two ERP components during outcome processing. Special attention is paid to extant literature addressing how these ERP components are modulated by outcome valence (negative vs. positive), outcome magnitude (large vs. small), outcome probability (unlikely vs. likely), and behavioral adjustment. The literature offers few generalizable conclusions, but is beset with a number of inconsistencies across studies. This paper discusses the potential reasons for these inconsistencies and points out some challenges that probably will shape the field over the next decade.

What is epistemic value in free energy models of learning and acting? A bounded rationality perspective.

PubMed

Ortega, Pedro A; Braun, Daniel A

2015-01-01

Free energy models of learning and acting do not only care about utility or extrinsic value, but also about intrinsic value, that is, the information value stemming from probability distributions that represent beliefs or strategies. While these intrinsic values can be interpreted as epistemic values or exploration bonuses under certain conditions, the framework of bounded rationality offers a complementary interpretation in terms of information-processing costs that we discuss here.

Cerebellar associative sensory learning defects in five mouse autism models

PubMed Central

Kloth, Alexander D; Badura, Aleksandra; Li, Amy; Cherskov, Adriana; Connolly, Sara G; Giovannucci, Andrea; Bangash, M Ali; Grasselli, Giorgio; Peñagarikano, Olga; Piochon, Claire; Tsai, Peter T; Geschwind, Daniel H; Hansel, Christian; Sahin, Mustafa; Takumi, Toru; Worley, Paul F; Wang, Samuel S-H

2015-01-01

Sensory integration difficulties have been reported in autism, but their underlying brain-circuit mechanisms are underexplored. Using five autism-related mouse models, Shank3+/ΔC, Mecp2R308/Y, Cntnap2−/−, L7-Tsc1 (L7/Pcp2Cre::Tsc1flox/+), and patDp(15q11-13)/+, we report specific perturbations in delay eyeblink conditioning, a form of associative sensory learning requiring cerebellar plasticity. By distinguishing perturbations in the probability and characteristics of learned responses, we found that probability was reduced in Cntnap2−/−, patDp(15q11-13)/+, and L7/Pcp2Cre::Tsc1flox/+, which are associated with Purkinje-cell/deep-nuclear gene expression, along with Shank3+/ΔC. Amplitudes were smaller in L7/Pcp2Cre::Tsc1flox/+ as well as Shank3+/ΔC and Mecp2R308/Y, which are associated with granule cell pathway expression. Shank3+/ΔC and Mecp2R308/Y also showed aberrant response timing and reduced Purkinje-cell dendritic spine density. Overall, our observations are potentially accounted for by defects in instructed learning in the olivocerebellar loop and response representation in the granule cell pathway. Our findings indicate that defects in associative temporal binding of sensory events are widespread in autism mouse models. DOI: http://dx.doi.org/10.7554/eLife.06085.001 PMID:26158416

HDSC/OWP

Science.gov Websites

Maximum Precipitation Documents Miscellaneous Publications Storm Analysis Record Precipitation Contact Us ; - Probability analysis for selected historical storm events learn more > - Record point precipitation for the Oceanic and Atmospheric Administration National Weather Service Office of Water Prediction (OWP) 1325 East

Pointwise probability reinforcements for robust statistical inference.

PubMed

Frénay, Benoît; Verleysen, Michel

2014-02-01

Statistical inference using machine learning techniques may be difficult with small datasets because of abnormally frequent data (AFDs). AFDs are observations that are much more frequent in the training sample that they should be, with respect to their theoretical probability, and include e.g. outliers. Estimates of parameters tend to be biased towards models which support such data. This paper proposes to introduce pointwise probability reinforcements (PPRs): the probability of each observation is reinforced by a PPR and a regularisation allows controlling the amount of reinforcement which compensates for AFDs. The proposed solution is very generic, since it can be used to robustify any statistical inference method which can be formulated as a likelihood maximisation. Experiments show that PPRs can be easily used to tackle regression, classification and projection: models are freed from the influence of outliers. Moreover, outliers can be filtered manually since an abnormality degree is obtained for each observation. Copyright © 2013 Elsevier Ltd. All rights reserved.

Risk management.

PubMed

Chambers, David W

2010-01-01

Every plan contains risk. To proceed without planning some means of managing that risk is to court failure. The basic logic of risk is explained. It consists in identifying a threshold where some corrective action is necessary, the probability of exceeding that threshold, and the attendant cost should the undesired outcome occur. This is the probable cost of failure. Various risk categories in dentistry are identified, including lack of liquidity; poor quality; equipment or procedure failures; employee slips; competitive environments; new regulations; unreliable suppliers, partners, and patients; and threats to one's reputation. It is prudent to make investments in risk management to the extent that the cost of managing the risk is less than the probable loss due to risk failure and when risk management strategies can be matched to type of risk. Four risk management strategies are discussed: insurance, reducing the probability of failure, reducing the costs of failure, and learning. A risk management accounting of the financial meltdown of October 2008 is provided.

Testing Sleep Consolidation in Skill Learning: A Field Study Using an Online Game.

PubMed

Stafford, Tom; Haasnoot, Erwin

2017-04-01

Using an observational sample of players of a simple online game (n > 1.2 million), we are able to trace the development of skill in that game. Information on playing time, and player location, allows us to estimate time of day during which practice took place. We compare those whose breaks in practice probably contained a night's sleep and those whose breaks in practice probably did not contain a night's sleep. Our analysis confirms experimental evidence showing a benefit of spacing for skill learning, but it fails to find any additional benefit of sleeping during a break from practice. We discuss reasons why the well-established phenomenon of sleep consolidation might not manifest in an observational study of skill development. We put the spacing effect into the context of the other known influences on skill learning: improvement with practice, and individual differences in initial performance. Analysis of performance data from games allows experimental results to be demonstrated outside of the lab and for experimental phenomenon to be put in the context of the performance of the whole task. Copyright © 2016 Cognitive Science Society, Inc.

An investigation of student understanding of classical ideas related to quantum mechanics: Potential energy diagrams and spatial probability density

NASA Astrophysics Data System (ADS)

Stephanik, Brian Michael

This dissertation describes the results of two related investigations into introductory student understanding of ideas from classical physics that are key elements of quantum mechanics. One investigation probes the extent to which students are able to interpret and apply potential energy diagrams (i.e., graphs of potential energy versus position). The other probes the extent to which students are able to reason classically about probability and spatial probability density. The results of these investigations revealed significant conceptual and reasoning difficulties that students encounter with these topics. The findings guided the design of instructional materials to address the major problems. Results from post-instructional assessments are presented that illustrate the impact of the curricula on student learning.

Bayesian learning

NASA Technical Reports Server (NTRS)

Denning, Peter J.

1989-01-01

In 1983 and 1984, the Infrared Astronomical Satellite (IRAS) detected 5,425 stellar objects and measured their infrared spectra. In 1987 a program called AUTOCLASS used Bayesian inference methods to discover the classes present in these data and determine the most probable class of each object, revealing unknown phenomena in astronomy. AUTOCLASS has rekindled the old debate on the suitability of Bayesian methods, which are computationally intensive, interpret probabilities as plausibility measures rather than frequencies, and appear to depend on a subjective assessment of the probability of a hypothesis before the data were collected. Modern statistical methods have, however, recently been shown to also depend on subjective elements. These debates bring into question the whole tradition of scientific objectivity and offer scientists a new way to take responsibility for their findings and conclusions.

Multi-sensor physical activity recognition in free-living.

PubMed

Ellis, Katherine; Godbole, Suneeta; Kerr, Jacqueline; Lanckriet, Gert

Physical activity monitoring in free-living populations has many applications for public health research, weight-loss interventions, context-aware recommendation systems and assistive technologies. We present a system for physical activity recognition that is learned from a free-living dataset of 40 women who wore multiple sensors for seven days. The multi-level classification system first learns low-level codebook representations for each sensor and uses a random forest classifier to produce minute-level probabilities for each activity class. Then a higher-level HMM layer learns patterns of transitions and durations of activities over time to smooth the minute-level predictions. [Formula: see text].

Does Education Need a Copernicus?

ERIC Educational Resources Information Center

Keller, George

1986-01-01

The changes in the U.S. population and society in the last 70 years have been so substantial that the old system of school and university learning needs more than tinkering and probably requires bold, imaginative recasting for a new age. (Author/MSE)

Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model

PubMed Central

Guan, Li; Hao, Bibo; Cheng, Qijin; Yip, Paul SF

2015-01-01

Background Traditional offline assessment of suicide probability is time consuming and difficult in convincing at-risk individuals to participate. Identifying individuals with high suicide probability through online social media has an advantage in its efficiency and potential to reach out to hidden individuals, yet little research has been focused on this specific field. Objective The objective of this study was to apply two classification models, Simple Logistic Regression (SLR) and Random Forest (RF), to examine the feasibility and effectiveness of identifying high suicide possibility microblog users in China through profile and linguistic features extracted from Internet-based data. Methods There were nine hundred and nine Chinese microblog users that completed an Internet survey, and those scoring one SD above the mean of the total Suicide Probability Scale (SPS) score, as well as one SD above the mean in each of the four subscale scores in the participant sample were labeled as high-risk individuals, respectively. Profile and linguistic features were fed into two machine learning algorithms (SLR and RF) to train the model that aims to identify high-risk individuals in general suicide probability and in its four dimensions. Models were trained and then tested by 5-fold cross validation; in which both training set and test set were generated under the stratified random sampling rule from the whole sample. There were three classic performance metrics (Precision, Recall, F1 measure) and a specifically defined metric “Screening Efficiency” that were adopted to evaluate model effectiveness. Results Classification performance was generally matched between SLR and RF. Given the best performance of the classification models, we were able to retrieve over 70% of the labeled high-risk individuals in overall suicide probability as well as in the four dimensions. Screening Efficiency of most models varied from 1/4 to 1/2. Precision of the models was generally below 30%. Conclusions Individuals in China with high suicide probability are recognizable by profile and text-based information from microblogs. Although there is still much space to improve the performance of classification models in the future, this study may shed light on preliminary screening of risky individuals via machine learning algorithms, which can work side-by-side with expert scrutiny to increase efficiency in large-scale-surveillance of suicide probability from online social media. PMID:26543921

Identifying Chinese Microblog Users With High Suicide Probability Using Internet-Based Profile and Linguistic Features: Classification Model.

PubMed

Guan, Li; Hao, Bibo; Cheng, Qijin; Yip, Paul Sf; Zhu, Tingshao

2015-01-01

Traditional offline assessment of suicide probability is time consuming and difficult in convincing at-risk individuals to participate. Identifying individuals with high suicide probability through online social media has an advantage in its efficiency and potential to reach out to hidden individuals, yet little research has been focused on this specific field. The objective of this study was to apply two classification models, Simple Logistic Regression (SLR) and Random Forest (RF), to examine the feasibility and effectiveness of identifying high suicide possibility microblog users in China through profile and linguistic features extracted from Internet-based data. There were nine hundred and nine Chinese microblog users that completed an Internet survey, and those scoring one SD above the mean of the total Suicide Probability Scale (SPS) score, as well as one SD above the mean in each of the four subscale scores in the participant sample were labeled as high-risk individuals, respectively. Profile and linguistic features were fed into two machine learning algorithms (SLR and RF) to train the model that aims to identify high-risk individuals in general suicide probability and in its four dimensions. Models were trained and then tested by 5-fold cross validation; in which both training set and test set were generated under the stratified random sampling rule from the whole sample. There were three classic performance metrics (Precision, Recall, F1 measure) and a specifically defined metric "Screening Efficiency" that were adopted to evaluate model effectiveness. Classification performance was generally matched between SLR and RF. Given the best performance of the classification models, we were able to retrieve over 70% of the labeled high-risk individuals in overall suicide probability as well as in the four dimensions. Screening Efficiency of most models varied from 1/4 to 1/2. Precision of the models was generally below 30%. Individuals in China with high suicide probability are recognizable by profile and text-based information from microblogs. Although there is still much space to improve the performance of classification models in the future, this study may shed light on preliminary screening of risky individuals via machine learning algorithms, which can work side-by-side with expert scrutiny to increase efficiency in large-scale-surveillance of suicide probability from online social media.

Cannon Reservoir Human Ecology Project. Volume 1. Euro-American Pioneer Settlement Systems in the Central Salt River Valley of Northeast Missouri.

DTIC Science & Technology

1984-01-01

entry were facturing; " learned persons" in each household also are recorded for each of the 16 quarter- quarter sections in identified. It appears that...probably were unmarried adult siblings most frequent of which probably was composed of two of the head of the household, and/or older persons, generations...family" also have been noted in frontier forging of iron implements required a specialist-the northern Texas, where young adult males delayed mar- local

Fast Nonparametric Machine Learning Algorithms for High-Dimensional Massive Data and Applications

DTIC Science & Technology

2006-03-01

know the probability of that from Lemma 2. Using the union bound, we know that for any query q, the probability that i-am-feeling-lucky search algorithm...and each point in a d-dimensional space, a naive k-NN search needs to do a linear scan of T for every single query q, and thus the computational time...algorithm based on partition trees with priority search , and give an expected query time O((1/)d log n). But the constant in the O((1/)d log n

Impact of censoring on learning Bayesian networks in survival modelling.

PubMed

Stajduhar, Ivan; Dalbelo-Basić, Bojana; Bogunović, Nikola

2009-11-01

Bayesian networks are commonly used for presenting uncertainty and covariate interactions in an easily interpretable way. Because of their efficient inference and ability to represent causal relationships, they are an excellent choice for medical decision support systems in diagnosis, treatment, and prognosis. Although good procedures for learning Bayesian networks from data have been defined, their performance in learning from censored survival data has not been widely studied. In this paper, we explore how to use these procedures to learn about possible interactions between prognostic factors and their influence on the variate of interest. We study how censoring affects the probability of learning correct Bayesian network structures. Additionally, we analyse the potential usefulness of the learnt models for predicting the time-independent probability of an event of interest. We analysed the influence of censoring with a simulation on synthetic data sampled from randomly generated Bayesian networks. We used two well-known methods for learning Bayesian networks from data: a constraint-based method and a score-based method. We compared the performance of each method under different levels of censoring to those of the naive Bayes classifier and the proportional hazards model. We did additional experiments on several datasets from real-world medical domains. The machine-learning methods treated censored cases in the data as event-free. We report and compare results for several commonly used model evaluation metrics. On average, the proportional hazards method outperformed other methods in most censoring setups. As part of the simulation study, we also analysed structural similarities of the learnt networks. Heavy censoring, as opposed to no censoring, produces up to a 5% surplus and up to 10% missing total arcs. It also produces up to 50% missing arcs that should originally be connected to the variate of interest. Presented methods for learning Bayesian networks from data can be used to learn from censored survival data in the presence of light censoring (up to 20%) by treating censored cases as event-free. Given intermediate or heavy censoring, the learnt models become tuned to the majority class and would thus require a different approach.

Statistical characteristics of climbing fiber spikes necessary for efficient cerebellar learning.

PubMed

Kuroda, S; Yamamoto, K; Miyamoto, H; Doya, K; Kawat, M

2001-03-01

Mean firing rates (MFRs), with analogue values, have thus far been used as information carriers of neurons in most brain theories of learning. However, the neurons transmit the signal by spikes, which are discrete events. The climbing fibers (CFs), which are known to be essential for cerebellar motor learning, fire at the ultra-low firing rates (around 1 Hz), and it is not yet understood theoretically how high-frequency information can be conveyed and how learning of smooth and fast movements can be achieved. Here we address whether cerebellar learning can be achieved by CF spikes instead of conventional MFR in an eye movement task, such as the ocular following response (OFR), and an arm movement task. There are two major afferents into cerebellar Purkinje cells: parallel fiber (PF) and CF, and the synaptic weights between PFs and Purkinje cells have been shown to be modulated by the stimulation of both types of fiber. The modulation of the synaptic weights is regulated by the cerebellar synaptic plasticity. In this study we simulated cerebellar learning using CF signals as spikes instead of conventional MFR. To generate the spikes we used the following four spike generation models: (1) a Poisson model in which the spike interval probability follows a Poisson distribution, (2) a gamma model in which the spike interval probability follows the gamma distribution, (3) a max model in which a spike is generated when a synaptic input reaches maximum, and (4) a threshold model in which a spike is generated when the input crosses a certain small threshold. We found that, in an OFR task with a constant visual velocity, learning was successful with stochastic models, such as Poisson and gamma models, but not in the deterministic models, such as max and threshold models. In an OFR with a stepwise velocity change and an arm movement task, learning could be achieved only in the Poisson model. In addition, for efficient cerebellar learning, the distribution of CF spike-occurrence time after stimulus onset must capture at least the first, second and third moments of the temporal distribution of error signals.

Neural Encoding and Integration of Learned Probabilistic Sequences in Avian Sensory-Motor Circuitry

PubMed Central

Brainard, Michael S.

2013-01-01

Many complex behaviors, such as human speech and birdsong, reflect a set of categorical actions that can be flexibly organized into variable sequences. However, little is known about how the brain encodes the probabilities of such sequences. Behavioral sequences are typically characterized by the probability of transitioning from a given action to any subsequent action (which we term “divergence probability”). In contrast, we hypothesized that neural circuits might encode the probability of transitioning to a given action from any preceding action (which we term “convergence probability”). The convergence probability of repeatedly experienced sequences could naturally become encoded by Hebbian plasticity operating on the patterns of neural activity associated with those sequences. To determine whether convergence probability is encoded in the nervous system, we investigated how auditory-motor neurons in vocal premotor nucleus HVC of songbirds encode different probabilistic characterizations of produced syllable sequences. We recorded responses to auditory playback of pseudorandomly sequenced syllables from the bird's repertoire, and found that variations in responses to a given syllable could be explained by a positive linear dependence on the convergence probability of preceding sequences. Furthermore, convergence probability accounted for more response variation than other probabilistic characterizations, including divergence probability. Finally, we found that responses integrated over >7–10 syllables (∼700–1000 ms) with the sign, gain, and temporal extent of integration depending on convergence probability. Our results demonstrate that convergence probability is encoded in sensory-motor circuitry of the song-system, and suggest that encoding of convergence probability is a general feature of sensory-motor circuits. PMID:24198363

Lessons Learned from Dependency Usage in HERA: Implications for THERP-Related HRA Methods

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

April M. Whaley; Ronald L. Boring; Harold S. Blackman

Dependency occurs when the probability of success or failure on one action changes the probability of success or failure on a subsequent action. Dependency may serve as a modifier on the human error probabilities (HEPs) for successive actions in human reliability analysis (HRA) models. Discretion should be employed when determining whether or not a dependency calculation is warranted: dependency should not be assigned without strongly grounded reasons. Human reliability analysts may sometimes assign dependency in cases where it is unwarranted. This inappropriate assignment is attributed to a lack of clear guidance to encompass the range of scenarios human reliability analystsmore » are addressing. Inappropriate assignment of dependency produces inappropriately elevated HEP values. Lessons learned about dependency usage in the Human Event Repository and Analysis (HERA) system may provide clarification and guidance for analysts using first-generation HRA methods. This paper presents the HERA approach to dependency assessment and discusses considerations for dependency usage in HRA, including the cognitive basis for dependency, direction for determining when dependency should be assessed, considerations for determining the dependency level, temporal issues to consider when assessing dependency, (e.g., considering task sequence versus overall event sequence, and dependency over long periods of time), and diagnosis and action influences on dependency.« less

Testing the limits of optimality: the effect of base rates in the Monty Hall dilemma.

PubMed

Herbranson, Walter T; Wang, Shanglun

2014-03-01

The Monty Hall dilemma is a probability puzzle in which a player tries to guess which of three doors conceals a desirable prize. After an initial selection, one of the nonchosen doors is opened, revealing that it is not a winner, and the player is given the choice of staying with the initial selection or switching to the other remaining door. Pigeons and humans were tested on two variants of the Monty Hall dilemma, in which one of the three doors had either a higher or a lower chance of containing the prize than did the other two options. The optimal strategy in both cases was to initially choose the lowest-probability door available and then switch away from it. Whereas pigeons learned to approximate the optimal strategy, humans failed to do so on both accounts: They did not show a preference for low-probability options, and they did not consistently switch. An analysis of performance over the course of training indicated that pigeons learned to perform a sequence of responses on each trial, and that sequence was one that yielded the highest possible rate of reinforcement. Humans, in contrast, continued to vary their responses throughout the experiment, possibly in search of a more complex strategy that would exceed the maximum possible win rate.

Correcting Classifiers for Sample Selection Bias in Two-Phase Case-Control Studies

PubMed Central

Theis, Fabian J.

2017-01-01

Epidemiological studies often utilize stratified data in which rare outcomes or exposures are artificially enriched. This design can increase precision in association tests but distorts predictions when applying classifiers on nonstratified data. Several methods correct for this so-called sample selection bias, but their performance remains unclear especially for machine learning classifiers. With an emphasis on two-phase case-control studies, we aim to assess which corrections to perform in which setting and to obtain methods suitable for machine learning techniques, especially the random forest. We propose two new resampling-based methods to resemble the original data and covariance structure: stochastic inverse-probability oversampling and parametric inverse-probability bagging. We compare all techniques for the random forest and other classifiers, both theoretically and on simulated and real data. Empirical results show that the random forest profits from only the parametric inverse-probability bagging proposed by us. For other classifiers, correction is mostly advantageous, and methods perform uniformly. We discuss consequences of inappropriate distribution assumptions and reason for different behaviors between the random forest and other classifiers. In conclusion, we provide guidance for choosing correction methods when training classifiers on biased samples. For random forests, our method outperforms state-of-the-art procedures if distribution assumptions are roughly fulfilled. We provide our implementation in the R package sambia. PMID:29312464

Contingency bias in probability judgement may arise from ambiguity regarding additional causes.

PubMed

Mitchell, Chris J; Griffiths, Oren; More, Pranjal; Lovibond, Peter F

2013-09-01

In laboratory contingency learning tasks, people usually give accurate estimates of the degree of contingency between a cue and an outcome. However, if they are asked to estimate the probability of the outcome in the presence of the cue, they tend to be biased by the probability of the outcome in the absence of the cue. This bias is often attributed to an automatic contingency detection mechanism, which is said to act via an excitatory associative link to activate the outcome representation at the time of testing. We conducted 3 experiments to test alternative accounts of contingency bias. Participants were exposed to the same outcome probability in the presence of the cue, but different outcome probabilities in the absence of the cue. Phrasing the test question in terms of frequency rather than probability and clarifying the test instructions reduced but did not eliminate contingency bias. However, removal of ambiguity regarding the presence of additional causes during the test phase did eliminate contingency bias. We conclude that contingency bias may be due to ambiguity in the test question, and therefore it does not require postulation of a separate associative link-based mechanism.

Transfer of conflict and cooperation from experienced games to new games: a connectionist model of learning

PubMed Central

Spiliopoulos, Leonidas

2015-01-01

The question of whether, and if so how, learning can be transfered from previously experienced games to novel games has recently attracted the attention of the experimental game theory literature. Existing research presumes that learning operates over actions, beliefs or decision rules. This study instead uses a connectionist approach that learns a direct mapping from game payoffs to a probability distribution over own actions. Learning is operationalized as a backpropagation rule that adjusts the weights of feedforward neural networks in the direction of increasing the probability of an agent playing a myopic best response to the last game played. One advantage of this approach is that it expands the scope of the model to any possible n × n normal-form game allowing for a comprehensive model of transfer of learning. Agents are exposed to games drawn from one of seven classes of games with significantly different strategic characteristics and then forced to play games from previously unseen classes. I find significant transfer of learning, i.e., behavior that is path-dependent, or conditional on the previously seen games. Cooperation is more pronounced in new games when agents are previously exposed to games where the incentive to cooperate is stronger than the incentive to compete, i.e., when individual incentives are aligned. Prior exposure to Prisoner's dilemma, zero-sum and discoordination games led to a significant decrease in realized payoffs for all the game classes under investigation. A distinction is made between superficial and deep transfer of learning both—the former is driven by superficial payoff similarities between games, the latter by differences in the incentive structures or strategic implications of the games. I examine whether agents learn to play the Nash equilibria of games, how they select amongst multiple equilibria, and whether they transfer Nash equilibrium behavior to unseen games. Sufficient exposure to a strategically heterogeneous set of games is found to be a necessary condition for deep learning (and transfer) across game classes. Paradoxically, superficial transfer of learning is shown to lead to better outcomes than deep transfer for a wide range of game classes. The simulation results corroborate important experimental findings with human subjects, and make several novel predictions that can be tested experimentally. PMID:25873855

Calibrating random forests for probability estimation.

PubMed

Dankowski, Theresa; Ziegler, Andreas

2016-09-30

Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. In this work, we present two approaches for updating random forests for probability estimation. The first method has been proposed by Elkan and may be used for updating any machine learning approach yielding consistent probabilities, so-called probability machines. The second approach is a new strategy specifically developed for random forests. Using the terminal nodes, which represent conditional probabilities, the random forest is first translated to logistic regression models. These are, in turn, used for re-calibration. The two updating strategies were compared in a simulation study and are illustrated with data from the German Stroke Study Collaboration. In most simulation scenarios, both methods led to similar improvements. In the simulation scenario in which the stricter assumptions of Elkan's method were not met, the logistic regression-based re-calibration approach for random forests outperformed Elkan's method. It also performed better on the stroke data than Elkan's method. The strength of Elkan's method is its general applicability to any probability machine. However, if the strict assumptions underlying this approach are not met, the logistic regression-based approach is preferable for updating random forests for probability estimation. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Reasoning and choice in the Monty Hall Dilemma (MHD): implications for improving Bayesian reasoning

PubMed Central

Tubau, Elisabet; Aguilar-Lleyda, David; Johnson, Eric D.

2015-01-01

The Monty Hall Dilemma (MHD) is a two-step decision problem involving counterintuitive conditional probabilities. The first choice is made among three equally probable options, whereas the second choice takes place after the elimination of one of the non-selected options which does not hide the prize. Differing from most Bayesian problems, statistical information in the MHD has to be inferred, either by learning outcome probabilities or by reasoning from the presented sequence of events. This often leads to suboptimal decisions and erroneous probability judgments. Specifically, decision makers commonly develop a wrong intuition that final probabilities are equally distributed, together with a preference for their first choice. Several studies have shown that repeated practice enhances sensitivity to the different reward probabilities, but does not facilitate correct Bayesian reasoning. However, modest improvements in probability judgments have been observed after guided explanations. To explain these dissociations, the present review focuses on two types of causes producing the observed biases: Emotional-based choice biases and cognitive limitations in understanding probabilistic information. Among the latter, we identify a crucial cause for the universal difficulty in overcoming the equiprobability illusion: Incomplete representation of prior and conditional probabilities. We conclude that repeated practice and/or high incentives can be effective for overcoming choice biases, but promoting an adequate partitioning of possibilities seems to be necessary for overcoming cognitive illusions and improving Bayesian reasoning. PMID:25873906

Ensemble learning and model averaging for material identification in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Basener, William F.

2017-05-01

In this paper we present a method for identifying the material contained in a pixel or region of pixels in a hyperspectral image. An identification process can be performed on a spectrum from an image from pixels that has been pre-determined to be of interest, generally comparing the spectrum from the image to spectra in an identification library. The metric for comparison used in this paper a Bayesian probability for each material. This probability can be computed either from Bayes' theorem applied to normal distributions for each library spectrum or using model averaging. Using probabilities has the advantage that the probabilities can be summed over spectra for any material class to obtain a class probability. For example, the probability that the spectrum of interest is a fabric is equal to the sum of all probabilities for fabric spectra in the library. We can do the same to determine the probability for a specific type of fabric, or any level of specificity contained in our library. Probabilities not only tell us which material is most likely, the tell us how confident we can be in the material presence; a probability close to 1 indicates near certainty of the presence of a material in the given class, and a probability close to 0.5 indicates that we cannot know if the material is present at the given level of specificity. This is much more informative than a detection score from a target detection algorithm or a label from a classification algorithm. In this paper we present results in the form of a hierarchical tree with probabilities for each node. We use Forest Radiance imagery with 159 bands.

100 Students

ERIC Educational Resources Information Center

Riskowski, Jody L.; Olbricht, Gayla; Wilson, Jennifer

2010-01-01

Statistics is the art and science of gathering, analyzing, and making conclusions from data. However, many people do not fully understand how to interpret statistical results and conclusions. Placing students in a collaborative environment involving project-based learning may enable them to overcome misconceptions of probability and enhance the…

Think Pair Share with Formative Assessment for Junior High School Student

NASA Astrophysics Data System (ADS)

Pradana, O. R. Y.; Sujadi, I.; Pramudya, I.

2017-09-01

Geometry is a science related to abstract thinking ability so that not many students are able to understand this material well. In this case, the learning model plays a crucial role in improving student achievement. This means that a less precise learning model will cause difficulties for students. Therefore, this study provides a quantitative explanation of the Think Pair Share learning model combined with the formative assessment. This study aims to test the Think Pair Share with the formative assessment on junior high school students. This research uses a quantitative approach of Pretest-Posttest in control group and experiment group. ANOVA test and Scheffe test used to analyse the effectiveness this learning. Findings in this study are student achievement on the material geometry with Think Pair Share using formative assessment has increased significantly. This happens probably because this learning makes students become more active during learning. Hope in the future, Think Pair Share with formative assessment be a useful learning for teachers and this learning applied by the teacher around the world especially on the material geometry.

Interaction of plasticity and circuit organization during the acquisition of cerebellum-dependent motor learning

PubMed Central

Yang, Yan; Lisberger, Stephen G

2013-01-01

Motor learning occurs through interactions between the cerebellar circuit and cellular plasticity at different sites. Previous work has established plasticity in brain slices and suggested plausible sites of behavioral learning. We now reveal what actually happens in the cerebellum during short-term learning. We monitor the expression of plasticity in the simple-spike firing of cerebellar Purkinje cells during trial-over-trial learning in smooth pursuit eye movements of monkeys. Our findings imply that: 1) a single complex-spike response driven by one instruction for learning causes short-term plasticity in a Purkinje cell’s mossy fiber/parallel-fiber input pathways; 2) complex-spike responses and simple-spike firing rate are correlated across the Purkinje cell population; and 3) simple-spike firing rate at the time of an instruction for learning modulates the probability of a complex-spike response, possibly through a disynaptic feedback pathway to the inferior olive. These mechanisms may participate in long-term motor learning. DOI: http://dx.doi.org/10.7554/eLife.01574.001 PMID:24381248

The effect of red grape juice on Alzheimer's disease in rats

PubMed Central

Siahmard, Zahra; Alaei, Hojjatollah; Reisi, Parham; Pilehvarian, Ali Asghar

2012-01-01

Background: Alzheimer's disease is a neurodegenerative disease appearing as a result of free radicals and oxidative stress. Antioxidants agents boost memory and control Alzheimer's disease. Since red grape juice contains antioxidant agents, its effects on speed of learning and improvement of memory was studied in Alzheimer's rats. Materials and Methods: Alzheimer's model was induced by bilateral infusion of streptozocine into lateral ventricles of brain of male rats. Rats drank 10% red grape juice for 21 days. Passive avoidance learning test was used for measuring memory and learning in rats. Results: Our results showed that learning and memory in STZ-group decreased significantly compared to Sham group. However, intake of red grape juice increased speed of learning and improvement of memory in Alzheimer's rats. Conclusions: Our results suggest that there are active ingredients in red grape juice, which probably have therapeutic and preventive effects on cognitive impairments in Alzheimer's disease. PMID:23326794

The effect of red grape juice on Alzheimer's disease in rats.

PubMed

Siahmard, Zahra; Alaei, Hojjatollah; Reisi, Parham; Pilehvarian, Ali Asghar

2012-01-01

Alzheimer's disease is a neurodegenerative disease appearing as a result of free radicals and oxidative stress. Antioxidants agents boost memory and control Alzheimer's disease. Since red grape juice contains antioxidant agents, its effects on speed of learning and improvement of memory was studied in Alzheimer's rats. Alzheimer's model was induced by bilateral infusion of streptozocine into lateral ventricles of brain of male rats. Rats drank 10% red grape juice for 21 days. Passive avoidance learning test was used for measuring memory and learning in rats. Our results showed that learning and memory in STZ-group decreased significantly compared to Sham group. However, intake of red grape juice increased speed of learning and improvement of memory in Alzheimer's rats. Our results suggest that there are active ingredients in red grape juice, which probably have therapeutic and preventive effects on cognitive impairments in Alzheimer's disease.

Does prediction error drive one-shot declarative learning?

PubMed

Greve, Andrea; Cooper, Elisa; Kaula, Alexander; Anderson, Michael C; Henson, Richard

2017-06-01

The role of prediction error (PE) in driving learning is well-established in fields such as classical and instrumental conditioning, reward learning and procedural memory; however, its role in human one-shot declarative encoding is less clear. According to one recent hypothesis, PE reflects the divergence between two probability distributions: one reflecting the prior probability (from previous experiences) and the other reflecting the sensory evidence (from the current experience). Assuming unimodal probability distributions, PE can be manipulated in three ways: (1) the distance between the mode of the prior and evidence, (2) the precision of the prior, and (3) the precision of the evidence. We tested these three manipulations across five experiments, in terms of peoples' ability to encode a single presentation of a scene-item pairing as a function of previous exposures to that scene and/or item. Memory was probed by presenting the scene together with three choices for the previously paired item, in which the two foil items were from other pairings within the same condition as the target item. In Experiment 1, we manipulated the evidence to be either consistent or inconsistent with prior expectations, predicting PE to be larger, and hence memory better, when the new pairing was inconsistent. In Experiments 2a-c, we manipulated the precision of the priors, predicting better memory for a new pairing when the (inconsistent) priors were more precise. In Experiment 3, we manipulated both visual noise and prior exposure for unfamiliar faces, before pairing them with scenes, predicting better memory when the sensory evidence was more precise. In all experiments, the PE hypotheses were supported. We discuss alternative explanations of individual experiments, and conclude the Predictive Interactive Multiple Memory Signals (PIMMS) framework provides the most parsimonious account of the full pattern of results.

Hubble Tarantula Treasury Project - VI. Identification of Pre-Main-Sequence Stars using Machine Learning techniques

NASA Astrophysics Data System (ADS)

Ksoll, Victor F.; Gouliermis, Dimitrios A.; Klessen, Ralf S.; Grebel, Eva K.; Sabbi, Elena; Anderson, Jay; Lennon, Daniel J.; Cignoni, Michele; de Marchi, Guido; Smith, Linda J.; Tosi, Monica; van der Marel, Roeland P.

2018-05-01

The Hubble Tarantula Treasury Project (HTTP) has provided an unprecedented photometric coverage of the entire star-burst region of 30 Doradus down to the half Solar mass limit. We use the deep stellar catalogue of HTTP to identify all the pre-main-sequence (PMS) stars of the region, i.e., stars that have not started their lives on the main-sequence yet. The photometric distinction of these stars from the more evolved populations is not a trivial task due to several factors that alter their colour-magnitude diagram positions. The identification of PMS stars requires, thus, sophisticated statistical methods. We employ Machine Learning Classification techniques on the HTTP survey of more than 800,000 sources to identify the PMS stellar content of the observed field. Our methodology consists of 1) carefully selecting the most probable low-mass PMS stellar population of the star-forming cluster NGC2070, 2) using this sample to train classification algorithms to build a predictive model for PMS stars, and 3) applying this model in order to identify the most probable PMS content across the entire Tarantula Nebula. We employ Decision Tree, Random Forest and Support Vector Machine classifiers to categorise the stars as PMS and Non-PMS. The Random Forest and Support Vector Machine provided the most accurate models, predicting about 20,000 sources with a candidateship probability higher than 50 percent, and almost 10,000 PMS candidates with a probability higher than 95 percent. This is the richest and most accurate photometric catalogue of extragalactic PMS candidates across the extent of a whole star-forming complex.

Security bound of cheat sensitive quantum bit commitment.

PubMed

He, Guang Ping

2015-03-23

Cheat sensitive quantum bit commitment (CSQBC) loosens the security requirement of quantum bit commitment (QBC), so that the existing impossibility proofs of unconditionally secure QBC can be evaded. But here we analyze the common features in all existing CSQBC protocols, and show that in any CSQBC having these features, the receiver can always learn a non-trivial amount of information on the sender's committed bit before it is unveiled, while his cheating can pass the security check with a probability not less than 50%. The sender's cheating is also studied. The optimal CSQBC protocols that can minimize the sum of the cheating probabilities of both parties are found to be trivial, as they are practically useless. We also discuss the possibility of building a fair protocol in which both parties can cheat with equal probabilities.

Deep convolutional neural network for mammographic density segmentation

NASA Astrophysics Data System (ADS)

Wei, Jun; Li, Songfeng; Chan, Heang-Ping; Helvie, Mark A.; Roubidoux, Marilyn A.; Lu, Yao; Zhou, Chuan; Hadjiiski, Lubomir; Samala, Ravi K.

2018-02-01

Breast density is one of the most significant factors for cancer risk. In this study, we proposed a supervised deep learning approach for automated estimation of percentage density (PD) on digital mammography (DM). The deep convolutional neural network (DCNN) was trained to estimate a probability map of breast density (PMD). PD was calculated as the ratio of the dense area to the breast area based on the probability of each pixel belonging to dense region or fatty region at a decision threshold of 0.5. The DCNN estimate was compared to a feature-based statistical learning approach, in which gray level, texture and morphological features were extracted from each ROI and the least absolute shrinkage and selection operator (LASSO) was used to select and combine the useful features to generate the PMD. The reference PD of each image was provided by two experienced MQSA radiologists. With IRB approval, we retrospectively collected 347 DMs from patient files at our institution. The 10-fold cross-validation results showed a strong correlation r=0.96 between the DCNN estimation and interactive segmentation by radiologists while that of the feature-based statistical learning approach vs radiologists' segmentation had a correlation r=0.78. The difference between the segmentation by DCNN and by radiologists was significantly smaller than that between the feature-based learning approach and radiologists (p < 0.0001) by two-tailed paired t-test. This study demonstrated that the DCNN approach has the potential to replace radiologists' interactive thresholding in PD estimation on DMs.

The neural correlates of statistical learning in a word segmentation task: An fMRI study

PubMed Central

Karuza, Elisabeth A.; Newport, Elissa L.; Aslin, Richard N.; Starling, Sarah J.; Tivarus, Madalina E.; Bavelier, Daphne

2013-01-01

Functional magnetic resonance imaging (fMRI) was used to assess neural activation as participants learned to segment continuous streams of speech containing syllable sequences varying in their transitional probabilities. Speech streams were presented in four runs, each followed by a behavioral test to measure the extent of learning over time. Behavioral performance indicated that participants could discriminate statistically coherent sequences (words) from less coherent sequences (partwords). Individual rates of learning, defined as the difference in ratings for words and partwords, were used as predictors of neural activation to ask which brain areas showed activity associated with these measures. Results showed significant activity in the pars opercularis and pars triangularis regions of the left inferior frontal gyrus (LIFG). The relationship between these findings and prior work on the neural basis of statistical learning is discussed, and parallels to the frontal/subcortical network involved in other forms of implicit sequence learning are considered. PMID:23312790

Comparison of patient simulation methods used in a physical assessment course.

PubMed

Grice, Gloria R; Wenger, Philip; Brooks, Natalie; Berry, Tricia M

2013-05-13

To determine whether there is a difference in student pharmacists' learning or satisfaction when standardized patients or manikins are used to teach physical assessment. Third-year student pharmacists were randomized to learn physical assessment (cardiac and pulmonary examinations) using either a standardized patient or a manikin. Performance scores on the final examination and satisfaction with the learning method were compared between groups. Eighty and 74 student pharmacists completed the cardiac and pulmonary examinations, respectively. There was no difference in performance scores between student pharmacists who were trained using manikins vs standardized patients (93.8% vs. 93.5%, p=0.81). Student pharmacists who were trained using manikins indicated that they would have probably learned to perform cardiac and pulmonary examinations better had they been taught using standardized patients (p<0.001) and that they were less satisfied with their method of learning (p=0.04). Training using standardized patients and manikins are equally effective methods of learning physical assessment, but student pharmacists preferred using standardized patients.

Probability with Collaborative Data Visualization Software

ERIC Educational Resources Information Center

Willis, Melinda B. N.; Hay, Sue; Martin, Fred G.; Scribner-MacLean, Michelle; Rudnicki, Ivan

2015-01-01

Mathematics teachers continually look for ways to make the learning of mathematics more active and engaging. Hands-on activities, in particular, have been demonstrated to improve student engagement and understanding in mathematics classes. Likewise, many scholars have emphasized the growing importance of giving students experience with the…

Consensus in the Wasserstein Metric Space of Probability Measures

DTIC Science & Technology

2015-07-01

this direction, potential applications/uses for the Wasser - stein barycentre (itself) have been considered previously in a number of fields...one is interested in more general empirical input measures. Applications in machine learning and Bayesian statistics have also made use of the Wasser

Probability Learning as a Function of Age, Sex, and Type of Constraint

ERIC Educational Resources Information Center

Pecan, Erene V.; Schvaneveldt, Roger W.

1970-01-01

Higher levels of predicting the more frequent event were achieved with males than females; with the contingent than the noncontingent situation; and with adult males than boys in the noncontingent situation. Females were more likely to repeat an incorrect prediction. (MH)

Oleanna Math Program Materials.

ERIC Educational Resources Information Center

Coole, Walter A.

This document is a collection of course outlines, syllabi, and test materials designed for several high school level and lower division mathematics courses taught in an auto-tutorial learning laboratory at Skagit Valley College (Washington). The courses included are: Pre-Algebra, Basic Algebra, Plan Geometry, Intermediate Algebra, Probability and…

Investigations in Mathematics Education, Vol. 10, No. 3.

ERIC Educational Resources Information Center

Osborne, Alan R., Ed.

Eighteen research reports related to mathematics education are abstracted and analyzed in this publication. Three of the reports deal with aspects of learning theory, seven with topics in mathematics instruction (problem solving, weight, quadratic inequalities, probability and statistics, area and volume conservation, cardinality), five with…

The Dynamics of Conditioning and Extinction

PubMed Central

Killeen, Peter R.; Sanabria, Federico; Dolgov, Igor

2009-01-01

Pigeons responded to intermittently reinforced classical conditioning trials with erratic bouts of responding to the CS. Responding depended on whether the prior trial contained a peck, food, or both. A linear-persistence/learning model moved animals into and out of a response state, and a Weibull distribution for number of within-trial responses governed in-state pecking. Variations of trial and inter-trial durations caused correlated changes in rate and probability of responding, and model parameters. A novel prediction—in the protracted absence of food, response rates can plateau above zero—was validated. The model predicted smooth acquisition functions when instantiated with the probability of food, but a more accurate jagged learning curve when instantiated with trial-to-trial records of reinforcement. The Skinnerian parameter was dominant only when food could be accelerated or delayed by pecking. These experiments provide a framework for trial-by-trial accounts of conditioning and extinction that increases the information available from the data, permitting them to comment more definitively on complex contemporary models of momentum and conditioning. PMID:19839699

Improving deep convolutional neural networks with mixed maxout units

PubMed Central

Liu, Fu-xian; Li, Long-yue

2017-01-01

Motivated by insights from the maxout-units-based deep Convolutional Neural Network (CNN) that “non-maximal features are unable to deliver” and “feature mapping subspace pooling is insufficient,” we present a novel mixed variant of the recently introduced maxout unit called a mixout unit. Specifically, we do so by calculating the exponential probabilities of feature mappings gained by applying different convolutional transformations over the same input and then calculating the expected values according to their exponential probabilities. Moreover, we introduce the Bernoulli distribution to balance the maximum values with the expected values of the feature mappings subspace. Finally, we design a simple model to verify the pooling ability of mixout units and a Mixout-units-based Network-in-Network (NiN) model to analyze the feature learning ability of the mixout models. We argue that our proposed units improve the pooling ability and that mixout models can achieve better feature learning and classification performance. PMID:28727737

ANNz2: Photometric Redshift and Probability Distribution Function Estimation using Machine Learning

NASA Astrophysics Data System (ADS)

Sadeh, I.; Abdalla, F. B.; Lahav, O.

2016-10-01

We present ANNz2, a new implementation of the public software for photometric redshift (photo-z) estimation of Collister & Lahav, which now includes generation of full probability distribution functions (PDFs). ANNz2 utilizes multiple machine learning methods, such as artificial neural networks and boosted decision/regression trees. The objective of the algorithm is to optimize the performance of the photo-z estimation, to properly derive the associated uncertainties, and to produce both single-value solutions and PDFs. In addition, estimators are made available, which mitigate possible problems of non-representative or incomplete spectroscopic training samples. ANNz2 has already been used as part of the first weak lensing analysis of the Dark Energy Survey, and is included in the experiment's first public data release. Here we illustrate the functionality of the code using data from the tenth data release of the Sloan Digital Sky Survey and the Baryon Oscillation Spectroscopic Survey. The code is available for download at http://github.com/IftachSadeh/ANNZ.

Introducing computational thinking through hands-on projects using R with applications to calculus, probability and data analysis

NASA Astrophysics Data System (ADS)

Benakli, Nadia; Kostadinov, Boyan; Satyanarayana, Ashwin; Singh, Satyanand

2017-04-01

The goal of this paper is to promote computational thinking among mathematics, engineering, science and technology students, through hands-on computer experiments. These activities have the potential to empower students to learn, create and invent with technology, and they engage computational thinking through simulations, visualizations and data analysis. We present nine computer experiments and suggest a few more, with applications to calculus, probability and data analysis, which engage computational thinking through simulations, visualizations and data analysis. We are using the free (open-source) statistical programming language R. Our goal is to give a taste of what R offers rather than to present a comprehensive tutorial on the R language. In our experience, these kinds of interactive computer activities can be easily integrated into a smart classroom. Furthermore, these activities do tend to keep students motivated and actively engaged in the process of learning, problem solving and developing a better intuition for understanding complex mathematical concepts.

Experience and Explanation: Using Videogames to Prepare Students for Formal Instruction in Statistics

NASA Astrophysics Data System (ADS)

Arena, Dylan A.; Schwartz, Daniel L.

2014-08-01

Well-designed digital games can deliver powerful experiences that are difficult to provide through traditional instruction, while traditional instruction can deliver formal explanations that are not a natural fit for gameplay. Combined, they can accomplish more than either can alone. An experiment tested this claim using the topic of statistics, where people's everyday experiences often conflict with normative statistical theories and a videogame might provide an alternate set of experiences for students to draw upon. The research used a game called Stats Invaders!, a variant of the classic videogame Space Invaders. In Stats Invaders!, the locations of descending alien invaders follow probability distributions, and players need to infer the shape of the distributions to play well. The experiment tested whether the game developed participants' intuitions about the structure of random events and thereby prepared them for future learning from a subsequent written passage on probability distributions. Community-college students who played the game and then read the passage learned more than participants who only read the passage.

[Cognitive and functional decline in the stage previous to the diagnosis of Alzheimers disease].

PubMed

García-Sánchez, C; Estévez-González, A; Boltes, A; Otermín, P; López-Góngora, M; Gironell, A; Kulisevsky, J

2003-12-01

The decline in the phase prior to diagnosis of Alzheimers disease (AD) is not well known, although this knowledge is necessary to evaluate the efficiency of new drugs that can influence in disease course prior to diagnosis. To contribute to better knowledge of the decline prior to diagnosis, we have investigated the cognitive and functional deterioration for 2-3 years before the probable AD diagnosis was established. We compared results obtained by 17 control subjects and 27 patients at the time of diagnosis of a probable AD with results obtained 2-3 years before (interval of 27.7 4 months). We compared memory functions (logical, recognition, learning and autobiographical memory), naming, visual and visuospatial gnosis, visuoconstructive praxis, verbal fluency and the Mini-Mental State Examination (MMSE), Informant Questionnaire and Blessed's Scale scores. Performance of control subjects did not change. AD patients showed a significant decline in scores, except for verbal fluency. In order of importance, cognitive decline was more marked in scores of learning memory, visuospatial gnosis, autobiographical memory and visuoconstructive praxis. Decline prior to diagnosis of AD is characterized by an important learning memory impairment. Deterioration of visuospatial gnosis and visuoconstructive praxis is greater than deterioration of MMSE and Informant Questionnaire scores.

Neuromorphic learning of continuous-valued mappings from noise-corrupted data. Application to real-time adaptive control

NASA Technical Reports Server (NTRS)

Troudet, Terry; Merrill, Walter C.

1990-01-01

The ability of feed-forward neural network architectures to learn continuous valued mappings in the presence of noise was demonstrated in relation to parameter identification and real-time adaptive control applications. An error function was introduced to help optimize parameter values such as number of training iterations, observation time, sampling rate, and scaling of the control signal. The learning performance depended essentially on the degree of embodiment of the control law in the training data set and on the degree of uniformity of the probability distribution function of the data that are presented to the net during sequence. When a control law was corrupted by noise, the fluctuations of the training data biased the probability distribution function of the training data sequence. Only if the noise contamination is minimized and the degree of embodiment of the control law is maximized, can a neural net develop a good representation of the mapping and be used as a neurocontroller. A multilayer net was trained with back-error-propagation to control a cart-pole system for linear and nonlinear control laws in the presence of data processing noise and measurement noise. The neurocontroller exhibited noise-filtering properties and was found to operate more smoothly than the teacher in the presence of measurement noise.

Online Reinforcement Learning Using a Probability Density Estimation.

PubMed

Agostini, Alejandro; Celaya, Enric

2017-01-01

Function approximation in online, incremental, reinforcement learning needs to deal with two fundamental problems: biased sampling and nonstationarity. In this kind of task, biased sampling occurs because samples are obtained from specific trajectories dictated by the dynamics of the environment and are usually concentrated in particular convergence regions, which in the long term tend to dominate the approximation in the less sampled regions. The nonstationarity comes from the recursive nature of the estimations typical of temporal difference methods. This nonstationarity has a local profile, varying not only along the learning process but also along different regions of the state space. We propose to deal with these problems using an estimation of the probability density of samples represented with a gaussian mixture model. To deal with the nonstationarity problem, we use the common approach of introducing a forgetting factor in the updating formula. However, instead of using the same forgetting factor for the whole domain, we make it dependent on the local density of samples, which we use to estimate the nonstationarity of the function at any given input point. To address the biased sampling problem, the forgetting factor applied to each mixture component is modulated according to the new information provided in the updating, rather than forgetting depending only on time, thus avoiding undesired distortions of the approximation in less sampled regions.

Habitual attention in older and young adults.

PubMed

Jiang, Yuhong V; Koutstaal, Wilma; Twedell, Emily L

2016-12-01

Age-related decline is pervasive in tasks that require explicit learning and memory, but such reduced function is not universally observed in tasks involving incidental learning. It is unknown if habitual attention, involving incidental probabilistic learning, is preserved in older adults. Previous research on habitual attention investigated contextual cuing in young and older adults, yet contextual cuing relies not only on spatial attention but also on context processing. Here we isolated habitual attention from context processing in young and older adults. Using a challenging visual search task in which the probability of finding targets was greater in 1 of 4 visual quadrants in all contexts, we examined the acquisition, persistence, and spatial-reference frame of habitual attention. Although older adults showed slower visual search times and steeper search slopes (more time per additional item in the search display), like young adults they rapidly acquired a strong, persistent search habit toward the high-probability quadrant. In addition, habitual attention was strongly viewer-centered in both young and older adults. The demonstration of preserved viewer-centered habitual attention in older adults suggests that it may be used to counter declines in controlled attention. This, in turn, suggests the importance, for older adults, of maintaining habit-related spatial arrangements. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

A Study of Students' Learning Styles, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education.

PubMed

Christou, Nicolas; Dinov, Ivo D

2010-09-01

Many modern technological advances have direct impact on the format, style and efficacy of delivery and consumption of educational content. For example, various novel communication and information technology tools and resources enable efficient, timely, interactive and graphical demonstrations of diverse scientific concepts. In this manuscript, we report on a meta-study of 3 controlled experiments of using the Statistics Online Computational Resources in probability and statistics courses. Web-accessible SOCR applets, demonstrations, simulations and virtual experiments were used in different courses as treatment and compared to matched control classes utilizing traditional pedagogical approaches. Qualitative and quantitative data we collected for all courses included Felder-Silverman-Soloman index of learning styles, background assessment, pre and post surveys of attitude towards the subject, end-point satisfaction survey, and varieties of quiz, laboratory and test scores. Our findings indicate that students' learning styles and attitudes towards a discipline may be important confounds of their final quantitative performance. The observed positive effects of integrating information technology with established pedagogical techniques may be valid across disciplines within the broader spectrum courses in the science education curriculum. The two critical components of improving science education via blended instruction include instructor training, and development of appropriate activities, simulations and interactive resources.

A Study of Students' Learning Styles, Discipline Attitudes and Knowledge Acquisition in Technology-Enhanced Probability and Statistics Education

PubMed Central

Christou, Nicolas; Dinov, Ivo D.

2011-01-01

Many modern technological advances have direct impact on the format, style and efficacy of delivery and consumption of educational content. For example, various novel communication and information technology tools and resources enable efficient, timely, interactive and graphical demonstrations of diverse scientific concepts. In this manuscript, we report on a meta-study of 3 controlled experiments of using the Statistics Online Computational Resources in probability and statistics courses. Web-accessible SOCR applets, demonstrations, simulations and virtual experiments were used in different courses as treatment and compared to matched control classes utilizing traditional pedagogical approaches. Qualitative and quantitative data we collected for all courses included Felder-Silverman-Soloman index of learning styles, background assessment, pre and post surveys of attitude towards the subject, end-point satisfaction survey, and varieties of quiz, laboratory and test scores. Our findings indicate that students' learning styles and attitudes towards a discipline may be important confounds of their final quantitative performance. The observed positive effects of integrating information technology with established pedagogical techniques may be valid across disciplines within the broader spectrum courses in the science education curriculum. The two critical components of improving science education via blended instruction include instructor training, and development of appropriate activities, simulations and interactive resources. PMID:21603097

TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning.

PubMed

Mareschal, Denis; French, Robert M

2017-01-05

Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'. © 2016 The Author(s).

TRACX2: a connectionist autoencoder using graded chunks to model infant visual statistical learning

PubMed Central

French, Robert M.

2017-01-01

Even newborn infants are able to extract structure from a stream of sensory inputs; yet how this is achieved remains largely a mystery. We present a connectionist autoencoder model, TRACX2, that learns to extract sequence structure by gradually constructing chunks, storing these chunks in a distributed manner across its synaptic weights and recognizing these chunks when they re-occur in the input stream. Chunks are graded rather than all-or-nothing in nature. As chunks are learnt their component parts become more and more tightly bound together. TRACX2 successfully models the data from five experiments from the infant visual statistical learning literature, including tasks involving forward and backward transitional probabilities, low-salience embedded chunk items, part-sequences and illusory items. The model also captures performance differences across ages through the tuning of a single-learning rate parameter. These results suggest that infant statistical learning is underpinned by the same domain-general learning mechanism that operates in auditory statistical learning and, potentially, in adult artificial grammar learning. This article is part of the themed issue ‘New frontiers for statistical learning in the cognitive sciences’. PMID:27872375

Lattice Duality: The Origin of Probability and Entropy

NASA Technical Reports Server (NTRS)

Knuth, Kevin H.

2004-01-01

Bayesian probability theory is an inference calculus, which originates from a generalization of inclusion on the Boolean lattice of logical assertions to a degree of inclusion represented by a real number. Dual to this lattice is the distributive lattice of questions constructed from the ordered set of down-sets of assertions, which forms the foundation of the calculus of inquiry-a generalization of information theory. In this paper we introduce this novel perspective on these spaces in which machine learning is performed and discuss the relationship between these results and several proposed generalizations of information theory in the literature.

Probability, statistics, and computational science.

PubMed

Beerenwinkel, Niko; Siebourg, Juliane

2012-01-01

In this chapter, we review basic concepts from probability theory and computational statistics that are fundamental to evolutionary genomics. We provide a very basic introduction to statistical modeling and discuss general principles, including maximum likelihood and Bayesian inference. Markov chains, hidden Markov models, and Bayesian network models are introduced in more detail as they occur frequently and in many variations in genomics applications. In particular, we discuss efficient inference algorithms and methods for learning these models from partially observed data. Several simple examples are given throughout the text, some of which point to models that are discussed in more detail in subsequent chapters.

The Battle for Leyte Gulf, October 1944. Strategical and Tactical Analysis. Volume I. Preliminary Operations until 0719 October 17th, 1944 Including Battle off Formosa

DTIC Science & Technology

1953-01-01

probably will be inexperienced in cowrnand in war. Finally. all comments and criticL3,ns are designed to be constructive. By indicating what appear to be...CofS, Combined Fleet estimate *more than six ships sunk cr afire" 329 Final probable estimate 329 Learns submarine I-: 5 had departed Kure for des 329...defensive opera- tions, known as the "SHO" (Victory) operations, which were designed to deny to the Allies a "oothold in the "iast ditch" island

Learning About Climate and Atmospheric Models Through Machine Learning

NASA Astrophysics Data System (ADS)

Lucas, D. D.

2017-12-01

From the analysis of ensemble variability to improving simulation performance, machine learning algorithms can play a powerful role in understanding the behavior of atmospheric and climate models. To learn about model behavior, we create training and testing data sets through ensemble techniques that sample different model configurations and values of input parameters, and then use supervised machine learning to map the relationships between the inputs and outputs. Following this procedure, we have used support vector machines, random forests, gradient boosting and other methods to investigate a variety of atmospheric and climate model phenomena. We have used machine learning to predict simulation crashes, estimate the probability density function of climate sensitivity, optimize simulations of the Madden Julian oscillation, assess the impacts of weather and emissions uncertainty on atmospheric dispersion, and quantify the effects of model resolution changes on precipitation. This presentation highlights recent examples of our applications of machine learning to improve the understanding of climate and atmospheric models. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Probability Explorations in a Multicultural Context

ERIC Educational Resources Information Center

Naresh, Nirmala; Harper, Suzanne R.; Keiser, Jane M.; Krumpe, Norm

2014-01-01

Mathematical ideas exist and develop in many different cultures. From this multicultural perspective, teachers can use a variety of approaches to acknowledge the role of culture in the teaching and learning of mathematics. Curricular materials that "emphasize both the mathematical and sociocultural aspects" not only help teachers achieve…

Problems of Indian Children.

ERIC Educational Resources Information Center

Linton, Marigold

Previous approaches to the learning problems of American Indian children are viewed as inadequate. An alternative is suggested which emphasizes the problem solution strategies which these children bring to the school situation. Solutions were analyzed in terms of: (1) their probability; (2) their efficiency at permitting a present problem to be…

Handling Alters Aggression and "Loser" Effect Formation in "Drosophila Melanogaster"

ERIC Educational Resources Information Center

Trannoy, Severine; Chowdhury, Budhaditya; Kravitz, Edward A.

2015-01-01

In "Drosophila," prior fighting experience influences the outcome of later contests: losing a fight increases the probability of losing second contests, thereby revealing "loser" effects that involve learning and memory. In these experiments, to generate and quantify the behavioral changes observed as consequences of losing…

The Paradoxical Value of Privacy

DTIC Science & Technology

2003-03-14

on occurrence of identity theft correlated with consumer behavior so that probabilities of at least such clear privacy problems could be assigned to...now. And, the market typically needs to learn from experience, so consumer behavior is likely to lag behind any current reality. So one answer is that

Investigations in Mathematics Education, Vol. 10, No. 4.

ERIC Educational Resources Information Center

Osborne, Alan R., Ed.

Eighteen research reports related to mathematics education are abstracted and analyzed. Four of the reports deal with aspects of learning theory, five with topics in mathematics instruction (history of mathematics, exponents, probability, calculus, and calculators), four with teacher characteristics, and one each with testing, student interests,…

Modeling: It's More than Just Imitation

ERIC Educational Resources Information Center

Horner, Sherri L.; Bhattacharyya, Srilata; O'Connor, Evelyn A.

2008-01-01

Anyone who has observed or played with young children probably has noticed how they imitate what they see--their friends, siblings, parents, and teachers; television, movie, and book characters; and sometimes even their family pets. Frequently, this imitation can help children learn appropriate behaviors, attitudes, and thinking patterns.…

Teachers' Perceptions of the Relevance and Usefulness of Professional Development

ERIC Educational Resources Information Center

Shoemaker, Susan F.

2013-01-01

The purpose of this qualitative, phenomenological study was to investigate, through interviews, secondary teachers' perceptions of the level of the value, applicability, and implementation of skills learned within professional development offerings in the targeted school district. Non-probability, stratified, purposeful sampling was utilized to…

Dissecting the Function of Hippocampal Oscillations in a Human Anxiety Model

PubMed Central

Khemka, Saurabh

2017-01-01

Neural oscillations in hippocampus and medial prefrontal cortex (mPFC) are a hallmark of rodent anxiety models that build on conflict between approach and avoidance. Yet, the function of these oscillations, and their expression in humans, remain elusive. Here, we used magnetoencephalography (MEG) to investigate neural oscillations in a task that simulated approach–avoidance conflict, wherein 23 male and female human participants collected monetary tokens under a threat of virtual predation. Probability of threat was signaled by color and learned beforehand by direct experience. Magnitude of threat corresponded to a possible monetary loss, signaled as a quantity. We focused our analyses on an a priori defined region-of-interest, the bilateral hippocampus. Oscillatory power under conflict was linearly predicted by threat probability in a location consistent with right mid-hippocampus. This pattern was specific to the hippocampus, most pronounced in the gamma band, and not explained by spatial movement or anxiety-like behavior. Gamma power was modulated by slower theta rhythms, and this theta modulation increased with threat probability. Furthermore, theta oscillations in the same location showed greater synchrony with mPFC theta with increased threat probability. Strikingly, these findings were not seen in relation to an increase in threat magnitude, which was explicitly signaled as a quantity and induced similar behavioral responses as learned threat probability. Thus, our findings suggest that the expression of hippocampal and mPFC oscillatory activity in the context of anxiety is specifically linked to threat memory. These findings resonate with neurocomputational accounts of the role played by hippocampal oscillations in memory. SIGNIFICANCE STATEMENT We use a biologically relevant approach–avoidance conflict test in humans while recording neural oscillations with magnetoencephalography to investigate the expression and function of hippocampal oscillations in human anxiety. Extending nonhuman studies, we can assign a possible function to hippocampal oscillations in this task, namely threat memory communication. This blends into recent attempts to elucidate the role of brain synchronization in defensive responses to threat. PMID:28626018

Taking the easy way out? Increasing implementation effort reduces probability maximizing under cognitive load.

PubMed

Schulze, Christin; Newell, Ben R

2016-07-01

Cognitive load has previously been found to have a positive effect on strategy selection in repeated risky choice. Specifically, whereas inferior probability matching often prevails under single-task conditions, optimal probability maximizing sometimes dominates when a concurrent task competes for cognitive resources. We examined the extent to which this seemingly beneficial effect of increased task demands hinges on the effort required to implement each of the choice strategies. Probability maximizing typically involves a simple repeated response to a single option, whereas probability matching requires choice proportions to be tracked carefully throughout a sequential choice task. Here, we flipped this pattern by introducing a manipulation that made the implementation of maximizing more taxing and, at the same time, allowed decision makers to probability match via a simple repeated response to a single option. The results from two experiments showed that increasing the implementation effort of probability maximizing resulted in decreased adoption rates of this strategy. This was the case both when decision makers simultaneously learned about the outcome probabilities and responded to a dual task (Exp. 1) and when these two aspects were procedurally separated in two distinct stages (Exp. 2). We conclude that the effort involved in implementing a choice strategy is a key factor in shaping repeated choice under uncertainty. Moreover, highlighting the importance of implementation effort casts new light on the sometimes surprising and inconsistent effects of cognitive load that have previously been reported in the literature.

Seismicity alert probabilities at Parkfield, California, revisited

USGS Publications Warehouse

Michael, A.J.; Jones, L.M.

1998-01-01

For a decade, the US Geological Survey has used the Parkfield Earthquake Prediction Experiment scenario document to estimate the probability that earthquakes observed on the San Andreas fault near Parkfield will turn out to be foreshocks followed by the expected magnitude six mainshock. During this time, we have learned much about the seismogenic process at Parkfield, about the long-term probability of the Parkfield mainshock, and about the estimation of these types of probabilities. The probabilities for potential foreshocks at Parkfield are reexamined and revised in light of these advances. As part of this process, we have confirmed both the rate of foreshocks before strike-slip earthquakes in the San Andreas physiographic province and the uniform distribution of foreshocks with magnitude proposed by earlier studies. Compared to the earlier assessment, these new estimates of the long-term probability of the Parkfield mainshock are lower, our estimate of the rate of background seismicity is higher, and we find that the assumption that foreshocks at Parkfield occur in a unique way is not statistically significant at the 95% confidence level. While the exact numbers vary depending on the assumptions that are made, the new alert probabilities are lower than previously estimated. Considering the various assumptions and the statistical uncertainties in the input parameters, we also compute a plausible range for the probabilities. The range is large, partly due to the extra knowledge that exists for the Parkfield segment, making us question the usefulness of these numbers.

Learning the dynamics of objects by optimal functional interpolation.

PubMed

Ahn, Jong-Hoon; Kim, In Young

2012-09-01

Many areas of science and engineering rely on functional data and their numerical analysis. The need to analyze time-varying functional data raises the general problem of interpolation, that is, how to learn a smooth time evolution from a finite number of observations. Here, we introduce optimal functional interpolation (OFI), a numerical algorithm that interpolates functional data over time. Unlike the usual interpolation or learning algorithms, the OFI algorithm obeys the continuity equation, which describes the transport of some types of conserved quantities, and its implementation shows smooth, continuous flows of quantities. Without the need to take into account equations of motion such as the Navier-Stokes equation or the diffusion equation, OFI is capable of learning the dynamics of objects such as those represented by mass, image intensity, particle concentration, heat, spectral density, and probability density.

Dynamic Influence of Emotional States on Novel Word Learning

PubMed Central

Guo, Jingjing; Zou, Tiantian; Peng, Danling

2018-01-01

Many researchers realize that it's unrealistic to isolate language learning and processing from emotions. However, few studies on language learning have taken emotions into consideration so far, so that the probable influences of emotions on language learning are unclear. The current study thereby aimed to examine the effects of emotional states on novel word learning and their dynamic changes with learning continuing and task varying. Positive, negative or neutral pictures were employed to induce a given emotional state, and then participants learned the novel words through association with line-drawing pictures in four successive learning phases. At the end of each learning phase, participants were instructed to fulfill a semantic category judgment task (in Experiment 1) or a word-picture semantic consistency judgment task (in Experiment 2) to explore the effects of emotional states on different depths of word learning. Converging results demonstrated that negative emotional state led to worse performance compared with neutral condition; however, how positive emotional state affected learning varied with learning task. Specifically, a facilitative role of positive emotional state in semantic category learning was observed but disappeared in word specific meaning learning. Moreover, the emotional modulation on novel word learning was quite dynamic and changeable with learning continuing, and the final attainment of the learned words tended to be similar under different emotional states. The findings suggest that the impact of emotion can be offset when novel words became more and more familiar and a part of existent lexicon. PMID:29695994

Dynamic Influence of Emotional States on Novel Word Learning.

PubMed

Guo, Jingjing; Zou, Tiantian; Peng, Danling

2018-01-01

Many researchers realize that it's unrealistic to isolate language learning and processing from emotions. However, few studies on language learning have taken emotions into consideration so far, so that the probable influences of emotions on language learning are unclear. The current study thereby aimed to examine the effects of emotional states on novel word learning and their dynamic changes with learning continuing and task varying. Positive, negative or neutral pictures were employed to induce a given emotional state, and then participants learned the novel words through association with line-drawing pictures in four successive learning phases. At the end of each learning phase, participants were instructed to fulfill a semantic category judgment task (in Experiment 1) or a word-picture semantic consistency judgment task (in Experiment 2) to explore the effects of emotional states on different depths of word learning. Converging results demonstrated that negative emotional state led to worse performance compared with neutral condition; however, how positive emotional state affected learning varied with learning task. Specifically, a facilitative role of positive emotional state in semantic category learning was observed but disappeared in word specific meaning learning. Moreover, the emotional modulation on novel word learning was quite dynamic and changeable with learning continuing, and the final attainment of the learned words tended to be similar under different emotional states. The findings suggest that the impact of emotion can be offset when novel words became more and more familiar and a part of existent lexicon.

Integrated Bayesian models of learning and decision making for saccadic eye movements.

PubMed

Brodersen, Kay H; Penny, Will D; Harrison, Lee M; Daunizeau, Jean; Ruff, Christian C; Duzel, Emrah; Friston, Karl J; Stephan, Klaas E

2008-11-01

The neurophysiology of eye movements has been studied extensively, and several computational models have been proposed for decision-making processes that underlie the generation of eye movements towards a visual stimulus in a situation of uncertainty. One class of models, known as linear rise-to-threshold models, provides an economical, yet broadly applicable, explanation for the observed variability in the latency between the onset of a peripheral visual target and the saccade towards it. So far, however, these models do not account for the dynamics of learning across a sequence of stimuli, and they do not apply to situations in which subjects are exposed to events with conditional probabilities. In this methodological paper, we extend the class of linear rise-to-threshold models to address these limitations. Specifically, we reformulate previous models in terms of a generative, hierarchical model, by combining two separate sub-models that account for the interplay between learning of target locations across trials and the decision-making process within trials. We derive a maximum-likelihood scheme for parameter estimation as well as model comparison on the basis of log likelihood ratios. The utility of the integrated model is demonstrated by applying it to empirical saccade data acquired from three healthy subjects. Model comparison is used (i) to show that eye movements do not only reflect marginal but also conditional probabilities of target locations, and (ii) to reveal subject-specific learning profiles over trials. These individual learning profiles are sufficiently distinct that test samples can be successfully mapped onto the correct subject by a naïve Bayes classifier. Altogether, our approach extends the class of linear rise-to-threshold models of saccadic decision making, overcomes some of their previous limitations, and enables statistical inference both about learning of target locations across trials and the decision-making process within trials.

Back-Propagation Operation for Analog Neural Network Hardware with Synapse Components Having Hysteresis Characteristics

PubMed Central

Ueda, Michihito; Nishitani, Yu; Kaneko, Yukihiro; Omote, Atsushi

2014-01-01

To realize an analog artificial neural network hardware, the circuit element for synapse function is important because the number of synapse elements is much larger than that of neuron elements. One of the candidates for this synapse element is a ferroelectric memristor. This device functions as a voltage controllable variable resistor, which can be applied to a synapse weight. However, its conductance shows hysteresis characteristics and dispersion to the input voltage. Therefore, the conductance values vary according to the history of the height and the width of the applied pulse voltage. Due to the difficulty of controlling the accurate conductance, it is not easy to apply the back-propagation learning algorithm to the neural network hardware having memristor synapses. To solve this problem, we proposed and simulated a learning operation procedure as follows. Employing a weight perturbation technique, we derived the error change. When the error reduced, the next pulse voltage was updated according to the back-propagation learning algorithm. If the error increased the amplitude of the next voltage pulse was set in such way as to cause similar memristor conductance but in the opposite voltage scanning direction. By this operation, we could eliminate the hysteresis and confirmed that the simulation of the learning operation converged. We also adopted conductance dispersion numerically in the simulation. We examined the probability that the error decreased to a designated value within a predetermined loop number. The ferroelectric has the characteristics that the magnitude of polarization does not become smaller when voltages having the same polarity are applied. These characteristics greatly improved the probability even if the learning rate was small, if the magnitude of the dispersion is adequate. Because the dispersion of analog circuit elements is inevitable, this learning operation procedure is useful for analog neural network hardware. PMID:25393715

Identification of probabilities.

PubMed

Vitányi, Paul M B; Chater, Nick

2017-02-01

Within psychology, neuroscience and artificial intelligence, there has been increasing interest in the proposal that the brain builds probabilistic models of sensory and linguistic input: that is, to infer a probabilistic model from a sample. The practical problems of such inference are substantial: the brain has limited data and restricted computational resources. But there is a more fundamental question: is the problem of inferring a probabilistic model from a sample possible even in principle? We explore this question and find some surprisingly positive and general results. First, for a broad class of probability distributions characterized by computability restrictions, we specify a learning algorithm that will almost surely identify a probability distribution in the limit given a finite i.i.d. sample of sufficient but unknown length. This is similarly shown to hold for sequences generated by a broad class of Markov chains, subject to computability assumptions. The technical tool is the strong law of large numbers. Second, for a large class of dependent sequences, we specify an algorithm which identifies in the limit a computable measure for which the sequence is typical, in the sense of Martin-Löf (there may be more than one such measure). The technical tool is the theory of Kolmogorov complexity. We analyze the associated predictions in both cases. We also briefly consider special cases, including language learning, and wider theoretical implications for psychology.

Integrating multiple fitting regression and Bayes decision for cancer diagnosis with transcriptomic data from tumor-educated blood platelets.

PubMed

Huang, Guangzao; Yuan, Mingshun; Chen, Moliang; Li, Lei; You, Wenjie; Li, Hanjie; Cai, James J; Ji, Guoli

2017-10-07

The application of machine learning in cancer diagnostics has shown great promise and is of importance in clinic settings. Here we consider applying machine learning methods to transcriptomic data derived from tumor-educated platelets (TEPs) from individuals with different types of cancer. We aim to define a reliability measure for diagnostic purposes to increase the potential for facilitating personalized treatments. To this end, we present a novel classification method called MFRB (for Multiple Fitting Regression and Bayes decision), which integrates the process of multiple fitting regression (MFR) with Bayes decision theory. MFR is first used to map multidimensional features of the transcriptomic data into a one-dimensional feature. The probability density function of each class in the mapped space is then adjusted using the Gaussian probability density function. Finally, the Bayes decision theory is used to build a probabilistic classifier with the estimated probability density functions. The output of MFRB can be used to determine which class a sample belongs to, as well as to assign a reliability measure for a given class. The classical support vector machine (SVM) and probabilistic SVM (PSVM) are used to evaluate the performance of the proposed method with simulated and real TEP datasets. Our results indicate that the proposed MFRB method achieves the best performance compared to SVM and PSVM, mainly due to its strong generalization ability for limited, imbalanced, and noisy data.

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

NASA Astrophysics Data System (ADS)

Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

2017-10-01

A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

Language Aptitude: Desirable Trait or Acquirable Attribute?

ERIC Educational Resources Information Center

Singleton, David

2017-01-01

The traditional definition of language aptitude sees it as "an individual's initial state of readiness and capacity for learning a foreign language, and probable facility in doing so given the presence of motivation and opportunity" (Carroll, 1981, p. 86). This conception portrays language aptitude as a trait, in the sense of exhibiting…

Benefits of Accumulating versus Diminishing Cues in Recall

ERIC Educational Resources Information Center

Finley, Jason R.; Benjamin, Aaron S.; Hays, Matthew J.; Bjork, Robert A.; Kornell, Nate

2011-01-01

Optimizing learning over multiple retrieval opportunities requires a joint consideration of both the probability and the mnemonic value of a successful retrieval. Previous research has addressed this trade-off by manipulating the schedule of practice trials, suggesting that a pattern of increasingly long lags--"expanding retrieval practice"--may…

When 95% Accurate Isn't: Exploring Bayes's Theorem

ERIC Educational Resources Information Center

CadwalladerOlsker, Todd D.

2011-01-01

Bayes's theorem is notorious for being a difficult topic to learn and to teach. Problems involving Bayes's theorem (either implicitly or explicitly) generally involve calculations based on two or more given probabilities and their complements. Further, a correct solution depends on students' ability to interpret the problem correctly. Most people…

Building Resiliency to Childhood Trauma through Arts-Based Learning

ERIC Educational Resources Information Center

Smilan, Cathy

2009-01-01

Natural disasters are among the numerous events known to have a significant probability of producing trauma in school-age children, given the critical mental, physical, social, and emotional development that occurs during childhood. Studies involving children who have experienced natural disasters point to a significant increase in psychological…

A Lakatosian Encounter with Probability

ERIC Educational Resources Information Center

Chick, Helen

2010-01-01

There is much to be learned and pondered by reading "Proofs and Refutations" by Imre Lakatos (Lakatos, 1976). It highlights the importance of mathematical definitions, and how definitions evolve to capture the essence of the object they are defining. It also provides an exhilarating encounter with the ups and downs of the mathematical reasoning…

Educational Company and E-Learning

ERIC Educational Resources Information Center

Manlig, František; Šlaichová, Eva; Pelantová, Vera; Šimúnová, Michala; Koblasa, František; Vavruška, Jan

2013-01-01

This article deals with nowadays urgent issue. It tries to find a way how to achieve as highest probability of current students employment as possible, especially in the age of business crises. It comes from actual industry practice requirements on hiring employees. There is briefly, considering limited range of article, described innovative…

In-situ monitoring and assessment of post barge-bridge collision damage for minimizing traffic delay and detour : final report.

DOT National Transportation Integrated Search

2016-07-31

This report presents a novel framework for promptly assessing the probability of barge-bridge : collision damage of piers based on probabilistic-based classification through machine learning. The main : idea of the presented framework is to divide th...

Bringing Antarctica Home

ERIC Educational Resources Information Center

Constible, Juanita; Williams, Lauren; Faure, Jaime; Lee, Richard E., Jr.

2012-01-01

When one thinks of the amazing creatures of Antarctica, an insect probably does not come to mind. But this unlikely animal, and a scientific expedition to Antarctica, was the foundation for a learning event that created a community of learners spanning kindergarten through sixth grade and extended beyond the classroom. Miami University's Antarctic…

The Luddite Revolt Continues

ERIC Educational Resources Information Center

Baggaley, Jon

2010-01-01

The bicentenary in 2011 of the Luddite Revolt prompts us to ask "what would Ned Ludd think of today's automated styles of distance education?" He would no doubt echo the common criticism that educational technologies create an impersonal style of teaching and learning, and devalue the teacher. He would probably agree that online methods…

Verizon Connects to Success

ERIC Educational Resources Information Center

Weinstein, Margery

2011-01-01

A household name synonymous with modern telecommunications, Verizon's performance management innovations in 2010 kept pace with the growth of its brand. Many people probably are customers of the company's services. What's less known to the public is the consistent quality of Verizon's learning and development programs. A regular on the "Training"…

The Decentralisation Debate: Thinking about Power

ERIC Educational Resources Information Center

Berkhout, Susara J.

2005-01-01

Comparing the dynamics of centralisation/decentralisation in Belgium and South Africa has the advantage of revealing discrepancies between the public or official rationale for the (re) distribution of power and the probable or eventual effect of this (re)distribution on educational processes and learning outcomes. It can be seen that local…

Color Your Classroom II. A Math Curriculum Guide.

ERIC Educational Resources Information Center

Mississippi State Dept. of Education, Jackson.

This math curriculum guide, correlated with the numerical coding of the Math Skills List published by the Migrant Student Record Transfer System, covers 10 learning areas: readiness, number meaning, whole numbers, fractions, decimals, percent, measurement, geometry, probability and statistics, and sets. Each exercise is illustrated by a large…

Knowledge of Natural Kinds in Semantic Dementia and Alzheimer's Disease

ERIC Educational Resources Information Center

Cross, Katy; Smith, Edward E.; Grossman, Murray

2008-01-01

We examined the semantic impairment for natural kinds in patients with probable Alzheimer's disease (AD) and semantic dementia (SD) using an inductive reasoning paradigm. To learn about the relationships between natural kind exemplars and how these are distinguished from manufactured artifacts, subjects judged the strength of arguments such as…

Temporal and Statistical Information in Causal Structure Learning

ERIC Educational Resources Information Center

McCormack, Teresa; Frosch, Caren; Patrick, Fiona; Lagnado, David

2015-01-01

Three experiments examined children's and adults' abilities to use statistical and temporal information to distinguish between common cause and causal chain structures. In Experiment 1, participants were provided with conditional probability information and/or temporal information and asked to infer the causal structure of a 3-variable mechanical…

Predicting the Probability for Faculty Adopting an Audience Response System in Higher Education

ERIC Educational Resources Information Center

Chan, Tan Fung Ivan; Borja, Marianne; Welch, Brett; Batiuk, Mary Ellen

2016-01-01

Instructional technologies can be effective tools to foster student engagement, but university faculty may be reluctant to integrate innovative and evidence-based modern learning technologies into instruction. Based on Rogers' diffusion of innovation theory, this quantitative, nonexperimental, one-shot cross-sectional survey determined what…

Surviving an Avalanche of Data

ERIC Educational Resources Information Center

English, Lyn D.

2013-01-01

The National Council of Teachers of Mathematics (NCTM) continues to emphasize the importance of early statistical learning; data analysis and probability was the Council's professional development "Focus of the Year" for 2007-2008. Such a focus is needed, especially given the results of the statistics items from the 2003 NAEP. As…

Identification of Hierarchies of Student Learning about Percentages Using Rasch Analysis

ERIC Educational Resources Information Center

Burfitt, Joan

2013-01-01

A review of the research literature indicated that there were probable orders in which students develop understandings and skills for calculating with percentages. Such calculations might include using models to represent percentages, knowing fraction equivalents, selection of strategies to solve problems and determination of percentage change. To…

Teachers' Self Efficacy: Is Reporting Non-Significant Results Essential?

ERIC Educational Resources Information Center

Moalosi, Smitta Waitshega Tefo

2013-01-01

Self-efficacious teachers are viewed as having the ability to organize relevant activities, patient with students who are struggling in learning, and spending more time designing relevant teaching activities. The teachers exhibit good performance and probably remain committed to their work. And they are committed to organizing appropriate teaching…

Engaging with Islamic Patterns

ERIC Educational Resources Information Center

Sugarman, Ian

2012-01-01

Islamic patterns were a regular feature in mathematics classrooms, and probably still feature in many wall displays. However, as part of the learning process, these ancient designs appear to have lost any significant contemporary appeal. Here, the power of software is engaged to bring the construction of Islamic type patterns up to date. Forget…

Intellectual Property Rights and The Classroom: What Teachers Can Do

ERIC Educational Resources Information Center

Falcon, Raymond

2010-01-01

Intellectual property rights restrict teachers' and students' ability to freely explore the intellectual realms of the classroom. Copyright laws protect the author and their work but disable other intellectuals from investigating probable learning environments. This paper will look at key issues where educational institutions are conflicting with…

What Makes Professional Development Effective? Results from a National Sample of Teachers.

ERIC Educational Resources Information Center

Garet, Michael S.; Porter, Andrew C.; Desimone, Laura; Birman, Beatrice F.; Yoon, Kwang Suk

2001-01-01

Used a national probability sample of 1,027 mathematics and science teachers to provide a large-scale empirical comparison of effects of different characteristics of professional development on teachers' learning. Results identify three core features of professional development that have significant positive effects on teachers' self-reported…

Finding Possibility and Probability Lessons in Sports

ERIC Educational Resources Information Center

Busadee, Nutjira; Laosinchai, Parames; Panijpan, Bhinyo

2011-01-01

Today's students demand that their lessons be real, interesting, relevant, and manageable. Mathematics is one subject that eludes many students partly because its traditional presentation lacks those elements that encourage students to learn. Easy accessibility through electronic media has exposed people all over the world to a variety of sports…

A Teacher's Repertoire: Developing Creative Pedagogies

ERIC Educational Resources Information Center

Das, Sharmistha; Dewhurst, Yvonne; Gray, Donald

2011-01-01

Promoting creativity in schools involves the development of characteristics such as self-motivation, confidence, curiosity and flexibility. It can be argued that the development of the first three of these probably relies on the last, all of which need to be supported by a "flexible learning context." However, this cannot work without a…

Learning and Visualizing Modulation Discriminative Radio Signal Features

DTIC Science & Technology

2016-09-01

implemented as a mapping of a sequence of in-phase quadrature ( IQ ) measurements generated by a software-defined radio to a probability distri- bution...over modulation classes. 3.1 TRAINING SNR EVALUATION Training CNNs on RF data raises the unique question of determining an optimal training SNR, that

Industry's Expectations in Relationship to College and Institute Resources.

ERIC Educational Resources Information Center

Justesen, Henry E.

Industry and education must share responsibility for broadening and deepening the talent pool within Canada's population in order to ensure a productive economy. First, the educational enterprise should: (1) review its present program to focus more attention on the career and employment probabilities ahead; (2) revise learning materials with…

Enhancing the Teaching and Learning of Mathematical Visual Images

ERIC Educational Resources Information Center

Quinnell, Lorna

2014-01-01

The importance of mathematical visual images is indicated by the introductory paragraph in the Statistics and Probability content strand of the Australian Curriculum, which draws attention to the importance of learners developing skills to analyse and draw inferences from data and "represent, summarise and interpret data and undertake…

The Design and Analysis of Efficient Learning Algorithms

DTIC Science & Technology

1991-01-01

will be c-close to the target concept with high probability. (Technically, their approach needs some minor modifications to handle, for instance, a...test in the sense that if cii = cik = cik then nothing can be concluded about the relative depth of 17, and Fk . However, our next lemnas give

Incorporating Digital E-Books into Educational Curriculum

ERIC Educational Resources Information Center

Turner, Freda

2005-01-01

The first books were probably the Egyptian scrolls of papyrus that provided lineal content to readers. Today (2005) the Internet technology presents the Internet lifestyle that has introduced electronic or e-books that can enrich learning experiences. E-books have an advantage over traditional books in that they offer hypertext linking, search…

A Longitudinal Perspective on Inductive Reasoning Tasks. Illuminating the Probability of Change

ERIC Educational Resources Information Center

Ifenthaler, Dirk; Seel, Norbert M.

2011-01-01

Cognitive scientists have studied internal cognitive structures, processes, and systems for decades in order to understand how they function in human learning. Nevertheless, questions concerning the diagnosis of changes in these cognitive structures while solving inductive reasoning tasks are still being scrutinized. This paper reports findings…

Two Attentional Models of Classical Conditioning: Variations in CS Effectiveness Revisited.

DTIC Science & Technology

1987-04-03

probability is in closer agreement with empirical expectations, tending to lie on a line with slope equal to 1. Experiments in pigeon autoshaping have shown...Gibbon, J., Farrell, L., Locurto, C.M., Duncan, H., & Terrace, H.S. (1980). Partial reinforcement in autoshaping with pigeons. Animal Learning and

Analysis of the “naming game” with learning errors in communications

NASA Astrophysics Data System (ADS)

Lou, Yang; Chen, Guanrong

2015-07-01

Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. By pair-wise iterative interactions, the population reaches consensus asymptotically. We study naming game with communication errors during pair-wise conversations, with error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is suggested. To that end, three typical topologies of communication networks, namely random-graph, small-world and scale-free networks, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but distinctively increase the requirement for memory of each agent during lexicon propagation; 2) the maximum number of different words held by the population increases linearly as the error rate increases; 3) without applying any strategy to eliminate learning errors, there is a threshold of the learning errors which impairs the convergence. The new findings may help to better understand the role of learning errors in naming game as well as in human language development from a network science perspective.

How floral odours are learned inside the bumblebee ( Bombus terrestris) nest

NASA Astrophysics Data System (ADS)

Molet, Mathieu; Chittka, Lars; Raine, Nigel E.

2009-02-01

Recruitment in social insects often involves not only inducing nestmates to leave the nest, but also communicating crucial information about finding profitable food sources. Although bumblebees transmit chemosensory information (floral scent), the transmission mechanism is unknown as mouth-to-mouth fluid transfer (as in honeybees) does not occur. Because recruiting bumblebees release a pheromone in the nest that triggers foraging in previously inactive workers, we tested whether this pheromone helps workers learn currently rewarding floral odours, as found in food social learning in rats. We exposed colonies to artificial recruitment pheromone, paired with anise scent. The pheromone did not facilitate learning of floral scent. However, we found that releasing floral scent in the air of the colony was sufficient to trigger learning and that learning performance was improved when the chemosensory cue was provided in the nectar in honeypots; probably because it guarantees a tighter link between scent and reward, and possibly because gustatory cues are involved in addition to olfaction. Scent learning was maximal when anise-scented nectar was brought into the nest by demonstrator foragers, suggesting that previously unidentified cues provided by successful foragers play an important role in nestmates learning new floral odours.

Analysis of the "naming game" with learning errors in communications.

PubMed

Lou, Yang; Chen, Guanrong

2015-07-16

Naming game simulates the process of naming an objective by a population of agents organized in a certain communication network. By pair-wise iterative interactions, the population reaches consensus asymptotically. We study naming game with communication errors during pair-wise conversations, with error rates in a uniform probability distribution. First, a model of naming game with learning errors in communications (NGLE) is proposed. Then, a strategy for agents to prevent learning errors is suggested. To that end, three typical topologies of communication networks, namely random-graph, small-world and scale-free networks, are employed to investigate the effects of various learning errors. Simulation results on these models show that 1) learning errors slightly affect the convergence speed but distinctively increase the requirement for memory of each agent during lexicon propagation; 2) the maximum number of different words held by the population increases linearly as the error rate increases; 3) without applying any strategy to eliminate learning errors, there is a threshold of the learning errors which impairs the convergence. The new findings may help to better understand the role of learning errors in naming game as well as in human language development from a network science perspective.

Improved Membership Probability for Moving Groups: Bayesian and Machine Learning Approaches

NASA Astrophysics Data System (ADS)

Lee, Jinhee; Song, Inseok

2018-01-01

Gravitationally unbound loose stellar associations (i.e., young nearby moving groups: moving groups hereafter) have been intensively explored because they are important in planet and disk formation studies, exoplanet imaging, and age calibration. Among the many efforts devoted to the search for moving group members, a Bayesian approach (e.g.,using the code BANYAN) has become popular recently because of the many advantages it offers. However, the resultant membership probability needs to be carefully adopted because of its sensitive dependence on input models. In this study, we have developed an improved membership calculation tool focusing on the beta-Pic moving group. We made three improvements for building models used in BANYAN II: (1) updating a list of accepted members by re-assessing memberships in terms of position, motion, and age, (2) investigating member distribution functions in XYZ, and (3) exploring field star distribution functions in XYZUVW. Our improved tool can change membership probability up to 70%. Membership probability is critical and must be better defined. For example, our code identifies only one third of the candidate members in SIMBAD that are believed to be kinematically associated with beta-Pic moving group.Additionally, we performed cluster analysis of young nearby stars using an unsupervised machine learning approach. As more moving groups and their members are identified, the complexity and ambiguity in moving group configuration has been increased. To clarify this issue, we analyzed ~4,000 X-ray bright young stellar candidates. Here, we present the preliminary results. By re-identifying moving groups with the least human intervention, we expect to understand the composition of the solar neighborhood. Moreover better defined moving group membership will help us understand star formation and evolution in relatively low density environments; especially for the low-mass stars which will be identified in the coming Gaia release.

Human instrumental performance in ratio and interval contingencies: A challenge for associative theory.

PubMed

Pérez, Omar D; Aitken, Michael R F; Zhukovsky, Peter; Soto, Fabián A; Urcelay, Gonzalo P; Dickinson, Anthony

2016-12-15

Associative learning theories regard the probability of reinforcement as the critical factor determining responding. However, the role of this factor in instrumental conditioning is not completely clear. In fact, free-operant experiments show that participants respond at a higher rate on variable ratio than on variable interval schedules even though the reinforcement probability is matched between the schedules. This difference has been attributed to the differential reinforcement of long inter-response times (IRTs) by interval schedules, which acts to slow responding. In the present study, we used a novel experimental design to investigate human responding under random ratio (RR) and regulated probability interval (RPI) schedules, a type of interval schedule that sets a reinforcement probability independently of the IRT duration. Participants responded on each type of schedule before a final choice test in which they distributed responding between two schedules similar to those experienced during training. Although response rates did not differ during training, the participants responded at a lower rate on the RPI schedule than on the matched RR schedule during the choice test. This preference cannot be attributed to a higher probability of reinforcement for long IRTs and questions the idea that similar associative processes underlie classical and instrumental conditioning.

Impaired sequential and partially compensated probabilistic skill learning in Parkinson's disease.

PubMed

Kemény, Ferenc; Demeter, Gyula; Racsmány, Mihály; Valálik, István; Lukács, Ágnes

2018-06-08

The striatal dopaminergic dysfunction in Parkinson's disease (PD) has been associated with deficits in skill learning in numerous studies, but some of the findings remain controversial. Our aim was to explore the generality of the learning deficit using two widely reported skill learning tasks in the same group of Parkinson's patients. Thirty-four patients with PD (mean age: 62.83 years, SD: 7.67) were compared to age-matched healthy adults. Two tasks were employed: the Serial Reaction Time Task (SRT), testing the learning of motor sequences, and the Weather Prediction (WP) task, testing non-sequential probabilistic category learning. On the SRT task, patients with PD showed no significant evidence for sequence learning. These results support and also extend previous findings, suggesting that motor skill learning is vulnerable in PD. On the WP task, the PD group showed the same amount of learning as controls, but they exploited qualitatively different strategies in predicting the target categories. While controls typically combined probabilities from multiple predicting cues, patients with PD instead focused on individual cues. We also found moderate to high correlations between the different measures of skill learning. These findings support our hypothesis that skill learning is generally impaired in PD, and can in some cases be compensated by relying on alternative learning strategies. © 2018 The Authors. Journal of Neuropsychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.

Comparison of statistical models for writer verification

NASA Astrophysics Data System (ADS)

Srihari, Sargur; Ball, Gregory R.

2009-01-01

A novel statistical model for determining whether a pair of documents, a known and a questioned, were written by the same individual is proposed. The goal of this formulation is to learn the specific uniqueness of style in a particular author's writing, given the known document. Since there are often insufficient samples to extrapolate a generalized model of an writer's handwriting based solely on the document, we instead generalize over the differences between the author and a large population of known different writers. This is in contrast to an earlier model proposed whereby probability distributions were a priori without learning. We show the performance of the model along with a comparison in performance to the non-learning, older model, which shows significant improvement.

Optimal structure of metaplasticity for adaptive learning

PubMed Central

2017-01-01

Learning from reward feedback in a changing environment requires a high degree of adaptability, yet the precise estimation of reward information demands slow updates. In the framework of estimating reward probability, here we investigated how this tradeoff between adaptability and precision can be mitigated via metaplasticity, i.e. synaptic changes that do not always alter synaptic efficacy. Using the mean-field and Monte Carlo simulations we identified ‘superior’ metaplastic models that can substantially overcome the adaptability-precision tradeoff. These models can achieve both adaptability and precision by forming two separate sets of meta-states: reservoirs and buffers. Synapses in reservoir meta-states do not change their efficacy upon reward feedback, whereas those in buffer meta-states can change their efficacy. Rapid changes in efficacy are limited to synapses occupying buffers, creating a bottleneck that reduces noise without significantly decreasing adaptability. In contrast, more-populated reservoirs can generate a strong signal without manifesting any observable plasticity. By comparing the behavior of our model and a few competing models during a dynamic probability estimation task, we found that superior metaplastic models perform close to optimally for a wider range of model parameters. Finally, we found that metaplastic models are robust to changes in model parameters and that metaplastic transitions are crucial for adaptive learning since replacing them with graded plastic transitions (transitions that change synaptic efficacy) reduces the ability to overcome the adaptability-precision tradeoff. Overall, our results suggest that ubiquitous unreliability of synaptic changes evinces metaplasticity that can provide a robust mechanism for mitigating the tradeoff between adaptability and precision and thus adaptive learning. PMID:28658247

Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries.

PubMed

Jochems, Arthur; Deist, Timo M; El Naqa, Issam; Kessler, Marc; Mayo, Chuck; Reeves, Jackson; Jolly, Shruti; Matuszak, Martha; Ten Haken, Randall; van Soest, Johan; Oberije, Cary; Faivre-Finn, Corinne; Price, Gareth; de Ruysscher, Dirk; Lambin, Philippe; Dekker, Andre

2017-10-01

Tools for survival prediction for non-small cell lung cancer (NSCLC) patients treated with chemoradiation or radiation therapy are of limited quality. In this work, we developed a predictive model of survival at 2 years. The model is based on a large volume of historical patient data and serves as a proof of concept to demonstrate the distributed learning approach. Clinical data from 698 lung cancer patients, treated with curative intent with chemoradiation or radiation therapy alone, were collected and stored at 2 different cancer institutes (559 patients at Maastro clinic (Netherlands) and 139 at Michigan university [United States]). The model was further validated on 196 patients originating from The Christie (United Kingdon). A Bayesian network model was adapted for distributed learning (the animation can be viewed at https://www.youtube.com/watch?v=ZDJFOxpwqEA). Two-year posttreatment survival was chosen as the endpoint. The Maastro clinic cohort data are publicly available at https://www.cancerdata.org/publication/developing-and-validating-survival-prediction-model-nsclc-patients-through-distributed, and the developed models can be found at www.predictcancer.org. Variables included in the final model were T and N category, age, performance status, and total tumor dose. The model has an area under the curve (AUC) of 0.66 on the external validation set and an AUC of 0.62 on a 5-fold cross validation. A model based on the T and N category performed with an AUC of 0.47 on the validation set, significantly worse than our model (P<.001). Learning the model in a centralized or distributed fashion yields a minor difference on the probabilities of the conditional probability tables (0.6%); the discriminative performance of the models on the validation set is similar (P=.26). Distributed learning from federated databases allows learning of predictive models on data originating from multiple institutions while avoiding many of the data-sharing barriers. We believe that distributed learning is the future of sharing data in health care. Copyright © 2017 The Author(s). Published by Elsevier Inc. All rights reserved.

Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification.

PubMed

Lu, Huijuan; Wei, Shasha; Zhou, Zili; Miao, Yanzi; Lu, Yi

2015-01-01

The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.

Selective visual attention and motivation: the consequences of value learning in an attentional blink task.

PubMed

Raymond, Jane E; O'Brien, Jennifer L

2009-08-01

Learning to associate the probability and value of behavioral outcomes with specific stimuli (value learning) is essential for rational decision making. However, in demanding cognitive conditions, access to learned values might be constrained by limited attentional capacity. We measured recognition of briefly presented faces seen previously in a value-learning task involving monetary wins and losses; the recognition task was performed both with and without constraints on available attention. Regardless of available attention, recognition was substantially enhanced for motivationally salient stimuli (i.e., stimuli highly predictive of outcomes), compared with equally familiar stimuli that had weak or no motivational salience, and this effect was found regardless of valence (win or loss). However, when attention was constrained (because stimuli were presented during an attentional blink, AB), valence determined recognition; win-associated faces showed no AB, but all other faces showed large ABs. Motivational salience acts independently of attention to modulate simple perceptual decisions, but when attention is limited, visual processing is biased in favor of reward-associated stimuli.

Fast and Epsilon-Optimal Discretized Pursuit Learning Automata.

PubMed

Zhang, JunQi; Wang, Cheng; Zhou, MengChu

2015-10-01

Learning automata (LA) are powerful tools for reinforcement learning. A discretized pursuit LA is the most popular one among them. During an iteration its operation consists of three basic phases: 1) selecting the next action; 2) finding the optimal estimated action; and 3) updating the state probability. However, when the number of actions is large, the learning becomes extremely slow because there are too many updates to be made at each iteration. The increased updates are mostly from phases 1 and 3. A new fast discretized pursuit LA with assured ε -optimality is proposed to perform both phases 1 and 3 with the computational complexity independent of the number of actions. Apart from its low computational complexity, it achieves faster convergence speed than the classical one when operating in stationary environments. This paper can promote the applications of LA toward the large-scale-action oriented area that requires efficient reinforcement learning tools with assured ε -optimality, fast convergence speed, and low computational complexity for each iteration.

Energy management using virtual reality improves 2000-m rowing performance.

PubMed

Hoffmann, Charles P; Filippeschi, Alessandro; Ruffaldi, Emanuele; Bardy, Benoit G

2014-01-01

Elite-standard rowers tend to use a fast-start strategy followed by an inverted parabolic-shaped speed profile in 2000-m races. This strategy is probably the best to manage energy resources during the race and maximise performance. This study investigated the use of virtual reality (VR) with novice rowers as a means to learn about energy management. Participants from an avatar group (n = 7) were instructed to track a virtual boat on a screen, whose speed was set individually to follow the appropriate to-be-learned speed profile. A control group (n = 8) followed an indoor training programme. In spite of similar physiological characteristics in the groups, the avatar group learned and maintained the required profile, resulting in an improved performance (i.e. a decrease in race duration), whereas the control group did not. These results suggest that VR is a means to learn an energy-related skill and improve performance.

Can we (control) Engineer the degree learning process?

NASA Astrophysics Data System (ADS)

White, A. S.; Censlive, M.; Neilsen, D.

2014-07-01

This paper investigates how control theory could be applied to learning processes in engineering education. The initial point for the analysis is White's Double Loop learning model of human automation control modified for the education process where a set of governing principals is chosen, probably by the course designer. After initial training the student decides unknowingly on a mental map or model. After observing how the real world is behaving, a strategy to achieve the governing variables is chosen and a set of actions chosen. This may not be a conscious operation, it maybe completely instinctive. These actions will cause some consequences but not until a certain time delay. The current model is compared with the work of Hollenbeck on goal setting, Nelson's model of self-regulation and that of Abdulwahed, Nagy and Blanchard at Loughborough who investigated control methods applied to the learning process.

Belief state representation in the dopamine system.

PubMed

Babayan, Benedicte M; Uchida, Naoshige; Gershman, Samuel J

2018-05-14

Learning to predict future outcomes is critical for driving appropriate behaviors. Reinforcement learning (RL) models have successfully accounted for such learning, relying on reward prediction errors (RPEs) signaled by midbrain dopamine neurons. It has been proposed that when sensory data provide only ambiguous information about which state an animal is in, it can predict reward based on a set of probabilities assigned to hypothetical states (called the belief state). Here we examine how dopamine RPEs and subsequent learning are regulated under state uncertainty. Mice are first trained in a task with two potential states defined by different reward amounts. During testing, intermediate-sized rewards are given in rare trials. Dopamine activity is a non-monotonic function of reward size, consistent with RL models operating on belief states. Furthermore, the magnitude of dopamine responses quantitatively predicts changes in behavior. These results establish the critical role of state inference in RL.

Probabilistic motor sequence learning in a virtual reality serial reaction time task.

PubMed

Sense, Florian; van Rijn, Hedderik

2018-01-01

The serial reaction time task is widely used to study learning and memory. The task is traditionally administered by showing target positions on a computer screen and collecting responses using a button box or keyboard. By comparing response times to random or sequenced items or by using different transition probabilities, various forms of learning can be studied. However, this traditional laboratory setting limits the number of possible experimental manipulations. Here, we present a virtual reality version of the serial reaction time task and show that learning effects emerge as expected despite the novel way in which responses are collected. We also show that response times are distributed as expected. The current experiment was conducted in a blank virtual reality room to verify these basic principles. For future applications, the technology can be used to modify the virtual reality environment in any conceivable way, permitting a wide range of previously impossible experimental manipulations.

Alterations in choice behavior by manipulations of world model.

PubMed

Green, C S; Benson, C; Kersten, D; Schrater, P

2010-09-14

How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) "probability matching"-a consistent example of suboptimal choice behavior seen in humans-occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning.

Alterations in choice behavior by manipulations of world model

PubMed Central

Green, C. S.; Benson, C.; Kersten, D.; Schrater, P.

2010-01-01

How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) “probability matching”—a consistent example of suboptimal choice behavior seen in humans—occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning. PMID:20805507

Hebbian learning in a model with dynamic rate-coded neurons: an alternative to the generative model approach for learning receptive fields from natural scenes.

PubMed

Hamker, Fred H; Wiltschut, Jan

2007-09-01

Most computational models of coding are based on a generative model according to which the feedback signal aims to reconstruct the visual scene as close as possible. We here explore an alternative model of feedback. It is derived from studies of attention and thus, probably more flexible with respect to attentive processing in higher brain areas. According to this model, feedback implements a gain increase of the feedforward signal. We use a dynamic model with presynaptic inhibition and Hebbian learning to simultaneously learn feedforward and feedback weights. The weights converge to localized, oriented, and bandpass filters similar as the ones found in V1. Due to presynaptic inhibition the model predicts the organization of receptive fields within the feedforward pathway, whereas feedback primarily serves to tune early visual processing according to the needs of the task.

Bilinguals’ Existing Languages Benefit Vocabulary Learning in a Third Language

PubMed Central

Bartolotti, James; Marian, Viorica

2017-01-01

Learning a new language involves substantial vocabulary acquisition. Learners can accelerate this process by relying on words with native-language overlap, such as cognates. For bilingual third language learners, it is necessary to determine how their two existing languages interact during novel language learning. A scaffolding account predicts transfer from either language for individual words, whereas an accumulation account predicts cumulative transfer from both languages. To compare these accounts, twenty English-German bilingual adults were taught an artificial language containing 48 novel written words that varied orthogonally in English and German wordlikeness (neighborhood size and orthotactic probability). Wordlikeness in each language improved word production accuracy, and similarity to one language provided the same benefit as dual-language overlap. In addition, participants’ memory for novel words was affected by the statistical distributions of letters in the novel language. Results indicate that bilinguals utilize both languages during third language acquisition, supporting a scaffolding learning model. PMID:28781384

Bilinguals' Existing Languages Benefit Vocabulary Learning in a Third Language.

PubMed

Bartolotti, James; Marian, Viorica

2017-03-01

Learning a new language involves substantial vocabulary acquisition. Learners can accelerate this process by relying on words with native-language overlap, such as cognates. For bilingual third language learners, it is necessary to determine how their two existing languages interact during novel language learning. A scaffolding account predicts transfer from either language for individual words, whereas an accumulation account predicts cumulative transfer from both languages. To compare these accounts, twenty English-German bilingual adults were taught an artificial language containing 48 novel written words that varied orthogonally in English and German wordlikeness (neighborhood size and orthotactic probability). Wordlikeness in each language improved word production accuracy, and similarity to one language provided the same benefit as dual-language overlap. In addition, participants' memory for novel words was affected by the statistical distributions of letters in the novel language. Results indicate that bilinguals utilize both languages during third language acquisition, supporting a scaffolding learning model.

An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks

PubMed Central

Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

2016-01-01

To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It’s theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods. PMID:27669250

An Online Dictionary Learning-Based Compressive Data Gathering Algorithm in Wireless Sensor Networks.

PubMed

Wang, Donghao; Wan, Jiangwen; Chen, Junying; Zhang, Qiang

2016-09-22

To adapt to sense signals of enormous diversities and dynamics, and to decrease the reconstruction errors caused by ambient noise, a novel online dictionary learning method-based compressive data gathering (ODL-CDG) algorithm is proposed. The proposed dictionary is learned from a two-stage iterative procedure, alternately changing between a sparse coding step and a dictionary update step. The self-coherence of the learned dictionary is introduced as a penalty term during the dictionary update procedure. The dictionary is also constrained with sparse structure. It's theoretically demonstrated that the sensing matrix satisfies the restricted isometry property (RIP) with high probability. In addition, the lower bound of necessary number of measurements for compressive sensing (CS) reconstruction is given. Simulation results show that the proposed ODL-CDG algorithm can enhance the recovery accuracy in the presence of noise, and reduce the energy consumption in comparison with other dictionary based data gathering methods.

What predicts successful literacy acquisition in a second language?

PubMed Central

Frost, Ram; Siegelman, Noam; Narkiss, Alona; Afek, Liron

2013-01-01

We examined whether success (or failure) in assimilating the structure of a second language could be predicted by general statistical learning abilities that are non-linguistic in nature. We employed a visual statistical learning (VSL) task, monitoring our participants’ implicit learning of the transitional probabilities of visual shapes. A pretest revealed that performance in the VSL task is not correlated with abilities related to a general G factor or working memory. We found that native speakers of English who picked up the implicit statistical structure embedded in the continuous stream of shapes, on average, better assimilated the Semitic structure of Hebrew words. Our findings thus suggest that languages and their writing systems are characterized by idiosyncratic correlations of form and meaning, and these are picked up in the process of literacy acquisition, as they are picked up in any other type of learning, for the purpose of making sense of the environment. PMID:23698615

Overcoming Learning Aversion in Evaluating and Managing Uncertain Risks.

PubMed

Cox, Louis Anthony Tony

2015-10-01

Decision biases can distort cost-benefit evaluations of uncertain risks, leading to risk management policy decisions with predictably high retrospective regret. We argue that well-documented decision biases encourage learning aversion, or predictably suboptimal learning and premature decision making in the face of high uncertainty about the costs, risks, and benefits of proposed changes. Biases such as narrow framing, overconfidence, confirmation bias, optimism bias, ambiguity aversion, and hyperbolic discounting of the immediate costs and delayed benefits of learning, contribute to deficient individual and group learning, avoidance of information seeking, underestimation of the value of further information, and hence needlessly inaccurate risk-cost-benefit estimates and suboptimal risk management decisions. In practice, such biases can create predictable regret in selection of potential risk-reducing regulations. Low-regret learning strategies based on computational reinforcement learning models can potentially overcome some of these suboptimal decision processes by replacing aversion to uncertain probabilities with actions calculated to balance exploration (deliberate experimentation and uncertainty reduction) and exploitation (taking actions to maximize the sum of expected immediate reward, expected discounted future reward, and value of information). We discuss the proposed framework for understanding and overcoming learning aversion and for implementing low-regret learning strategies using regulation of air pollutants with uncertain health effects as an example. © 2015 Society for Risk Analysis.

Learning and liking of melody and harmony: further studies in artificial grammar learning.

PubMed

Loui, Psyche

2012-10-01

Much of what we know and love about music is based on implicitly acquired mental representations of musical pitches and the relationships between them. While previous studies have shown that these mental representations of music can be acquired rapidly and can influence preference, it is still unclear which aspects of music influence learning and preference formation. This article reports two experiments that use an artificial musical system to examine two questions: (1) which aspects of music matter most for learning, and (2) which aspects of music matter most for preference formation. Two aspects of music are tested: melody and harmony. In Experiment 1 we tested the learning and liking of a new musical system that is manipulated melodically so that only some of the possible conditional probabilities between successive notes are presented. In Experiment 2 we administered the same tests for learning and liking, but we used a musical system that is manipulated harmonically to eliminate the property of harmonic whole-integer ratios between pitches. Results show that disrupting melody (Experiment 1) disabled the learning of music without disrupting preference formation, whereas disrupting harmony (Experiment 2) does not affect learning and memory but disrupts preference formation. Results point to a possible dissociation between learning and preference in musical knowledge. Copyright © 2012 Cognitive Science Society, Inc.

Influence of maneuverability on helicopter combat effectiveness

NASA Technical Reports Server (NTRS)

Falco, M.; Smith, R.

1982-01-01

A computational procedure employing a stochastic learning method in conjunction with dynamic simulation of helicopter flight and weapon system operation was used to derive helicopter maneuvering strategies. The derived strategies maximize either survival or kill probability and are in the form of a feedback control based upon threat visual or warning system cues. Maneuverability parameters implicit in the strategy development include maximum longitudinal acceleration and deceleration, maximum sustained and transient load factor turn rate at forward speed, and maximum pedal turn rate and lateral acceleration at hover. Results are presented in terms of probability of skill for all combat initial conditions for two threat categories.

Probability Distributome: A Web Computational Infrastructure for Exploring the Properties, Interrelations, and Applications of Probability Distributions.

PubMed

Dinov, Ivo D; Siegrist, Kyle; Pearl, Dennis K; Kalinin, Alexandr; Christou, Nicolas

2016-06-01

Probability distributions are useful for modeling, simulation, analysis, and inference on varieties of natural processes and physical phenomena. There are uncountably many probability distributions. However, a few dozen families of distributions are commonly defined and are frequently used in practice for problem solving, experimental applications, and theoretical studies. In this paper, we present a new computational and graphical infrastructure, the Distributome , which facilitates the discovery, exploration and application of diverse spectra of probability distributions. The extensible Distributome infrastructure provides interfaces for (human and machine) traversal, search, and navigation of all common probability distributions. It also enables distribution modeling, applications, investigation of inter-distribution relations, as well as their analytical representations and computational utilization. The entire Distributome framework is designed and implemented as an open-source, community-built, and Internet-accessible infrastructure. It is portable, extensible and compatible with HTML5 and Web2.0 standards (http://Distributome.org). We demonstrate two types of applications of the probability Distributome resources: computational research and science education. The Distributome tools may be employed to address five complementary computational modeling applications (simulation, data-analysis and inference, model-fitting, examination of the analytical, mathematical and computational properties of specific probability distributions, and exploration of the inter-distributional relations). Many high school and college science, technology, engineering and mathematics (STEM) courses may be enriched by the use of modern pedagogical approaches and technology-enhanced methods. The Distributome resources provide enhancements for blended STEM education by improving student motivation, augmenting the classical curriculum with interactive webapps, and overhauling the learning assessment protocols.

Probability Distributome: A Web Computational Infrastructure for Exploring the Properties, Interrelations, and Applications of Probability Distributions

PubMed Central

Dinov, Ivo D.; Siegrist, Kyle; Pearl, Dennis K.; Kalinin, Alexandr; Christou, Nicolas

2015-01-01

Probability distributions are useful for modeling, simulation, analysis, and inference on varieties of natural processes and physical phenomena. There are uncountably many probability distributions. However, a few dozen families of distributions are commonly defined and are frequently used in practice for problem solving, experimental applications, and theoretical studies. In this paper, we present a new computational and graphical infrastructure, the Distributome, which facilitates the discovery, exploration and application of diverse spectra of probability distributions. The extensible Distributome infrastructure provides interfaces for (human and machine) traversal, search, and navigation of all common probability distributions. It also enables distribution modeling, applications, investigation of inter-distribution relations, as well as their analytical representations and computational utilization. The entire Distributome framework is designed and implemented as an open-source, community-built, and Internet-accessible infrastructure. It is portable, extensible and compatible with HTML5 and Web2.0 standards (http://Distributome.org). We demonstrate two types of applications of the probability Distributome resources: computational research and science education. The Distributome tools may be employed to address five complementary computational modeling applications (simulation, data-analysis and inference, model-fitting, examination of the analytical, mathematical and computational properties of specific probability distributions, and exploration of the inter-distributional relations). Many high school and college science, technology, engineering and mathematics (STEM) courses may be enriched by the use of modern pedagogical approaches and technology-enhanced methods. The Distributome resources provide enhancements for blended STEM education by improving student motivation, augmenting the classical curriculum with interactive webapps, and overhauling the learning assessment protocols. PMID:27158191

Recognizing human actions by learning and matching shape-motion prototype trees.

PubMed

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2012-03-01

A shape-motion prototype-based approach is introduced for action recognition. The approach represents an action as a sequence of prototypes for efficient and flexible action matching in long video sequences. During training, an action prototype tree is learned in a joint shape and motion space via hierarchical K-means clustering and each training sequence is represented as a labeled prototype sequence; then a look-up table of prototype-to-prototype distances is generated. During testing, based on a joint probability model of the actor location and action prototype, the actor is tracked while a frame-to-prototype correspondence is established by maximizing the joint probability, which is efficiently performed by searching the learned prototype tree; then actions are recognized using dynamic prototype sequence matching. Distance measures used for sequence matching are rapidly obtained by look-up table indexing, which is an order of magnitude faster than brute-force computation of frame-to-frame distances. Our approach enables robust action matching in challenging situations (such as moving cameras, dynamic backgrounds) and allows automatic alignment of action sequences. Experimental results demonstrate that our approach achieves recognition rates of 92.86 percent on a large gesture data set (with dynamic backgrounds), 100 percent on the Weizmann action data set, 95.77 percent on the KTH action data set, 88 percent on the UCF sports data set, and 87.27 percent on the CMU action data set.

Bayesian deterministic decision making: a normative account of the operant matching law and heavy-tailed reward history dependency of choices.

PubMed

Saito, Hiroshi; Katahira, Kentaro; Okanoya, Kazuo; Okada, Masato

2014-01-01

The decision making behaviors of humans and animals adapt and then satisfy an "operant matching law" in certain type of tasks. This was first pointed out by Herrnstein in his foraging experiments on pigeons. The matching law has been one landmark for elucidating the underlying processes of decision making and its learning in the brain. An interesting question is whether decisions are made deterministically or probabilistically. Conventional learning models of the matching law are based on the latter idea; they assume that subjects learn choice probabilities of respective alternatives and decide stochastically with the probabilities. However, it is unknown whether the matching law can be accounted for by a deterministic strategy or not. To answer this question, we propose several deterministic Bayesian decision making models that have certain incorrect beliefs about an environment. We claim that a simple model produces behavior satisfying the matching law in static settings of a foraging task but not in dynamic settings. We found that the model that has a belief that the environment is volatile works well in the dynamic foraging task and exhibits undermatching, which is a slight deviation from the matching law observed in many experiments. This model also demonstrates the double-exponential reward history dependency of a choice and a heavier-tailed run-length distribution, as has recently been reported in experiments on monkeys.

The assessment of knowledge and learning in competence spaces: The gain-loss model for dependent skills.

PubMed

Anselmi, Pasquale; Stefanutti, Luca; de Chiusole, Debora; Robusto, Egidio

2017-11-01

The gain-loss model (GaLoM) is a formal model for assessing knowledge and learning. In its original formulation, the GaLoM assumes independence among the skills. Such an assumption is not reasonable in several domains, in which some preliminary knowledge is the foundation for other knowledge. This paper presents an extension of the GaLoM to the case in which the skills are not independent, and the dependence relation among them is described by a well-graded competence space. The probability of mastering skill s at the pretest is conditional on the presence of all skills on which s depends. The probabilities of gaining or losing skill s when moving from pretest to posttest are conditional on the mastery of s at the pretest, and on the presence at the posttest of all skills on which s depends. Two formulations of the model are presented, in which the learning path is allowed to change from pretest to posttest or not. A simulation study shows that models based on the true competence space obtain a better fit than models based on false competence spaces, and are also characterized by a higher assessment accuracy. An empirical application shows that models based on pedagogically sound assumptions about the dependencies among the skills obtain a better fit than models assuming independence among the skills. © 2017 The British Psychological Society.

Machine Learning-based Individual Assessment of Cortical Atrophy Pattern in Alzheimer's Disease Spectrum: Development of the Classifier and Longitudinal Evaluation.

PubMed

Lee, Jin San; Kim, Changsoo; Shin, Jeong-Hyeon; Cho, Hanna; Shin, Dae-Seock; Kim, Nakyoung; Kim, Hee Jin; Kim, Yeshin; Lockhart, Samuel N; Na, Duk L; Seo, Sang Won; Seong, Joon-Kyung

2018-03-07

To develop a new method for measuring Alzheimer's disease (AD)-specific similarity of cortical atrophy patterns at the individual-level, we employed an individual-level machine learning algorithm. A total of 869 cognitively normal (CN) individuals and 473 patients with probable AD dementia who underwent high-resolution 3T brain MRI were included. We propose a machine learning-based method for measuring the similarity of an individual subject's cortical atrophy pattern with that of a representative AD patient cohort. In addition, we validated this similarity measure in two longitudinal cohorts consisting of 79 patients with amnestic-mild cognitive impairment (aMCI) and 27 patients with probable AD dementia. Surface-based morphometry classifier for discriminating AD from CN showed sensitivity and specificity values of 87.1% and 93.3%, respectively. In the longitudinal validation study, aMCI-converts had higher atrophy similarity at both baseline (p < 0.001) and first year visits (p < 0.001) relative to non-converters. Similarly, AD patients with faster decline had higher atrophy similarity than slower decliners at baseline (p = 0.042), first year (p = 0.028), and third year visits (p = 0.027). The AD-specific atrophy similarity measure is a novel approach for the prediction of dementia risk and for the evaluation of AD trajectories on an individual subject level.

Reinforcement learning and decision making in monkeys during a competitive game.

PubMed

Lee, Daeyeol; Conroy, Michelle L; McGreevy, Benjamin P; Barraclough, Dominic J

2004-12-01

Animals living in a dynamic environment must adjust their decision-making strategies through experience. To gain insights into the neural basis of such adaptive decision-making processes, we trained monkeys to play a competitive game against a computer in an oculomotor free-choice task. The animal selected one of two visual targets in each trial and was rewarded only when it selected the same target as the computer opponent. To determine how the animal's decision-making strategy can be affected by the opponent's strategy, the computer opponent was programmed with three different algorithms that exploited different aspects of the animal's choice and reward history. When the computer selected its targets randomly with equal probabilities, animals selected one of the targets more often, violating the prediction of probability matching, and their choices were systematically influenced by the choice history of the two players. When the computer exploited only the animal's choice history but not its reward history, animal's choice became more independent of its own choice history but was still related to the choice history of the opponent. This bias was substantially reduced, but not completely eliminated, when the computer used the choice history of both players in making its predictions. These biases were consistent with the predictions of reinforcement learning, suggesting that the animals sought optimal decision-making strategies using reinforcement learning algorithms.

Empathy and feedback processing in active and observational learning.

PubMed

Rak, Natalia; Bellebaum, Christian; Thoma, Patrizia

2013-12-01

The feedback-related negativity (FRN) and the P300 have been related to the processing of one's own and other individuals' feedback during both active and observational learning. The aim of the present study was to elucidate the role of trait-empathic responding with regard to the modulation of the neural correlates of observational learning in particular. Thirty-four healthy participants completed an active and an observational learning task. On both tasks, the participants' aim was to maximize their monetary gain by choosing from two stimuli the one that showed the higher probability of reward. Participants gained insight into the stimulus-reward contingencies according to monetary feedback presented after they had made an active choice or by observing the choices of a virtual partner. Participants showed a general improvement in learning performance on both learning tasks. P200, FRN, and P300 amplitudes were larger during active, as compared with observational, learning. Furthermore, nonreward elicited a significantly more negative FRN than did reward in the active learning task, while only a trend was observed for observational learning. Distinct subcomponents of trait cognitive empathy were related to poorer performance and smaller P300 amplitudes for observational learning only. Taken together, both the learning performance and event-related potentials during observational learning are affected by different aspects of trait cognitive empathy, and certain types of observational learning may actually be disrupted by a higher tendency to understand and adopt other people's perspectives.

A Generalization of "n Choose r"

ERIC Educational Resources Information Center

Skurnick, Ronald

2005-01-01

The subject matter presented in this article can be used in the classroom to enrich the learning experience of students taking a course that includes a unit on combinatorics, such as discrete mathematics, graph theory, or probability. In order to provide such students with the background needed to appreciate the significance of the generalization…

Properties of the Bayesian Knowledge Tracing Model

ERIC Educational Resources Information Center

van de Sande, Brett

2013-01-01

Bayesian Knowledge Tracing is used very widely to model student learning. It comes in two different forms: The first form is the Bayesian Knowledge Tracing "hidden Markov model" which predicts the probability of correct application of a skill as a function of the number of previous opportunities to apply that skill and the model…

United States Middle School Students' Perspectives on Learning Statistics

ERIC Educational Resources Information Center

Dwyer, Jerry; Moorhouse, Kim; Colwell, Malinda J.

2009-01-01

This paper describes an intervention at the 8th grade level where university mathematics researchers presented a series of lessons on introductory concepts in probability and statistics. Pre- and post-tests, and interviews were conducted to examine whether or not students at this grade level can understand these concepts. Students showed a…

What Makes Lectures "Unmissable"? Insights into Teaching Excellence and Active Learning

ERIC Educational Resources Information Center

Revell, Andrea; Wainwright, Emma

2009-01-01

This research explores "teaching excellence" by examining the perceptions of geography academics and students at Brunel University as to what makes a lecture "unmissable". The findings from 10 interviews with lecturers and five focus groups with undergraduate students suggest that whilst there is probably no such thing as an…

Why Cramming Doesn't Work

ERIC Educational Resources Information Center

Glenn, David

2007-01-01

Most college instructors probably are not about to start giving the daily quizzes that some researchers recommend to improve learning, so students might want to try testing themselves when they study on their own. But there's a catch: When people study with flashcards, by far the most common method of self-quizzing, they're notoriously bad at…

Numeracy, Ratio Bias, and Denominator Neglect in Judgments of Risk and Probability

ERIC Educational Resources Information Center

Reyna, Valerie F.; Brainerd, Charles J.

2008-01-01

"Numeracy," so-called on analogy with literacy, is essential for making health and other social judgments in everyday life [Reyna, V. F., & Brainerd, C. J. (in press). The importance of mathematics in health and human judgment: Numeracy, risk communication, and medical decision making. "Learning and Individual Differences."]. Recent research on…

ADHD and Executive Functions: Lessons Learned from Research

ERIC Educational Resources Information Center

Mahone, Mark E.; Silverman, Wayne

2008-01-01

Today, attention deficit hyperactivity disorder (ADHD) is the most common and most studied psychiatric disorder of childhood, affecting approximately five percent of school-aged children. That means that there are probably at least two children with ADHD in any average elementary school class. In the last 20 years, there has been an explosion in…

Madness comes a month early. February was chock full of corporate retrenchments, broken deals and red ink.

PubMed

Saphir, A

1999-03-01

After a slew of retrenchments, split-ups and sliding profits last month, healthcare companies are learning that they probably need to change their game plans and get back to basics. Experts say the same cycle has occurred in other industries and is expected in healthcare.

Grammars Leak: Modeling How Phonotactic Generalizations Interact within the Grammar

ERIC Educational Resources Information Center

Martin, Andrew

2011-01-01

I present evidence from Navajo and English that weaker, gradient versions of morpheme-internal phonotactic constraints, such as the ban on geminate consonants in English, hold even across prosodic word boundaries. I argue that these lexical biases are the result of a MAXIMUM ENTROPY phonotactic learning algorithm that maximizes the probability of…

Disappearing Species: The Social Challenge. Worldwatch Paper 22.

ERIC Educational Resources Information Center

Eckholm, Erik

A key question to ask in determining whether a solution will be found to the current worldwide destruction of plant and animal life is whether people will learn to reconcile effectively the demands of environmental conservationists and developers. Probably the most immediate threat which ecological destruction poses to human welfare is shrinkage…

How Transitional Probabilities and the Edge Effect Contribute to Listeners' Phonological Bootstrapping Success

ERIC Educational Resources Information Center

Sohail, Juwairia; Johnson, Elizabeth K.

2016-01-01

Much of what we know about the development of listeners' word segmentation strategies originates from the artificial language-learning literature. However, many artificial speech streams designed to study word segmentation lack a salient cue found in all natural languages: utterance boundaries. In this study, participants listened to a…

"The Scarlet Letter" from a Geometric Perspective

ERIC Educational Resources Information Center

Cozza, Barbara; McDonough, Patrick; Laboranti, Carol

2011-01-01

Many times teachers hear students say: "Why are we learning this? Why do we have to know this? When are we going to use this outside of class time?" These common questions are probably familiar to most high school teachers. An 11th-grade English teacher attended a university-school district professional development (PD) program on…

Student Distractor Choices on the Mathematics Virginia Standards of Learning Middle School Assessments

ERIC Educational Resources Information Center

Lewis, Virginia Vimpeny

2011-01-01

Number Concepts; Measurement; Geometry; Probability; Statistics; and Patterns, Functions and Algebra. Procedural Errors were further categorized into the following content categories: Computation; Measurement; Statistics; and Patterns, Functions, and Algebra. The results of the analysis showed the main sources of error for 6th, 7th, and 8th…

Using Technology to Support Statistical Reasoning: Birds, Eggs and Times to Hatch

ERIC Educational Resources Information Center

Reeve, Elizabeth; Beswick, Kim

2013-01-01

This article by Elizabeth Reeve and Kim Beswick illustrates how primary children may engage with the Statistics and Probability content contained in the Australian Curriculum. Technology has opened up many possibilities for young children to engage with statistics. In the process the children learned a great deal more than just mathematics.

Particle in a Box: An Experiential Environment for Learning Introductory Quantum Mechanics

ERIC Educational Resources Information Center

Anupam, Aditya; Gupta, Ridhima; Naeemi, Azad; JafariNaimi, Nassim

2018-01-01

Quantum mechanics (QMs) is a foundational subject in many science and engineering fields. It is difficult to teach, however, as it requires a fundamental revision of the assumptions and laws of classical physics and probability. Furthermore, introductory QM courses and texts predominantly focus on the mathematical formulations of the subject and…

Teaching and Learning the Nature of Technical Artifacts

ERIC Educational Resources Information Center

Frederik, Ineke; Sonneveld, Wim; de Vries, Marc J.

2011-01-01

Artifacts are probably our most obvious everyday encounter with technology. Therefore, a good understanding of the nature of technical artifacts is a relevant part of technological literacy. In this article we draw from the philosophy of technology to develop a conceptualization of technical artifacts that can be used for educational purposes.…

Across Space and Time: Infants Learn from Backward and Forward Visual Statistics

ERIC Educational Resources Information Center

Tummeltshammer, Kristen; Amso, Dima; French, Robert M.; Kirkham, Natasha Z.

2017-01-01

This study investigates whether infants are sensitive to backward and forward transitional probabilities within temporal and spatial visual streams. Two groups of 8-month-old infants were familiarized with an artificial grammar of shapes, comprising backward and forward base pairs (i.e. two shapes linked by strong backward or forward transitional…

Inferring a Learner's Cognitive, Motivational and Emotional State in a Digital Educational Game

ERIC Educational Resources Information Center

Bedek, Michael; Seitlinger, Paul; Kopeinik, Simone; Albert, Dietrich

2012-01-01

Digital educational games (DEGs) possess the potential of providing an appealing and intrinsically motivating learning context. Usually this potential is either taken for granted or examined through questionnaires or interviews in the course of evaluation studies. However, an "adaptive" game would increase the probability of a DEG being…

Academic English: A Conceptual Framework. Technical Report 2003-1

ERIC Educational Resources Information Center

Scarcella , Robin

2003-01-01

Learning academic English is probably one of the surest, most reliable ways of attaining socioeconomic success in the United States today. Learners cannot function in school settings effectively without it. This variety of English entails the multiple, complex features of English required for success in public schooling and career advancement. It…

Occam's Rattle: Children's Use of Simplicity and Probability to Constrain Inference

ERIC Educational Resources Information Center

Bonawitz, Elizabeth Baraff; Lombrozo, Tania

2012-01-01

A growing literature suggests that generating and evaluating explanations is a key mechanism for learning and inference, but little is known about how children generate and select competing explanations. This study investigates whether young children prefer explanations that are simple, where simplicity is quantified as the number of causes…

Semi-automatic segmentation of brain tumors using population and individual information.

PubMed

Wu, Yao; Yang, Wei; Jiang, Jun; Li, Shuanqian; Feng, Qianjin; Chen, Wufan

2013-08-01

Efficient segmentation of tumors in medical images is of great practical importance in early diagnosis and radiation plan. This paper proposes a novel semi-automatic segmentation method based on population and individual statistical information to segment brain tumors in magnetic resonance (MR) images. First, high-dimensional image features are extracted. Neighborhood components analysis is proposed to learn two optimal distance metrics, which contain population and patient-specific information, respectively. The probability of each pixel belonging to the foreground (tumor) and the background is estimated by the k-nearest neighborhood classifier under the learned optimal distance metrics. A cost function for segmentation is constructed through these probabilities and is optimized using graph cuts. Finally, some morphological operations are performed to improve the achieved segmentation results. Our dataset consists of 137 brain MR images, including 68 for training and 69 for testing. The proposed method overcomes segmentation difficulties caused by the uneven gray level distribution of the tumors and even can get satisfactory results if the tumors have fuzzy edges. Experimental results demonstrate that the proposed method is robust to brain tumor segmentation.

Neural correlates of instrumental contingency learning: Differential effects of action-reward conjunction and disjunction

PubMed Central

Liljeholm, Mimi; Tricomi, Elizabeth; O’Doherty, John P.; Balleine, Bernard W.

2011-01-01

Contingency theories of goal-directed action propose that experienced disjunctions between an action and its specific consequences, as well as conjunctions between these events, contribute to encoding the action-outcome association. Although considerable behavioral research in rats and humans has provided evidence for this proposal, relatively little is known about the neural processes that contribute to the two components of the contingency calculation. Specifically, while recent findings suggest that the influence of action-outcome conjunctions on goal-directed learning is mediated by a circuit involving ventromedial prefrontal, medial orbitofrontal cortex and dorsomedial striatum, the neural processes that mediate the influence of experienced disjunctions between these events are unknown. Here we show differential responses to probabilities of conjunctive and disjunctive reward deliveries in the ventromedial prefrontal cortex, the dorsomedial striatum, and the inferior frontal gyrus. Importantly, activity in the inferior parietal lobule and the left middle frontal gyrus varied with a formal integration of the two reward probabilities, ΔP, as did response rates and explicit judgments of the causal efficacy of the action. PMID:21325514

Neural correlates of the divergence of instrumental probability distributions.

PubMed

Liljeholm, Mimi; Wang, Shuo; Zhang, June; O'Doherty, John P

2013-07-24

Flexible action selection requires knowledge about how alternative actions impact the environment: a "cognitive map" of instrumental contingencies. Reinforcement learning theories formalize this map as a set of stochastic relationships between actions and states, such that for any given action considered in a current state, a probability distribution is specified over possible outcome states. Here, we show that activity in the human inferior parietal lobule correlates with the divergence of such outcome distributions-a measure that reflects whether discrimination between alternative actions increases the controllability of the future-and, further, that this effect is dissociable from those of other information theoretic and motivational variables, such as outcome entropy, action values, and outcome utilities. Our results suggest that, although ultimately combined with reward estimates to generate action values, outcome probability distributions associated with alternative actions may be contrasted independently of valence computations, to narrow the scope of the action selection problem.

Detecting Anomalies in Process Control Networks

NASA Astrophysics Data System (ADS)

Rrushi, Julian; Kang, Kyoung-Don

This paper presents the estimation-inspection algorithm, a statistical algorithm for anomaly detection in process control networks. The algorithm determines if the payload of a network packet that is about to be processed by a control system is normal or abnormal based on the effect that the packet will have on a variable stored in control system memory. The estimation part of the algorithm uses logistic regression integrated with maximum likelihood estimation in an inductive machine learning process to estimate a series of statistical parameters; these parameters are used in conjunction with logistic regression formulas to form a probability mass function for each variable stored in control system memory. The inspection part of the algorithm uses the probability mass functions to estimate the normalcy probability of a specific value that a network packet writes to a variable. Experimental results demonstrate that the algorithm is very effective at detecting anomalies in process control networks.

Exact and Approximate Probabilistic Symbolic Execution

NASA Technical Reports Server (NTRS)

Luckow, Kasper; Pasareanu, Corina S.; Dwyer, Matthew B.; Filieri, Antonio; Visser, Willem

2014-01-01

Probabilistic software analysis seeks to quantify the likelihood of reaching a target event under uncertain environments. Recent approaches compute probabilities of execution paths using symbolic execution, but do not support nondeterminism. Nondeterminism arises naturally when no suitable probabilistic model can capture a program behavior, e.g., for multithreading or distributed systems. In this work, we propose a technique, based on symbolic execution, to synthesize schedulers that resolve nondeterminism to maximize the probability of reaching a target event. To scale to large systems, we also introduce approximate algorithms to search for good schedulers, speeding up established random sampling and reinforcement learning results through the quantification of path probabilities based on symbolic execution. We implemented the techniques in Symbolic PathFinder and evaluated them on nondeterministic Java programs. We show that our algorithms significantly improve upon a state-of- the-art statistical model checking algorithm, originally developed for Markov Decision Processes.

Neural Correlates of the Divergence of Instrumental Probability Distributions

PubMed Central

Wang, Shuo; Zhang, June; O'Doherty, John P.

2013-01-01

Flexible action selection requires knowledge about how alternative actions impact the environment: a “cognitive map” of instrumental contingencies. Reinforcement learning theories formalize this map as a set of stochastic relationships between actions and states, such that for any given action considered in a current state, a probability distribution is specified over possible outcome states. Here, we show that activity in the human inferior parietal lobule correlates with the divergence of such outcome distributions–a measure that reflects whether discrimination between alternative actions increases the controllability of the future–and, further, that this effect is dissociable from those of other information theoretic and motivational variables, such as outcome entropy, action values, and outcome utilities. Our results suggest that, although ultimately combined with reward estimates to generate action values, outcome probability distributions associated with alternative actions may be contrasted independently of valence computations, to narrow the scope of the action selection problem. PMID:23884955

Automatic Sleep Stage Determination by Multi-Valued Decision Making Based on Conditional Probability with Optimal Parameters

NASA Astrophysics Data System (ADS)

Wang, Bei; Sugi, Takenao; Wang, Xingyu; Nakamura, Masatoshi

Data for human sleep study may be affected by internal and external influences. The recorded sleep data contains complex and stochastic factors, which increase the difficulties for the computerized sleep stage determination techniques to be applied for clinical practice. The aim of this study is to develop an automatic sleep stage determination system which is optimized for variable sleep data. The main methodology includes two modules: expert knowledge database construction and automatic sleep stage determination. Visual inspection by a qualified clinician is utilized to obtain the probability density function of parameters during the learning process of expert knowledge database construction. Parameter selection is introduced in order to make the algorithm flexible. Automatic sleep stage determination is manipulated based on conditional probability. The result showed close agreement comparing with the visual inspection by clinician. The developed system can meet the customized requirements in hospitals and institutions.

Social learning spreads knowledge about dangerous humans among American crows.

PubMed

Cornell, Heather N; Marzluff, John M; Pecoraro, Shannon

2012-02-07

Individuals face evolutionary trade-offs between the acquisition of costly but accurate information gained firsthand and the use of inexpensive but possibly less reliable social information. American crows (Corvus brachyrhynchos) use both sources of information to learn the facial features of a dangerous person. We exposed wild crows to a novel 'dangerous face' by wearing a unique mask as we trapped, banded and released 7-15 birds at five study sites near Seattle, WA, USA. An immediate scolding response to the dangerous mask after trapping by previously captured crows demonstrates individual learning, while an immediate response by crows that were not captured probably represents conditioning to the trapping scene by the mob of birds that assembled during the capture. Later recognition of dangerous masks by lone crows that were never captured is consistent with horizontal social learning. Independent scolding by young crows, whose parents had conditioned them to scold the dangerous mask, demonstrates vertical social learning. Crows that directly experienced trapping later discriminated among dangerous and neutral masks more precisely than did crows that learned through social means. Learning enabled scolding to double in frequency and spread at least 1.2 km from the place of origin over a 5 year period at one site.

Social learning of predators in the dark: understanding the role of visual, chemical and mechanical information.

PubMed

Manassa, R P; McCormick, M I; Chivers, D P; Ferrari, M C O

2013-08-22

The ability of prey to observe and learn to recognize potential predators from the behaviour of nearby individuals can dramatically increase survival and, not surprisingly, is widespread across animal taxa. A range of sensory modalities are available for this learning, with visual and chemical cues being well-established modes of transmission in aquatic systems. The use of other sensory cues in mediating social learning in fishes, including mechano-sensory cues, remains unexplored. Here, we examine the role of different sensory cues in social learning of predator recognition, using juvenile damselfish (Amphiprion percula). Specifically, we show that a predator-naive observer can socially learn to recognize a novel predator when paired with a predator-experienced conspecific in total darkness. Furthermore, this study demonstrates that when threatened, individuals release chemical cues (known as disturbance cues) into the water. These cues induce an anti-predator response in nearby individuals; however, they do not facilitate learnt recognition of the predator. As such, another sensory modality, probably mechano-sensory in origin, is responsible for information transfer in the dark. This study highlights the diversity of sensory cues used by coral reef fishes in a social learning context.

Behavioral and neural properties of social reinforcement learning

PubMed Central

Jones, Rebecca M.; Somerville, Leah H.; Li, Jian; Ruberry, Erika J.; Libby, Victoria; Glover, Gary; Voss, Henning U.; Ballon, Douglas J.; Casey, BJ

2011-01-01

Social learning is critical for engaging in complex interactions with other individuals. Learning from positive social exchanges, such as acceptance from peers, may be similar to basic reinforcement learning. We formally test this hypothesis by developing a novel paradigm that is based upon work in non-human primates and human imaging studies of reinforcement learning. The probability of receiving positive social reinforcement from three distinct peers was parametrically manipulated while brain activity was recorded in healthy adults using event-related functional magnetic resonance imaging (fMRI). Over the course of the experiment, participants responded more quickly to faces of peers who provided more frequent positive social reinforcement, and rated them as more likeable. Modeling trial-by-trial learning showed ventral striatum and orbital frontal cortex activity correlated positively with forming expectations about receiving social reinforcement. Rostral anterior cingulate cortex activity tracked positively with modulations of expected value of the cues (peers). Together, the findings across three levels of analysis - social preferences, response latencies and modeling neural responses – are consistent with reinforcement learning theory and non-human primate electrophysiological studies of reward. This work highlights the fundamental influence of acceptance by one’s peers in altering subsequent behavior. PMID:21917787

Mathematical Theories of Interaction with Oracles

DTIC Science & Technology

2013-10-01

have made a lasting impact on my mathematical perspective. I am grateful for the wonderful and stimulating discussion I had with Alan Frieze on...Otherwise, by Theorem 7.1, with probability at least 1−ε/2, we have ‖πθ⋆ −πθ̂(t−1)θ⋆‖ ≤ R(t−1, ε/2). On this event, ifR (t−1, ε/2) ≤ ε/8, then by a triangle... impact on how much benefit we gain from transfer learning when we are faced with only a finite sequence of learning problems. As such, it is certainly

Visual complexity: a review.

PubMed

Donderi, Don C

2006-01-01

The idea of visual complexity, the history of its measurement, and its implications for behavior are reviewed, starting with structuralism and Gestalt psychology at the beginning of the 20th century and ending with visual complexity theory, perceptual learning theory, and neural circuit theory at the beginning of the 21st. Evidence is drawn from research on single forms, form and texture arrays and visual displays. Form complexity and form probability are shown to be linked through their reciprocal relationship in complexity theory, which is in turn shown to be consistent with recent developments in perceptual learning and neural circuit theory. Directions for further research are suggested.

A model for the transfer of perceptual-motor skill learning in human behaviors.

PubMed

Rosalie, Simon M; Müller, Sean

2012-09-01

This paper presents a preliminary model that outlines the mechanisms underlying the transfer of perceptual-motor skill learning in sport and everyday tasks. Perceptual-motor behavior is motivated by performance demands and evolves over time to increase the probability of success through adaptation. Performance demands at the time of an event create a unique transfer domain that specifies a range of potentially successful actions. Transfer comprises anticipatory subconscious and conscious mechanisms. The model also outlines how transfer occurs across a continuum, which depends on the individual's expertise and contextual variables occurring at the incidence of transfer

Reward and punishment learning in daily life: A replication study.

PubMed

Heininga, Vera E; van Roekel, Eeske; Wichers, Marieke; Oldehinkel, Albertine J

2017-01-01

Day-to-day experiences are accompanied by feelings of Positive Affect (PA) and Negative Affect (NA). Implicitly, without conscious processing, individuals learn about the reward and punishment value of each context and activity. These associative learning processes, in turn, affect the probability that individuals will re-engage in such activities or seek out that context. So far, implicit learning processes are almost exclusively investigated in controlled laboratory settings and not in daily life. Here we aimed to replicate the first study that investigated implicit learning processes in real life, by means of the Experience Sampling Method (ESM). That is, using an experience-sampling study with 90 time points (three measurements over 30 days), we prospectively measured time spent in social company and amount of physical activity as well as PA and NA in the daily lives of 18-24-year-old young adults (n = 69 with anhedonia, n = 69 without anhedonia). Multilevel analyses showed a punishment learning effect with regard to time spent in company of friends, but not a reward learning effect. Neither reward nor punishment learning effects were found with regard to physical activity. Our study shows promising results for future research on implicit learning processes in daily life, with the proviso of careful consideration of the timescale used. Short-term retrospective ESM design with beeps approximately six hours apart may suffer from mismatch noise that hampers accurate detection of associative learning effects over time.

Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting.

PubMed

Vock, David M; Wolfson, Julian; Bandyopadhyay, Sunayan; Adomavicius, Gediminas; Johnson, Paul E; Vazquez-Benitez, Gabriela; O'Connor, Patrick J

2016-06-01

Models for predicting the probability of experiencing various health outcomes or adverse events over a certain time frame (e.g., having a heart attack in the next 5years) based on individual patient characteristics are important tools for managing patient care. Electronic health data (EHD) are appealing sources of training data because they provide access to large amounts of rich individual-level data from present-day patient populations. However, because EHD are derived by extracting information from administrative and clinical databases, some fraction of subjects will not be under observation for the entire time frame over which one wants to make predictions; this loss to follow-up is often due to disenrollment from the health system. For subjects without complete follow-up, whether or not they experienced the adverse event is unknown, and in statistical terms the event time is said to be right-censored. Most machine learning approaches to the problem have been relatively ad hoc; for example, common approaches for handling observations in which the event status is unknown include (1) discarding those observations, (2) treating them as non-events, (3) splitting those observations into two observations: one where the event occurs and one where the event does not. In this paper, we present a general-purpose approach to account for right-censored outcomes using inverse probability of censoring weighting (IPCW). We illustrate how IPCW can easily be incorporated into a number of existing machine learning algorithms used to mine big health care data including Bayesian networks, k-nearest neighbors, decision trees, and generalized additive models. We then show that our approach leads to better calibrated predictions than the three ad hoc approaches when applied to predicting the 5-year risk of experiencing a cardiovascular adverse event, using EHD from a large U.S. Midwestern healthcare system. Copyright © 2016 Elsevier Inc. All rights reserved.