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Organizations are increasingly expecting their employees to engage in self-regulated learning to meet the demands of a rapidly changing work environment (Sitzmann & Ely, 2011). Self-regulated learning is an informal learning process by which learners alter the learning experience by adapting their actions (i.e., affect, cognition, behavior) to reach a desired level of achievement (Sitzmann & Ely, 2011). One can imagine that motivating employees to self-regulate their own learning paths in the first place is difficult given various demands from work, family, and other life domains. Intervening on core self-regulatory processes in the learning environment by altering training design elements could prove useful in motivating employees to develop their knowledge and skills (e.g., Bell & Kozlowski, 2008). However, over the last decade, training design research that has languished. This might be due to a lack of deep integration of motivational theory that could help inform the design of instruction content to foster successful self-regulation in the learning environment. One design framework typically grounded in motivational theory that is used to motivate learners is gamification. Gamification is a design strategy characterized by the process of adding game mechanics, features, and/or to non-game systems to alter the user (i.e., learner) experience (Arthur et al., 2017; Deterding et al., 2011; Landers et al., 2018). A common goal of gamification is to elicit a gameful experience, or the psychological state targeted in gameful design that is analogous to the experience of entertainment games (Landers et al., 2018). Gamification researchers characterize this psychological state as intrinsically motivating, where gameplay induces “senses of enjoyment, flow, autonomy, mastery” (Koivisto &Hamari, 2019, p. 193). Another characterization of a gameful experience is “the … experience of need satisfaction”, which is what defines self-regulatory behavior as being “intrinsically motivated” (Deterding, 2015, p. ). Thus, many gamification researchers describe the goal of gamification is “is aimed at invoking users’ intrinsic motivations commonly through design reminiscent from games” (Hamari & Koivisto, 2015, p. 420). That is, a popular goal of gamification is to harness motivational aspects of games and to transfer similar levels of intrinsic motivation to other contexts that are not typically thought as gameful. In doing so, applying game elements to non-game contexts sparks

Organizations are increasingly expecting their employees to engage in self-regulated learning to meet the demands of a rapidly changing work environment (Sitzmann & Ely, 2011). Self-regulated learning is an informal learning process by which learners alter the learning experience by adapting their actions (i.e., affect, cognition, behavior) to reach a desired level of achievement (Sitzmann & Ely, 2011). One can imagine that motivating employees to self-regulate their own learning paths in the first place is difficult given various demands from work, family, and other life domains. Intervening on core self-regulatory processes in the learning environment by altering training design elements could prove useful in motivating employees to develop their knowledge and skills (e.g., Bell & Kozlowski, 2008). However, over the last decade, training design research that has languished. This might be due to a lack of deep integration of motivational theory that could help inform the design of instruction content to foster successful self-regulation in the learning environment. One design framework typically grounded in motivational theory that is used to motivate learners is gamification. Gamification is a design strategy characterized by the process of adding game mechanics, features, and/or to non-game systems to alter the user (i.e., learner) experience (Arthur et al., 2017; Deterding et al., 2011; Landers et al., 2018). A common goal of gamification is to elicit a gameful experience, or the psychological state targeted in gameful design that is analogous to the experience of entertainment games (Landers et al., 2018). Gamification researchers characterize this psychological state as intrinsically motivating, where gameplay induces “senses of enjoyment, flow, autonomy, mastery” (Koivisto &Hamari, 2019, p. 193). Another characterization of a gameful experience is “the … experience of need satisfaction”, which is what defines self-regulatory behavior as being “intrinsically motivated” (Deterding, 2015, p. ). Thus, many gamification researchers describe the goal of gamification is “is aimed at invoking users’ intrinsic motivations commonly through design reminiscent from games” (Hamari & Koivisto, 2015, p. 420). That is, a popular goal of gamification is to harness motivational aspects of games and to transfer similar levels of intrinsic motivation to other contexts that are not typically thought as gameful. In doing so, applying game elements to non-game contexts sparks

This study investigates the effectiveness of the live interactive broadcast method of delivering engineering content into a standard university classroom. The Dynamics Concepts Inventory, a standardized quantitative assessment, has been administered as a pretest and final assessment for the broadcast section of dynamics for four semesters. Student attitudes toward the non-standard learning environment were assessed through an instructor-developed survey, third party interviews, and anecdotal evidence. Results of the DCI indicate that outcomes from the broadcast section are similar to national average for lecture style classes. Attitude surveys revealed a persistent sense of student isolation and frustration with lack of personal contact with the instructor, but little difficulty with the broadcast and technology assisted class room environment.

Whereas Natural User Interface technological devices, such as tablets, are bringing physical interaction back into mathematics learning activities, existing educational theory is not geared to inform or interpret such learning. In particular, educational researchers investigating instructional interactions still need intellectual and methodological frameworks for conceptualizing, designing, facilitating, and analyzing how students' immersive hands-on dynamical experiences become formulated within semiotic registers typical of mathematical discourse. We present paradigmatic empirical examples of tutor-student behaviors in an embodied-interaction learning environment, the Mathematical Imagery Trainer for Proportion. Drawing on ecological dynamics--a blend of dynamical-systems theory and ecological psychology--we describe the emergence of mathematical concepts from the guided discovery of sensorimotor schemes. [For the complete proceedings, see ED583989.]

Whereas Natural User Interface technological devices, such as tablets, are bringing physical interaction back into mathematics learning activities, existing educational theory is not geared to inform or interpret such learning. In particular, educational researchers investigating instructional interactions still need intellectual and methodological frameworks for conceptualizing, designing, facilitating, and analyzing how students' immersive hands-on dynamical experiences become formulated within semiotic registers typical of mathematical discourse. We present paradigmatic empirical examples of tutor-student behaviors in an embodied-interaction learning environment, the Mathematical Imagery Trainer for Proportion. Drawing on ecological dynamics--a blend of dynamical-systems theory and ecological psychology--we describe the emergence of mathematical concepts from the guided discovery of sensorimotor schemes. [For the complete proceedings, see ED583989.]

This article is from Journal of Neurophysiology , volume 111 . Abstract Successful motor performance requires the ability to adapt motor commands to task dynamics. A central question in movement neuroscience is how these dynamics are represented. Although it is widely assumed that dynamics (e.g., force fields) are represented in intrinsic, joint-based coordinates (Shadmehr R, Mussa-Ivaldi FA. J Neurosci 14: 3208–3224, 1994), recent evidence has questioned this proposal. Here we reexamine the representation of dynamics in two experiments. By testing generalization following changes in shoulder, elbow, or wrist configurations, the first experiment tested for extrinsic, intrinsic, or object-centered representations. No single coordinate frame accounted for the pattern of generalization. Rather, generalization patterns were better accounted for by a mixture of representations or by models that assumed local learning and graded, decaying generalization. A second experiment, in which we replicated the design of an influential study that had suggested encoding in intrinsic coordinates (Shadmehr and Mussa-Ivaldi 1994), yielded similar results. That is, we could not find evidence that dynamics are represented in a single coordinate system. Taken together, our experiments suggest that internal models do not employ a single coordinate system when generalizing and may well be represented as a mixture of coordinate systems, as a single system with local learning, or both.

This article is from Journal of Neurophysiology , volume 111 . Abstract Successful motor performance requires the ability to adapt motor commands to task dynamics. A central question in movement neuroscience is how these dynamics are represented. Although it is widely assumed that dynamics (e.g., force fields) are represented in intrinsic, joint-based coordinates (Shadmehr R, Mussa-Ivaldi FA. J Neurosci 14: 3208–3224, 1994), recent evidence has questioned this proposal. Here we reexamine the representation of dynamics in two experiments. By testing generalization following changes in shoulder, elbow, or wrist configurations, the first experiment tested for extrinsic, intrinsic, or object-centered representations. No single coordinate frame accounted for the pattern of generalization. Rather, generalization patterns were better accounted for by a mixture of representations or by models that assumed local learning and graded, decaying generalization. A second experiment, in which we replicated the design of an influential study that had suggested encoding in intrinsic coordinates (Shadmehr and Mussa-Ivaldi 1994), yielded similar results. That is, we could not find evidence that dynamics are represented in a single coordinate system. Taken together, our experiments suggest that internal models do not employ a single coordinate system when generalizing and may well be represented as a mixture of coordinate systems, as a single system with local learning, or both.

The study will examine the learning rates and discount factors of the semantic and affective representations of valence during reversal learning with two states.

256 p. 23 cm

The paper's primary focus revolves around the pivotal role of effective classroom management in creating an optimal learning environment for students within the English department of an education faculty. Employing field research and a questionnaire, the researchers sought to gauge the significance of classroom management, drawing insights from the experiences and background knowledge of 105 students in their third and fourth years. The study identified key components for successful classroom management, encompassing curriculum and instructional aspects, teacher behavior, and the integration of management technology. The findings underscore the students' recognition of the critical importance of managing the classroom to ensure the smooth execution of learning activities and foster positive learning outcomes. The research advocates for teachers to prioritize various elements such as rule enforcement, strategic lesson planning, and active involvement in assessment processes. The paper concludes by asserting that language teachers must be attentive to the nuances of effective classroom management in their teaching practices, with the hope that the research's outcomes will serve as a valuable resource for language educators seeking to enhance their teaching methodologies.

Thesis Advisor(s): Haderlie, E. C

Although mechanisms underlying decisions made for ourselves are well-explored, predictions of other’s choices remain relatively understudied. Recent evidence indicates that decisions for the self and another person may be performed by the same underlying cognitive mechanisms (Bavard et al., 2023), although this seems to depend on factors such as the perceived self-other similarity (Stuchlý et al., 2024). On the brain dynamics level, studies on mentalising and perspective-taking give an inconsistent picture, identifying both similarities (Jenkins et al., 2008) and differences (Tamir & Mitchell, 2010) when representing oneself vs. another person. The few studies which investigated the brain signal of predicting decisions more specifically (e.g., Harris et al., 2018) are, likewise, not conclusive or comprehensive, as they have only used vaguely described hypothetical others, making it impossible to quantify aspects such as self-other similarity or to determine the accuracy of the predicted decisions. The aim of the current study is to fill this gap by identifying and comparing the neural underpinnings of making decisions for oneself and when predicting decisions of another agent in a more information-rich environment. Participants will first make risky choices for themselves; then observe the choices of another (artificial) agent and learn their risk preference; and finally predict how this agent would decide on the same set of options that participants completed for themselves. Importantly, participants will observe and predict for one similar and one dissimilar agent, where we will precisely adjust the self-other risk preference similarity based on participants’ own risk preference. Most important is the question of whether we find evidence of the same brain signal encoding decisions for oneself and the decisions predicted for another agent (similar, dissimilar or both).

The study will examine the learning rates and discount factors of the semantic and affective representations of valence during reversal learning with two states.

This paper explores one practitioner's learning development work with PhD students in a changing university context in which managerialism and financial stringency have combined. It questions how learning development practitioners can maintain their professional goals while negotiating issues arising from managerialism, financial stringency, task-oriented budgeting and inter-professional co-operation. While learning development workers have traditionally espoused a broad, colleagual view of student learning and practice which looks beyond remediation, a cash-starved sector may look for simpler, task-oriented definitions in which work is discretely allocated to different professional groups rather than dealing with dynamic, complex and overlapping processes. Hence, two teaching strategies are explored: the use of pre-course learning development tutorials as a means to tackle timeliness and efficacy of courses, and learning development contributions to supervision development courses.

The beliefs of job seekers about their own future prospects are important, but despite being a long-standing topic of interest, relatively little is known about how unemployed job seekers themselves think their job-finding probability will evolve if they remain unemployed for longer. In this study, we conduct a survey to explore this question.

The beliefs of job seekers about their own future prospects are important, but despite being a long-standing topic of interest, relatively little is known about how unemployed job seekers themselves think their job-finding probability will evolve if they remain unemployed for longer. In this study, we conduct a survey to explore this question.

The beliefs of job seekers about their own future prospects are important, but despite being a long-standing topic of interest, relatively little is known about how unemployed job seekers themselves think their job-finding probability will evolve if they remain unemployed for longer. In this study, we conduct a survey to explore this question.

The beliefs of job seekers about their own future prospects are important, but despite being a long-standing topic of interest, relatively little is known about how unemployed job seekers themselves think their job-finding probability will evolve if they remain unemployed for longer. In this study, we conduct a survey to explore this question.

The beliefs of job seekers about their own future prospects are important, but despite being a long-standing topic of interest, relatively little is known about how unemployed job seekers themselves think their job-finding probability will evolve if they remain unemployed for longer. In this study, we conduct a survey to explore this question.

This study aimed to describe the dynamics of content aspects, user aspects and social aspects of mobile learning utilization (m-learning) in vocational education from the FRAME Model perspective review. This study was quantitative descriptive research. The population in this study was teachers and students of state vocational school and private school in Makassar, Indonesia. Samples of the study were 103 teachers and 320 students, determined by a proportional random sampling technique. Data collection techniques were questionnaires. Data analysis uses descriptive analysis techniques. The results showed that the available content on the internet and accessed by teachers were relevant to the major, whereas students stated them relevantly less to their majors, so the value and substance of the utilization of m-learning content were still less relevant. The user aspect showed that teachers and students utilized mobile devices for vocational learning purposes, so the value and substance of m-learning could be still maintained. The social aspect showed that teachers and students expressed sociocultural support influenced the utilization of m-learning, so that the value and substance of m-learning in vocational education could be continually maintained. So the value and substance of m-learning in these three aspects could be maintained and improved, this study showed alternative model of utilization and development of m-learning that were appropriate for vocational education.

We present an unsupervised representation learning approach that compactly encodes the motion dependencies in videos. Given a pair of images from a video clip, our framework learns to predict the long-term 3D motions. To reduce the complexity of the learning framework, we propose to describe the motion as a sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent Neural Network based Encoder-Decoder framework to predict these sequences of flows. We argue that in order for the decoder to reconstruct these sequences, the encoder must learn a robust video representation that captures long-term motion dependencies and spatial-temporal relations. We demonstrate the effectiveness of our learned temporal representations on activity classification across multiple modalities and datasets such as NTU RGB+D and MSR Daily Activity 3D. Our framework is generic to any input modality, i.e., RGB, Depth, and RGB-D videos.

A formalism for describing the dynamics of Genetic Algorithms (GAs) using methods from statistical mechanics is applied to the problem of generalization in a perceptron with binary weights. The dynamics are solved for the case where a new batch of training patterns is presented to each population member each generation, which considerably simplifies the calculation. The theory is shown to agree closely to simulations of a real GA averaged over many runs, accurately predicting the mean best solution found. For weak selection and large problem size the difference equations describing the dynamics can be expressed analytically and we find that the effects of noise due to the finite size of each training batch can be removed by increasing the population size appropriately. If this population resizing is used, one can deduce the most computationally efficient size of training batch each generation. For independent patterns this choice also gives the minimum total number of training patterns used. Although using independent patterns is a very inefficient use of training patterns in general, this work may also prove useful for determining the optimum batch size in the case where patterns are recycled.

A formalism for describing the dynamics of Genetic Algorithms (GAs) using methods from statistical mechanics is applied to the problem of generalization in a perceptron with binary weights. The dynamics are solved for the case where a new batch of training patterns is presented to each population member each generation, which considerably simplifies the calculation. The theory is shown to agree closely to simulations of a real GA averaged over many runs, accurately predicting the mean best solution found. For weak selection and large problem size the difference equations describing the dynamics can be expressed analytically and we find that the effects of noise due to the finite size of each training batch can be removed by increasing the population size appropriately. If this population resizing is used, one can deduce the most computationally efficient size of training batch each generation. For independent patterns this choice also gives the minimum total number of training patterns used. Although using independent patterns is a very inefficient use of training patterns in general, this work may also prove useful for determining the optimum batch size in the case where patterns are recycled.

Reading has been perceived as a dynamic transactional process wherein readers negotiate meaning with writers by virtue of their prior knowledge. The act of meaning construction is realized primarily through the exploration of intertextual links that connect various sources of texts the readers have composed or experienced. Readers utilize intertextuality as an inquiry tool to build their own perspectives of the ideas and thoughts communicated in the texts. Recent inquiries corroborate the possibility of investigating intertextuality by analyzing reader response to the readings without purposefully establishing an "intertextuality rich environment." A study investigated the intertextual dynamics of the reading and inquiry processes of 23 advanced adult readers at the graduate levels of education in an authentic collaborative learning context that capitalizes on an online course listserv for open discussions. The research questions this study sought to answer are: What are the intertextual intentions of advanced adult readers? What are the intertextual loci that ground their intertextual processes? and What are the intertextual strategies readers employ to construct the meanings of the texts? This paper first discusses the concepts of text and intertextuality to formulate a theoretical frame to locate the investigation. The paper then provides a detailed analysis of readers' reflections and responses to address the three research questions. It then delineates a preliminary model of intertextual dynamics and examines the advantages of the listserv format in fostering intertextual inquiry. Includes a figure and 2 notes. Contains 41 references and cites 17 materials. (NKA)

Human culture is widely believed to undergo evolution, via mechanisms rooted in the nature of human cognition. A number of theories predict the kinds of human learning strategies, as well as the population dynamics that result from their action. There is little work, however, that quantitatively examines the evidence for these strategies and resulting cultural evolution within human populations. One of the obstacles is the lack of individual-level data with which to link transmission events to larger cultural dynamics. Here, we address this problem with a rich quantitative database from the East Asian board game known as Go. We draw from a large archive of Go games spanning the last six decades of professional play, and find evidence that the evolutionary dynamics of particular cultural variants are driven by a mix of individual and social learning processes. Particular players vary dramatically in their sensitivity to population knowledge, which also varies by age and nationality. The dynamic patterns of opening Go moves are consistent with an ancient, ongoing arms race within the game itself.

Using the cavity method and diagrammatic methods, we model the dynamics of batch learning of restricted sets of examples, widely applicable to general learning cost functions, and fully taking into account the temporal correlations introduced by the recycling of the examples.

Using the cavity method and diagrammatic methods, we model the dynamics of batch learning of restricted sets of examples, widely applicable to general learning cost functions, and fully taking into account the temporal correlations introduced by the recycling of the examples.

Using the cavity method and diagrammatic methods, we model the dynamics of batch learning of restricted sets of examples, widely applicable to general learning cost functions, and fully taking into account the temporal correlations introduced by the recycling of the examples.

Using the cavity method and diagrammatic methods, we model the dynamics of batch learning of restricted sets of examples, widely applicable to general learning cost functions, and fully taking into account the temporal correlations introduced by the recycling of the examples.

Learning would be a convincing method to achieve coordination on an equilibrium. But does learning converge, and to what? We answer this question in generic 2-player, 2-strategy games, using Experience-Weighted Attraction (EWA), which encompasses many extensively studied learning algorithms. We exhaustively characterize the parameter space of EWA learning, for any payoff matrix, and we understand the generic properties that imply convergent or non-convergent behaviour in 2 x 2 games. Irrational choice and lack of incentives imply convergence to a mixed strategy in the centre of the strategy simplex, possibly far from the Nash Equilibrium (NE). In the opposite limit, in which the players quickly modify their strategies, the behaviour depends on the payoff matrix: (i) a strong discrepancy between the pure strategies describes dominance-solvable games, which show convergence to a unique fixed point close to the NE; (ii) a preference towards profiles of strategies along the main diagonal describes coordination games, with multiple stable fixed points corresponding to the NE; (iii) a cycle of best responses defines discoordination games, which commonly yield limit cycles or low-dimensional chaos. While it is well known that mixed strategy equilibria may be unstable, our approach is novel from several perspectives: we fully analyse EWA and provide explicit thresholds that define the onset of instability; we find an emerging taxonomy of the learning dynamics, without focusing on specific classes of games ex-ante; we show that chaos can occur even in the simplest games; we make a precise theoretical prediction that can be tested against data on experimental learning of discoordination games.

Two papers in this report deal with early childhood. "Developmental Factors--Early Cognitive Growth" discusses the experimental-analytical and existential-experimental approaches to religious cognitive growth. The second paper, "Developmental Factors--Early Emotional-Social Growth" asserts that emotional and social integrity has to be achieved through progressive but gradual maturation of the personality from infancy onward. (MS)

We study the time taken by a language learner to correctly identify the meaning of all words in a lexicon under conditions where many plausible meanings can be inferred whenever a word is uttered. We show that the most basic form of cross-situational learning - whereby information from multiple episodes is combined to eliminate incorrect meanings - can perform badly when words are learned independently and meanings are drawn from a nonuniform distribution. If learners further assume that no two words share a common meaning, we find a phase transition between a maximally-efficient learning regime, where the learning time is reduced to the shortest it can possibly be, and a partially-efficient regime where incorrect candidate meanings for words persist at late times. We obtain exact results for the word-learning process through an equivalence to a statistical mechanical problem of enumerating loops in the space of word-meaning mappings.

Learning and memory are acquired through long-lasting changes in synapses. In the simplest models, such synaptic potentiation typically leads to runaway excitation, but in reality there must exist processes that robustly preserve overall stability of the neural system dynamics. How is this accomplished? Various approaches to this basic question have been considered. Here we propose a particularly compelling and natural mechanism for preserving stability of learning neural systems. This mechanism is based on the global processes by which metabolic resources are distributed to the neurons by glial cells. Specifically, we introduce and study a model comprised of two interacting networks: a model neural network interconnected by synapses which undergo spike-timing dependent plasticity (STDP); and a model glial network interconnected by gap junctions which diffusively transport metabolic resources among the glia and, ultimately, to neural synapses where they are consumed. Our main result is that the biophysical constraints imposed by diffusive transport of metabolic resources through the glial network can prevent runaway growth of synaptic strength, both during ongoing activity and during learning. Our findings suggest a previously unappreciated role for glial transport of metabolites in the feedback control stabilization of neural network dynamics during learning.

We describe the application of tools from statistical mechanics to analyse the dynamics of various classes of supervised learning rules in perceptrons. The character of this paper is mostly that of a cross between a biased non-encyclopedic review and lecture notes: we try to present a coherent and self-contained picture of the basics of this field, to explain the ideas and tricks, to show how the predictions of the theory compare with (simulation) experiments, and to bring together scattered results. Technical details are given explicitly in an appendix. In order to avoid distraction we concentrate the references in a final section. In addition this paper contains some new results: (i) explicit solutions of the macroscopic equations that describe the error evolution for on-line and batch learning rules, (ii) an analysis of the dynamics of arbitrary macroscopic observables (for complete and incomplete trainingsets), leading to a general Fokker-Planck equation, and (iii) the macroscopic laws describing batch learning with complete training sets. We close the paper with a preliminary expose of ongoing research on the dynamics of learning for the case where the training set is incomplete (i.e. where the number of examples scales linearly with the network size).

We describe the application of tools from statistical mechanics to analyse the dynamics of various classes of supervised learning rules in perceptrons. The character of this paper is mostly that of a cross between a biased non-encyclopedic review and lecture notes: we try to present a coherent and self-contained picture of the basics of this field, to explain the ideas and tricks, to show how the predictions of the theory compare with (simulation) experiments, and to bring together scattered results. Technical details are given explicitly in an appendix. In order to avoid distraction we concentrate the references in a final section. In addition this paper contains some new results: (i) explicit solutions of the macroscopic equations that describe the error evolution for on-line and batch learning rules, (ii) an analysis of the dynamics of arbitrary macroscopic observables (for complete and incomplete trainingsets), leading to a general Fokker-Planck equation, and (iii) the macroscopic laws describing batch learning with complete training sets. We close the paper with a preliminary expose of ongoing research on the dynamics of learning for the case where the training set is incomplete (i.e. where the number of examples scales linearly with the network size).

The purpose of the study was to model the processes involved in the development of creativity and discuss how motivation and teaching strategies figure into a creativity-supportive learning environment. The subjects included 28 college students enrolled in an Introduction to Instructional Media course. A questionnaire and interviews were used for the collection of data. Over the course of the semester, the students were tasked with making a "creative multimedia video website". The results of the study reveal that the motivation of students in areas such as task value and self-efficacy were enhanced by this exercise. Moreover, this study developed effective strategies for the promotion of creativity and the enhancement of high-level meta-cognitive processes. (Contains 4 tables and 1 figure.)

This article is from Frontiers in Psychology , volume 5 . Abstract The evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB) category learning and procedural memory dominates information-integration (II) category learning. For example, several studies have reported that feedback timing is critical for II category learning, but not for RB category learning—results that have broad support within the memory systems literature. Specifically, II category learning has been shown to be best with feedback delays of 500 ms compared to delays of 0 and 1000 ms, and highly impaired with delays of 2.5 s or longer. In contrast, RB learning is unaffected by any feedback delay up to 10 s. We propose a neurobiologically detailed theory of procedural learning that is sensitive to different feedback delays. The theory assumes that procedural learning is mediated by plasticity at cortical-striatal synapses that are modified by dopamine-mediated reinforcement learning. The model captures the time-course of the biochemical events in the striatum that cause synaptic plasticity, and thereby accounts for the empirical effects of various feedback delays on II category learning.

We study stochastic effects on the lagging anchor dynamics, a reinforcement learning algorithm used to learn successful strategies in iterated games, which is known to converge to Nash points in the absence of noise. The dynamics is stochastic when players only have limited information about their opponents' strategic propensities. The effects of this noise are studied analytically in the case where it is small but finite, and we show that the statistics and correlation properties of fluctuations can be computed to a high accuracy. We find that the system can exhibit quasicycles, driven by intrinsic noise. If players are asymmetric and use different parameters for their learning, a net payoff advantage can be achieved due to these stochastic oscillations around the deterministic equilibrium.

Using the cavity method and diagrammatic methods, we model the dynamics of batch learning of restricted sets of examples. Simulations of the Green's function and the cavity activation distributions support the theory well. The learning dynamics approaches a steady state in agreement with the static version of the cavity method. The picture of the rough energy landscape is reviewed.

The complexity of chess matches has attracted broad interest since its invention. This complexity and the availability of large number of recorded matches make chess an ideal model systems for the study of population-level learning of a complex system. We systematically investigate the move-by-move dynamics of the white player's advantage from over seventy thousand high level chess matches spanning over 150 years. We find that the average advantage of the white player is positive and that it has been increasing over time. Currently, the average advantage of the white player is ~0.17 pawns but it is exponentially approaching a value of 0.23 pawns with a characteristic time scale of 67 years. We also study the diffusion of the move dependence of the white player's advantage and find that it is non-Gaussian, has long-ranged anti-correlations and that after an initial period with no diffusion it becomes super-diffusive. We find that the duration of the non-diffusive period, corresponding to the opening stage of a match, is increasing in length and exponentially approaching a value of 15.6 moves with a characteristic time scale of 130 years. We interpret these two trends as a resulting from learning of the features of the game. Additionally, we find that the exponent {\alpha} characterizing the super-diffusive regime is increasing toward a value of 1.9, close to the ballistic regime. We suggest that this trend is due to the increased broadening of the range of abilities of chess players participating in major tournaments.

What role does working memory (WM) play in instrumental learning in an ever-changing environment? An unpublished study (Humann et al., PsyArxiv, 2020) left open the question whether working memory modulated learning performance in a reversal learning task (causation), or whether working memory and learning performance relied on a common underlying mechanism (correlation). We aim to address this question in a series of follow-up experiments. In a first online experiment (Janssen et al., https://osf.io/ujrt8), we manipulated WM load between subjects by varying the number of stimuli (1 to 4) of four presented stimuli, for which reward probabilities changed and thus needed to be tracked. The remaining stimuli had stable reward probabilities throughout the task, which deviates from the original task design with (three) changing stimuli only. With this version we could not replicate all of the correlations for the condition (WM load = 3) that is most similar to the original experiment with 3 changing stimuli (unpublished results). The current experiment serves as a control and includes only stimuli for which reward probability changes and needs to be tracked, similar to the original study. WM load is manipulated in the same fashion as before, but with two, three or four stimuli (not one). In addition to the hypotheses of the first online study (H1-4; https://osf.io/ujrt8), it will be of particular interest to investigate how the delay between two iterations of the same stimulus affects performance and dynamic learning rates. In contrast to the first online study, where the delay distribution did not depend on WM load, here, a higher WM load inherently leads to longer delays on average. A direct comparison of the two studies allows us to evaluate the difference in delay effects, as well as the effects of mixing changing and stable stimuli on performance and learning dynamics.

Starting with a group of reinforcement-learning agents we derive coupled replicator equations that describe the dynamics of collective learning in multiagent systems. We show that, although agents model their environment in a self-interested way without sharing knowledge, a game dynamics emerges naturally through environment-mediated interactions. An application to rock-scissors-paper game interactions shows that the collective learning dynamics exhibits a diversity of competitive and cooperative behaviors. These include quasiperiodicity, stable limit cycles, intermittency, and deterministic chaos--behaviors that should be expected in heterogeneous multiagent systems described by the general replicator equations we derive.

The aim of the present paper is to study the effects of Hebbian learning in random recurrent neural networks with biological connectivity, i.e. sparse connections and separate populations of excitatory and inhibitory neurons. We furthermore consider that the neuron dynamics may occur at a (shorter) time scale than synaptic plasticity and consider the possibility of learning rules with passive forgetting. We show that the application of such Hebbian learning leads to drastic changes in the network dynamics and structure. In particular, the learning rule contracts the norm of the weight matrix and yields a rapid decay of the dynamics complexity and entropy. In other words, the network is rewired by Hebbian learning into a new synaptic structure that emerges with learning on the basis of the correlations that progressively build up between neurons. We also observe that, within this emerging structure, the strongest synapses organize as a small-world network. The second effect of the decay of the weight matrix spectral radius consists in a rapid contraction of the spectral radius of the Jacobian matrix. This drives the system through the ``edge of chaos'' where sensitivity to the input pattern is maximal. Taken together, this scenario is remarkably predicted by theoretical arguments derived from dynamical systems and graph theory.

The purpose of this article is to contribute to a gradually expanding discussion on the role of lifelong learning in the personal and professional lives of individuals, as well as on the socioeconomic trends and practices in the global context. It presents the results of a survey carried out with a structured questionnaire on postgraduate students in social and economic sciences in a Greek university, in order to explore their views, perceptions and attitudes regarding lifelong learning; it also explores the way in which these factors guide their ideological commitment of lifelong learners, as well as their corresponding dynamics and potentiality. The definition of these determinants would significantly enable the effective implementation of lifelong learning by contributing to a wider range of interventions, varying from planning activities to eventually influencing the socioeconomic environment and transforming relevant national and supranational strategies

We study opinion dynamics in a population of interacting adaptive agents voting on a set of complex multidimensional issues. We consider agents which can classify issues into for or against. The agents arrive at the opinions about each issue in question using an adaptive algorithm. Adaptation comes from learning and the information for the learning process comes from interacting with other neighboring agents and trying to change the internal state in order to concur with their opinions. The change in the internal state is driven by the information contained in the issue and in the opinion of the other agent. We present results in a simple yet rich context where each agent uses a Boolean Perceptron to state its opinion. If there is no internal clock, so the update occurs with asynchronously exchanged information among pairs of agents, then the typical case, if the number of issues is kept small, is the evolution into a society thorn by the emergence of factions with extreme opposite beliefs. This occurs even when seeking consensus with agents with opposite opinions. The curious result is that it is learning from those that hold the same opinions that drives the emergence of factions. This results follows from the fact that factions are prevented by not learning at all from those agents that hold the same opinion. If the number of issues is large, the dynamics becomes trapped and the society does not evolve into factions and a distribution of moderate opinions is observed. We also study the less realistic, but technically simpler synchronous case showing that global consensus is a fixed point. However, the approach to this consensus is glassy in the limit of large societies if agents adapt even in the case of agreement.

This article is from PLoS Computational Biology , volume 10 . Abstract Learning arises through the activity of large ensembles of cells, yet most of the data neuroscientists accumulate is at the level of individual neurons; we need models that can bridge this gap. We have taken spatial learning as our starting point, computationally modeling the activity of place cells using methods derived from algebraic topology, especially persistent homology. We previously showed that ensembles of hundreds of place cells could accurately encode topological information about different environments (“learn” the space) within certain values of place cell firing rate, place field size, and cell population; we called this parameter space the learning region. Here we advance the model both technically and conceptually. To make the model more physiological, we explored the effects of theta precession on spatial learning in our virtual ensembles. Theta precession, which is believed to influence learning and memory, did in fact enhance learning in our model, increasing both speed and the size of the learning region. Interestingly, theta precession also increased the number of spurious loops during simplicial complex formation. We next explored how downstream readout neurons might define co-firing by grouping together cells within different windows of time and thereby capturing different degrees of temporal overlap between spike trains. Our model's optimum coactivity window correlates well with experimental data, ranging from ∼150–200 msec. We further studied the relationship between learning time, window width, and theta precession. Our results validate our topological model for spatial learning and open new avenues for connecting data at the level of individual neurons to behavioral outcomes at the neuronal ensemble level. Finally, we analyzed the dynamics of simplicial complex formation and loop transience to propose that the simplicial complex provides a useful working description of the spatial learning process.

For early language development, it is critical for young infants to be able to appropriately associate an auditory word with a certain object. Research has suggested that social contexts facilitate infants’ word learning despite the complexity of social information (e.g. Kuhl et al., 2003). While a body of studies have demonstrated infants’ cognition and learning are facilitated by social environmental factors such as joint attention (e.g. Striano, Reid & Hoehl, 2006; Parise et al., 2008), to our knowledge, no studies thus far have directly explored the temporal dynamics of infant’s word learning emerging in a social interactional setting. Hence, the effect of social interaction on the process of infants’ word learning developing through social interactions is yet to be fully understood. The use of neural data can address this question by providing insights into what cognitive processes are involved when infants form the link between a word and an object during a live situation. Using an electroencephalogram (EEG), the proposed study aims to explore neural correlates of 10- to 11-month-old infants while they interact with an experimenter who demonstrates the name and the function of novel objects in a live setting. The specific goal of the study is to investigate how infant’s brain activity as an index of infant word learning develops over the course of a social interaction. The study will be amongst the first to demonstrate the temporal dynamics of infant’s word learning emerging in live interactional contexts. This study is expected to advance our understanding on how infants’ language development is mediated by their social environment. The results of this study are expected to help deepen our knowledge on what social environment we could offer infants to facilitate their language development.

One of the key challenges in applying reinforcement learning to complex robotic control tasks is the need to gather large amounts of experience in order to find an effective policy for the task at hand. Model-based reinforcement learning can achieve good sample efficiency, but requires the ability to learn a model of the dynamics that is good enough to learn an effective policy. In this work, we develop a model-based reinforcement learning algorithm that combines prior knowledge from previous tasks with online adaptation of the dynamics model. These two ingredients enable highly sample-efficient learning even in regimes where estimating the true dynamics is very difficult, since the online model adaptation allows the method to locally compensate for unmodeled variation in the dynamics. We encode the prior experience into a neural network dynamics model, adapt it online by progressively refitting a local linear model of the dynamics, and use model predictive control to plan under these dynamics. Our experimental results show that this approach can be used to solve a variety of complex robotic manipulation tasks in just a single attempt, using prior data from other manipulation behaviors.

We apply a general theory describing the dynamics of supervised learning in layered neural networks in the regime where the size p of the training set is proportional to the number of inputs N, as developed in a previous paper, to several choices of learning rules. In the case of (on-line and batch) Hebbian learning, where a direct exact solution is possible, we show that our theory provides exact results at any time in many different verifiable cases. For non-Hebbian learning rules, such as Perceptron and AdaTron, we find very good agreement between the predictions of our theory and numerical simulations. Finally, we derive three approximation schemes aimed at eliminating the need to solve a functional saddle-point equation at each time step, and assess their performance. The simplest of these schemes leads to a fully explicit and relatively simple non-linear diffusion equation for the joint field distribution, which already describes the learning dynamics surprisingly well over a wide range of parameters.

We present a new model for the dynamics of the presynaptic intracellular calcium concentration in neurons evoked by various stimulation protocols. The aim of the model is twofold: We want to discuss the calcium transients during and after specific stimulation protocols as they are used to induce long-term-depression and long-term-potentiation. In addition we would like to provide a general tool which allows the comparison of different calcium experiments. This may help to draw conclusions on a wider base in future.