Center for Computational and Animal Learning Research

Saint Albans, United Kingdom

Center for Computational and Animal Learning Research

Saint Albans, United Kingdom
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Alonso E.,City University London | Alonso E.,Center for Computational and Animal Learning Research | Fairbank M.,City University London | Mondragon E.,City University London | Mondragon E.,Center for Computational and Animal Learning Research
Adaptive Behavior | Year: 2015

Whether animals behave optimally is an open question of great importance, both theoretically and in practice. Attempts to answer this question focus on two aspects of the optimization problem, the quantity to be optimized and the optimization process itself. In this paper, we assume the abstract concept of cost as the quantity to be minimized and propose a reinforcement learning algorithm, called Value-Gradient Learning (VGL), as a computational model of behavior optimality. We prove that, unlike standard models of Reinforcement Learning, Temporal Difference in particular, VGL is guaranteed to converge to optimality under certain conditions. The core of the proof is the mathematical equivalence of VGL and Pontryagin’s Minimum Principle, a well-known optimization technique in systems and control theory. Given the similarity between VGL’s formulation and regulatory models of behavior, we argue that our algorithm may provide psychologists with a tool to formulate such models in optimization terms. © The Author(s) 2015.


Alonso E.,Northampton Square | Fairbank M.,Northampton Square | Mondragon E.,Center for Computational and Animal Learning Research
Proceedings of the 11th International Conference on Cognitive Modeling, ICCM 2012 | Year: 2012

It is well known that, in one form or another, the variational Principle of Least Action (PLA) governs Nature. Although traditionally referred to explain physical phenomena, PLA has also been used to account for biological phenomena and even natural selection. However, its value in studying psychological processes has not been fully explored. In this paper we present a computational model, value-gradient learning, based on Pontryagin's Minimum Principle (a version of PLA used in optimal theory), that applies to both classical and operant conditioning.


Bonardi C.,University of Nottingham | Mondragon E.,Center for Computational and Animal Learning Research | Brilot B.,Newcastle University | Jennings D.J.,Newcastle University
Quarterly Journal of Experimental Psychology | Year: 2015

Two experiments investigated the effect of the temporal distribution form of a stimulus on its ability to produce an overshadowing effect. The overshadowing stimuli were either of the same duration on every trial, or of a variable duration drawn from an exponential distribution with the same mean duration as that of the fixed stimulus. Both experiments provided evidence that a variable-duration stimulus was less effective than a fixed-duration cue at overshadowing conditioning to a target conditioned stimulus (CS); moreover, this effect was independent of whether the overshadowed CS was fixed or variable. The findings presented here are consistent with the idea that the strength of the association between CS and unconditioned stimulus (US) is, in part, determined by the temporal distribution form of the CS. These results are discussed in terms of time-accumulation and trial-based theories of conditioning and timing. © 2014, © 2014 The Experimental Psychology Society.


Alonso E.,University of London | Mondragon E.,Center for Computational and Animal Learning Research
Proceedings of the 11th International Conference on Cognitive Modeling, ICCM 2012 | Year: 2012

Classical conditioning is at the heart of most learning processes. It is thus essential that we develop accurate models of conditioning phenomena and data. In this paper we review the different uses of computational models in exploring conditioning, as simulators and as psychological models by proxy.


Jennings D.J.,Northumbria University | Alonso E.,City University London | Mondragon E.,Center for Computational and Animal Learning Research | Franssen M.,University of Nottingham | Bonardi C.,University of Nottingham
Journal of Experimental Psychology: Animal Behavior Processes | Year: 2013

In four experiments rats were conditioned to an auditory conditioned stimulus (conditioned stimulus; CS) that was paired with food, and learning about the CS was compared across two conditions in which the mean duration of the CS was equated. In one, the CS was of a single, fixed duration on every trial, and in the other the CS duration was drawn from an exponential distribution, and hence changed from trial to trial. Higher rates of conditioned responding to the fixed than to the variable stimulus were observed, in both between- (Experiment 1) and within-subject designs (Experiments 2 and 3). Moreover, this difference was maintained when stimuli trained with fixed or variable durations were tested under identical conditions (i.e., with equal numbers of fixed and variable duration trials)-suggesting that the difference could not be attributed to performance effects (Experiment 3). In order to estimate the speed of acquisition of conditioned responding, the scaled cumulative distribution of a Weibull function was fitted to the trial-by-trial response rates for each rat. In the within-subject experiments specific differences in the pattern of acquisition to fixed and variable CS were shown; a somewhat different pattern was found when intertrial interval (ITI) was manipulated (Experiment 4). The implications of these findings for theories of conditioning and timing are discussed. © 2013 American Psychological Association.


Alonso E.,City University London | Mondragon E.,Center for Computational and Animal Learning Research | Fernandez A.,Rey Juan Carlos University
Computer Methods and Programs in Biomedicine | Year: 2012

In this paper we present the "R&W Simulator"(version 3.0), a Java simulator of Rescorla and Wagner's prediction error model of learning. It is able to run whole experimental designs, and compute and display the associative values of elemental and compound stimuli simultaneously, as well as use extra configural cues in generating compound values; it also permits change of the US parameters across phases. The simulator produces both numerical and graphical outputs, and includes a functionality to export the results to a data processor spreadsheet. It is user-friendly, and built with a graphical interface designed to allow neuroscience researchers to input the data in their own "language". It is a cross-platform simulator, so it does not require any special equipment, operative system or support program, and does not need installation. The "R&W Simulator"(version 3.0) is available free. © 2012 Elsevier Ireland Ltd.


Mondragon E.,Center for Computational and Animal Learning Research | Alonso E.,City University London | Fernandez A.,Rey Juan Carlos University | Gray J.,City University London
Computer Methods and Programs in Biomedicine | Year: 2013

This paper introduces R&W Simulator version 4, which extends previous work by incorporating context simulation within standard Pavlovian designs. This addition allows the assessment of: (1) context-stimulus competition, by treating contextual cues as ordinary background stimuli present throughout the whole experimental session; (2) summation, by computing compound stimuli with contextual cues as an integrating feature, with and without the addition of specific configural cues; and (3) contingency effects in causal learning. These new functionalities broaden the range of experimental designs that the simulator is able to replicate, such as some recovery from extinction phenomena (e.g., renewal effects). In addition, the new version permits specifying probe trials among standard trials and extracting their values. © 2013 Elsevier Ireland Ltd.


Alonso E.,City University London | Mondragon E.,Center for Computational and Animal Learning Research
ICAART 2013 - Proceedings of the 5th International Conference on Agents and Artificial Intelligence | Year: 2013

In this position paper we propose to enhance learning algorithms, reinforcement learning in particular, for agents and for multi-agent systems, with the introduction of concepts and mechanisms borrowed from associative learning theory. It is argued that existing algorithms are limited in that they adopt a very restricted view of what "learning" is, partly due to the constraints imposed by the Markov assumption upon which they are built. Interestingly, psychological theories of associative learning account for a wide range of social behaviours, making it an ideal framework to model learning in single agent scenarios as well as in multi-agent domains.


Mondragon E.,Center for Computational and Animal Learning Research | Gray J.,Center for Computational and Animal Learning Research | Gray J.,University of Southampton | Alonso E.,Center for Computational and Animal Learning Research | And 5 more authors.
PLoS ONE | Year: 2014

This paper presents a novel representational framework for the Temporal Difference (TD) model of learning, which allows the computation of configural stimuli - cumulative compounds of stimuli that generate perceptual emergents known as configural cues. This Simultaneous and Serial Configural-cue Compound Stimuli Temporal Difference model (SSCC TD) can model both simultaneous and serial stimulus compounds, as well as compounds including the experimental context. This modification significantly broadens the range of phenomena which the TD paradigm can explain, and allows it to predict phenomena which traditional TD solutions cannot, particularly effects that depend on compound stimuli functioning as a whole, such as pattern learning and serial structural discriminations, and context-related effects. © 2014 Mondragón et al.


PubMed | Center for Computational and Animal Learning Research
Type: Journal Article | Journal: Computer methods and programs in biomedicine | Year: 2013

This paper introduces R&W Simulator version 4, which extends previous work by incorporating context simulation within standard Pavlovian designs. This addition allows the assessment of: (1) context-stimulus competition, by treating contextual cues as ordinary background stimuli present throughout the whole experimental session; (2) summation, by computing compound stimuli with contextual cues as an integrating feature, with and without the addition of specific configural cues; and (3) contingency effects in causal learning. These new functionalities broaden the range of experimental designs that the simulator is able to replicate, such as some recovery from extinction phenomena (e.g., renewal effects). In addition, the new version permits specifying probe trials among standard trials and extracting their values.

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