An extension of the Rescorla and Wagner Simulator for context conditioning

Mondragón, Esther; Alonso, Eduardo; Fernández, Alberto; Gray, Jonathan

doi:10.1016/j.cmpb.2013.01.016

Cited by 12 publications

(15 citation statements)

References 21 publications

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“…It is true that the RWM is a simple model but in reality most applications of the model actually use more than these two explicitly declared free parameters. It is common practise to allow different values of α for different cue types (e.g., context cues and configural cues may have lower values) and different β values for reinforced and non-reinforced trials (e.g., Mondragon et al, 2013). If the model is intended to make quantitative rather than just qualitative predictions then inclusion of a rule to map associative strength to response strength necessarily introduces additional parameters.…”

Section: Discussionmentioning

confidence: 99%

Revisiting the learning curve (once again)

Glautier

2013

Front. Psychol.

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The vast majority of published work in the field of associative learning seeks to test the adequacy of various theoretical accounts of the learning process using average data. Of course, averaging hides important information, but individual departures from the average are usually designated “error” and largely ignored. However, from the perspective of an individual differences approach, this error is the data of interest; and when associative models are applied to individual learning curves the error is substantial. To some extent individual differences can be reasonably understood in terms of parametric variations of the underlying model. Unfortunately, in many cases, the data cannot be accomodated in this way and the applicability of the underlying model can be called into question. Indeed several authors have proposed alternatives to associative models because of the poor fits between data and associative model. In the current paper a novel associative approach to the analysis of individual learning curves is presented. The Memory Environment Cue Array Model (MECAM) is described and applied to two human predictive learning datasets. The MECAM is predicated on the assumption that participants do not parse the trial sequences to which they are exposed into independent episodes as is often assumed when learning curves are modeled. Instead, the MECAM assumes that learning and responding on a trial may also be influenced by the events of the previous trial. Incorporating non-local information the MECAM produced better approximations to individual learning curves than did the Rescorla–Wagner Model (RWM) suggesting that further exploration of the approach is warranted.

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Section: Discussionmentioning

confidence: 99%

Revisiting the learning curve (once again)

Glautier

2013

Front. Psychol.

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“…Hence, learning between a CS and US in the RW model is driven by the total discrepancy between the US presence and the expectation elicited for it by all cues, as seen in the US. The RW model thus not only accounts for empirical data, but its formulation implied the existence of learning effects not predicted by earlier models or assumed to exist by preexisting theory, as can be tested in a wide range of simulations (Alonso, Mondragón, & Fernández, 2012;Mondragón, Alonso, Fernández, & Gray, 2013). Primary among these predictions is that if two CSs are independently conditioned to an asymptotic level, then their reinforcement in compound should lead to a decline in their associative strength.…”

Section: The Learning Rule: Error Correctionmentioning

confidence: 92%

A Double Error Dynamic Asymptote Model of Associative Learning

Kokkola

Mondragón

Alonso

2017

Preprint

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In this paper a formal model of associative learning is presented which incorporates representational and computational mechanisms that, as a coherent corpus, empower it to make accurate predictions of a wide variety of phenomena that so far have eluded a unified account in learning theory. In particular, the Double Error model introduces: 1) a fully-connected network architecture in which stimuli are represented as temporally distributed elements that associate to each other, which naturally implements neutral stimuli associations and mediated learning; 2) a predictor error term within the traditional error correction rule (the double error), which reduces the rate of learning for expected predictors; 3) a revaluation associability rate that operates on the assumption that the outcome predictiveness is tracked over time so that prolonged uncertainty is learned, reducing the levels of attention to initially surprising outcomes; and critically 4) a biologically plausible variable asymptote, which encapsulates the principle of Hebbian learning, leading to stronger associations for similar levels of element activity. The outputs of a set of simulations of the Double Error model are presented along with empirical results from the literature. Finally, the predictive scope of the model is discussed.Keywords: Associative learning, classical conditioning, computational modelling, real-time double error correction, variable asymptote Correspondence concerning this article should be addressed to Esther Mondragón, Centre for Computational and Animal Learning Research, 21 Bardwell Road, St Albans, Hertfordshire AL1 1RQ, United Kingdom. Email: e.mondragon@cal-r.org . CC-BY-NC-ND 4.0 International license not peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was . http://dx.doi.org/10.1101/210674 doi: bioRxiv preprint first posted online Oct. 28, 2017; 2 Associative learning aims at understanding the precise mechanisms by which humans and animals learn to relate events in their environment. Associative learning has been replicated across numerous species and procedures (Hall, 2002;Pearce & Bouton, 2001;Turkkan, 1989) ; its neural correlates have been extensively studied (Gomez et al., 2001;Kobayashi & Poo, 2004;Marschner, Kalisch, Vervliet, Vansteenwegen, & Büchel, 2011;Panayi & Killcross, 2014;Roesch, Esber, Li, Daw, & Schoenbaum, 2012); it has proved to be a core learning mechanism in high-order cognitive processes such as judgment of causality and categorization (Shanks, 1995), and rule learning (Murphy, Mondragón, & Murphy, 2008); it underpins a good number of clinical models (Haselgrove & Hogarth, 2011;Schachtman & Reilly, 2011); and its evolutionary origins are beginning to be elucidated (Ginsburg & Jablonka, 2010). It is thus paramount that we develop comprehensive, accurate models of associative learning.In classical conditioning, a fundamental pillar of associative learning, the repeated cooccurrence of two stimuli (e.g., an odour or tone), S1 and S2, is...

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“…These pairs are des- Note. Predictions were generated using Rescorla-Wagner simulator Model 4.0 (Mondragon et al, 2013). This document is copyrighted by the American Psychological Association or one of its allied publishers.…”

Section: Methodsmentioning

confidence: 99%

“…Table 2 shows predictions from the Rescorla-Wagner theory for the stimulus configurations in the first and third rows of Table 1, using the Rescorla-Wagner Simulator (Version 4) from the Centre for Computation and Animal Learning Research (Mondragon, Alonso, Fernandez, & Gray, 2013). The top row in the table shows the mean predicted discrimination for traditional versions of feature positive and feature negative discriminations in a within subject design, with all trials intermixed.…”

Section: Novelty As a Stimulus Featurementioning

confidence: 99%

On the representation of novel stimuli in patterning and irrelevant cue discriminations.

Whitlow¹

2018

Journal of Experimental Psychology: Animal Learning and Cogniti

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Two experiments explored ways in which novel stimuli might be represented in associative learning, focusing on (1) representations in which novel stimuli embody novelty as a stimulus feature that acquires associative strength in the same fashion as color, shape, texture, or other frequently used stimulus features; and (2) representations in which novel stimuli embody common elements, that is, the stimulus elements shared among other stimuli in an experimental setting. Both experiments examined the effects of reinforcing or nonreinforcing separately presented novel stimuli on learning about compound stimuli that included novel stimuli as part of the compound. Experiment 1 found that the relative ease of learning positive and negative patterning discriminations could be reversed, depending on whether novel stimuli were separately reinforced or nonreinforced. This result is consistent with predictions from a model that assumes novelty is a stimulus feature. Experiment 2 found that the additive effects of combining relevant and irrelevant cues in compound were not obtained when novel stimuli were used as irrelevant cues. This result is consistent with predictions from a model that assumes novel stimuli are represented as common elements. Implications for understanding how both types of representation account for the role of novelty in complex discrimination learning in general are discussed. (PsycINFO Database Record

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An extension of the Rescorla and Wagner Simulator for context conditioning

Cited by 12 publications

References 21 publications

Revisiting the learning curve (once again)

Revisiting the learning curve (once again)

A Double Error Dynamic Asymptote Model of Associative Learning

On the representation of novel stimuli in patterning and irrelevant cue discriminations.

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