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Affective–associative two-process theory: a neurocomputational account of partial reinforcement extinction effects
Department of Applied IT, University of Gothenburg, Gothenburg, Sweden. (Interaction Lab)
Department of Applied IT, University of Gothenburg, Gothenburg, Sweden.
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Interaction Lab (ILAB))ORCID iD: 0000-0002-6568-9342
Institute of Neuroinformatics, Neuroscience Center Zurich, University and ETH Zurich, Zurich, Switzerland.
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2017 (English)In: Biological Cybernetics, ISSN 0340-1200, E-ISSN 1432-0770, Vol. 111, no 5-6, 365-388 p.Article in journal (Refereed) Published
Abstract [en]

The partial reinforcement extinction effect (PREE) is an experimentally established phenomenon: behavioural response to a given stimulus is more persistent when previously inconsistently rewarded than when consistently rewarded. This phenomenon is, however, controversial in animal/human learning theory. Contradictory findings exist regarding when the PREE occurs. One body of research has found a within-subjects PREE, while another has found a within-subjects reversed PREE (RPREE). These opposing findings constitute what is considered the most important problem of PREE for theoreticians to explain. Here, we provide a neurocomputational account of the PREE, which helps to reconcile these seemingly contradictory findings of within-subjects experimental conditions. The performance of our model demonstrates how omission expectancy, learned according to low probability reward, comes to control response choice following discontinuation of reward presentation (extinction). We find that a PREE will occur when multiple responses become controlled by omission expectation in extinction, but not when only one omission-mediated response is available. Our model exploits the affective states of reward acquisition and reward omission expectancy in order to differentially classify stimuli and differentially mediate response choice. We demonstrate that stimulus–response (retrospective) and stimulus–expectation–response (prospective) routes are required to provide a necessary and sufficient explanation of the PREE versus RPREE data and that Omission representation is key for explaining the nonlinear nature of extinction data.

Place, publisher, year, edition, pages
Springer, 2017. Vol. 111, no 5-6, 365-388 p.
Keyword [en]
Partial reinforcement, Reinforcement learning, Decision making, Associative two-process theory, Affect
National Category
Other Computer and Information Science
Research subject
Interaction Lab (ILAB)
Identifiers
URN: urn:nbn:se:his:diva-14392DOI: 10.1007/s00422-017-0730-1PubMedID: 28913644OAI: oai:DiVA.org:his-14392DiVA: diva2:1156311
Projects
NeuralDynamics, 7th framework of the EU, grant #270247
Funder
EU, FP7, Seventh Framework Programme, 270247
Available from: 2017-11-11 Created: 2017-11-11 Last updated: 2017-11-21

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