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Affective-associative two-process theory: a neurocomputational account of partial reinforcement extinction effects

Journal article
Authors Robert Lowe
Alexander Almér
E. Billing
Y. Sandamirskaya
C. Balkenius
Published in Biological Cybernetics
Volume 111
Issue 5-6
Pages 365-388
ISSN 0340-1200
Publication year 2017
Published at Department of Applied Information Technology (GU)
Pages 365-388
Language en
Links https://doi.org/10.1007/s00422-017-...
Keywords Partial reinforcement, Reinforcement learning, Decision making, Associative two-process theory, orbitofrontal cortex, behavioral momentum, outcome expectancies, multiple schedules, reversed pree, model, hypothesis, resistance, persistence, prediction, Computer Science, Neurosciences & Neurology
Subject categories Neurosciences, Computer and Information Science

Abstract

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.

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