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

Robert Lowe, Alexander Almér, Erik Billing, Yulia Sandamirskaya, Christian Balkenius

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8 Citations (SciVal)


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.

Original languageEnglish
Pages (from-to)365-388
JournalBiological Cybernetics
Issue number5-6
Early online date2017 Sep 14
Publication statusPublished - 2017 Dec

Subject classification (UKÄ)

  • Other Computer and Information Science
  • Philosophy


  • Affect
  • Associative two-process theory
  • Decision making
  • Partial reinforcement
  • Reinforcement learning


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