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

Overview of attention for article published in Biological Cybernetics, September 2017
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Title
Affective–associative two-process theory: a neurocomputational account of partial reinforcement extinction effects
Published in
Biological Cybernetics, September 2017
DOI 10.1007/s00422-017-0730-1
Pubmed ID
Authors

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

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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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 27%
Student > Ph. D. Student 5 19%
Professor 3 12%
Student > Master 3 12%
Student > Doctoral Student 1 4%
Other 1 4%
Unknown 6 23%
Readers by discipline Count As %
Psychology 8 31%
Computer Science 4 15%
Neuroscience 3 12%
Medicine and Dentistry 2 8%
Social Sciences 1 4%
Other 2 8%
Unknown 6 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 September 2017.
All research outputs
#15,479,632
of 23,002,898 outputs
Outputs from Biological Cybernetics
#503
of 679 outputs
Outputs of similar age
#198,573
of 316,254 outputs
Outputs of similar age from Biological Cybernetics
#3
of 5 outputs
Altmetric has tracked 23,002,898 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 679 research outputs from this source. They receive a mean Attention Score of 4.1. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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