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Affective-Associative Two-Process theory: A neural network investigation of adaptive behaviour in differential outcomes training
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Göteborgs Universitet, Tillämpad IT. (Interaction Lab)
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Interaction Lab)ORCID iD: 0000-0002-6568-9342
2017 (English)In: Adaptive Behavior, ISSN 1059-7123, E-ISSN 1741-2633, Vol. 25, no 1, 5-23 p.Article in journal (Refereed) Published
Abstract [en]

In this article we present a novel neural network implementation of Associative Two-Process (ATP) theory based on an Actor–Critic-like architecture. Our implementation emphasizes the affective components of differential reward magnitude and reward omission expectation and thus we model Affective-Associative Two-Process theory (Aff-ATP). ATP has been used to explain the findings of differential outcomes training (DOT) procedures, which emphasize learning differentially valuated outcomes for cueing actions previously associated with those outcomes. ATP hypothesizes the existence of a ‘prospective’ memory route through which outcome expectations can bring to bear on decision making and can even substitute for decision making based on the ‘retrospective’ inputs of standard working memory. While DOT procedures are well recognized in the animal learning literature they have not previously been computationally modelled. The model presented in this article helps clarify the role of ATP computationally through the capturing of empirical data based on DOT. Our Aff-ATP model illuminates the different roles that prospective and retrospective memory can have in decision making (combining inputs to action selection functions). In specific cases, the model’s prospective route allows for adaptive switching (correct action selection prior to learning) following changes in the stimulus–response–outcome contingencies.

Place, publisher, year, edition, pages
Sage Publications, 2017. Vol. 25, no 1, 5-23 p.
Keyword [en]
Actor–Critic, Reward learning, Animal models, Associative-Two Process theory, Neo-behaviourism, Decision making
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Natural sciences; Interaction Lab (ILAB)
Identifiers
URN: urn:nbn:se:his:diva-13405DOI: 10.1177/1059712316682999ISI: 000394678600002Scopus ID: 2-s2.0-85012120144OAI: oai:DiVA.org:his-13405DiVA: diva2:1076565
Funder
EU, FP7, Seventh Framework Programme, 270247
Available from: 2017-02-23 Created: 2017-02-23 Last updated: 2017-05-22Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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