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Minimalist Social-Affective Value for Use in Joint Action: A Neural-Computational Hypothesis

Frontiers in Computational Neuroscience. 2016;10 DOI 10.3389/fncom.2016.00088


Journal Homepage

Journal Title: Frontiers in Computational Neuroscience

ISSN: 1662-5188 (Online)

Publisher: Frontiers Media S.A.

LCC Subject Category: Medicine: Internal medicine: Neurosciences. Biological psychiatry. Neuropsychiatry

Country of publisher: Switzerland

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML



Robert J Lowe (University of Skövde)

Robert J Lowe (University of Gothenburg)

Alexander Almer (University of Gothenburg)

Gustaf Lindblad (University of Gothenburg)

Pierre Gander (University of Gothenburg)

John Michael (Central European University)

Cordula Vesper (Central European University)


Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 14 weeks


Abstract | Full Text

Joint Action is typically described as social interaction that requires coordination among two or more co-actors in order to achieve a common goal. In this article, we put forward a hypothesis for the existence of a neural-computational mechanism of affective valuation that may be critically exploited in Joint Action. Such a mechanism would serve to facilitate coordination between co-actors permitting a reduction of required information. Our hypothesized affective mechanism provides a value function based implementation of Associative Two-Process theory that entails the classification of external stimuli according to outcome expectancies. This approach has been used to describe animal and human action that concerns differential outcome expectancies. Until now it has not been applied to social interaction. We describe our Affective Associative Two-Process (ATP) model as applied to social learning consistent with an ‘extended common currency’ perspective in the social neuroscience literature. We contrast this to an alternative mechanism that provides an example implementation of the so-called social-specific value perspective. In brief, our Social-Affective ATP mechanism builds upon established formalisms for reinforcement learning (temporal difference learning models) nuanced to accommodate expectations (consistent with ATP theory) and extended to integrate non-social and social cues for use in Joint Action.