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  • 1.
    Lowe, Robert
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Ice Lab, Applied IT, University of Gothenburg, Gothenburg, Sweden.
    Almér, Alexander
    Ice Lab, Applied IT, University of Gothenburg, Gothenburg, Sweden.
    Lindblad, Gustaf
    Ice Lab, Applied IT, University of Gothenburg, Gothenburg, Sweden.
    Gander, Pierre
    Ice Lab, Applied IT, University of Gothenburg, Gothenburg, Sweden.
    Michael, John
    Department of Cognitive Science, Central European University, Budapest, Hungary.
    Vesper, Cordula
    Department of Cognitive Science, Central European University, Budapest, Hungary.
    Minimalist Social-Affective Value for Use in Joint Action: A Neural-Computational Hypothesis2016In: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 10, article id 88Article in journal (Refereed)
    Abstract [en]

    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 (ATP) 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 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.

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