Minimalist Social-Affective Value for Use in Joint Action: A Neural-Computational Hypothesis
Artikel i vetenskaplig tidskrift, 2016

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.

joint action

social Aff-ATP hypothesis

extended common currency


associative two-process theory

minimal architectures

social value computation


Robert Lowe

Göteborgs universitet

Alexander Almér

Göteborgs universitet

Gustaf Lindblad

Chalmers, Tillämpad informationsteknologi, Kognition och kommunikation

Frontiers in Computational Neuroscience

1662-5188 (ISSN)

Vol. 10 88-


Psykologi (exklusive tillämpad psykologi)