Decoding social decisions from movement kinematics

Giacomo Turri; Andrea Cavallo; Luca Romeo; Massimiliano Pontil; Alan Sanfey; Stefano Panzeri; Cristina Becchio

doi:10.1016/j.isci.2022.105550

Decoding social decisions from movement kinematics

iScience. 2022 Nov 10;25(12):105550. doi: 10.1016/j.isci.2022.105550. eCollection 2022 Dec 22.

Authors

Giacomo Turri^{1

2}, Andrea Cavallo^{2

1}, Luca Romeo³, Massimiliano Pontil^{4

5}, Alan Sanfey^{6

7}, Stefano Panzeri^{8

9}, Cristina Becchio^{10

1}

Affiliations

¹ Cognition, Motion and Neuroscience Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy.
² Department of Psychology, University of Turin, Torino, Italy.
³ Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.
⁴ Computational Statistics and Machine Learning Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy.
⁵ Department of Computer Science, University College London, London, UK.
⁶ Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen 6525 EN, the Netherlands.
⁷ Behavioral Science Institute, Radboud University, Nijmegen 6525 HR, the Netherlands.
⁸ Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
⁹ Neural Computational Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy.
¹⁰ Department of Neurology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.

Abstract

Decisions, including social decisions, are ultimately expressed through actions. However, very little is known about the kinematics of social decisions, and whether movements might reveal important aspects of social decision-making. We addressed this question by developing a motor version of a widely used behavioral economic game - the Ultimatum Game - and using a multivariate kinematic decoding approach to map parameters of social decisions to the single-trial kinematics of individual responders. Using this approach, we demonstrated that movement contains predictive information about both the fairness of a proposed offer and the choice to either accept or reject that offer. This information is expressed in personalized kinematic patterns that are consistent within a given responder, but that varies from one responder to another. These results provide insights into the relationship between decision-making and sensorimotor control, as they suggest that hand kinematics can reveal hidden parameters of complex, social interactive, choice.

Keywords: Social interaction; research methodology social sciences; social sciences.