Decoding social decisions from movement kinematics
Giacomo Turri,
Andrea Cavallo,
Luca Romeo,
Massimiliano Pontil,
Alan Sanfey,
Stefano Panzeri,
Cristina Becchio
Affiliations
Giacomo Turri
Cognition, Motion and Neuroscience Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy; Department of Psychology, University of Turin, Torino, Italy
Andrea Cavallo
Department of Psychology, University of Turin, Torino, Italy; Cognition, Motion and Neuroscience Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy
Luca Romeo
Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
Massimiliano Pontil
Computational Statistics and Machine Learning Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy; Department of Computer Science, University College London, London, UK
Alan Sanfey
Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen 6525 EN, the Netherlands; Behavioral Science Institute, Radboud University, Nijmegen 6525 HR, the Netherlands
Stefano Panzeri
Department of Excellence for Neural Information Processing, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany; Neural Computational Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy
Cristina Becchio
Department of Neurology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany; Cognition, Motion and Neuroscience Laboratory, Center for Human Technologies, Istituto Italiano di Tecnologia, Genova, Italy; Corresponding author
Summary: 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.
