Breaking human social decision making into multiple components and then putting them together again. (June 2020)

• Record Type: Journal Article
• Authors: Suzuki, Shinsuke; O'Doherty, John P.
• Is Part Of: Cortex. Volume 127(2020)
• Page Start: 221
• Page End: 230
• Publication Date: 2020-06
• Subjects: Social cognition -- Decision-making -- Model-based fMRI -- Reinforcement learning -- Computational neuroscience; Neuropsychology -- Periodicals; Nervous system -- Periodicals; Neurology -- Periodicals; Psychophysiology -- Periodicals; Behavior -- Periodicals; Neurology -- Periodicals; 612.825
• DOI: 10.1016/j.cortex.2020.02.014
• Languages: English
• ISSNs: 0010-9452

Abstract

Abstract: Most of our waking time as human beings is spent interacting with other individuals. In order to make good decisions in this social milieu, it is often necessary to make inferences about the internal states, traits and intentions of others. Recently, some progress has been made toward uncovering the neural computations underlying human social decision-making by combining functional magnetic resonance neuroimaging (fMRI) with computational modeling of behavior. Modeling of behavioral data allows us to identify the key computations necessary for social decision-making and to determine how these computations are integrated. Furthermore, by correlating these variables against neuroimaging data, it has become possible to elucidate where in the brain various computations are implemented. Here we review the current state of knowledge in the domain of social computational neuroscience. Findings to date have emphasized that social decisions are driven by multiple computations conducted in parallel, and implemented in distinct brain regions. We suggest that further progress is going to depend on identifying how and where such variables get integrated in order to yield a coherent behavioral output.