Breaking human social decision making into multiple components and then putting them together again

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 to uncover 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 key computations necessary for decision-making and how these computations are integrated. Furthermore, by correlating these computational variables against neuroimaging data, it has become possible to elucidate where in the brain various computational variables are implemented during social decision making. 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 that are conducted in parallel and which are 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.

A Computational Study of Executive Dysfunction in Amyotrophic Lateral Sclerosis

“…Computational modeling offers a technique to decompose behavior on neuropsychological assessment instruments into assumed latent cognitive processes, allowing inferences closer to the level of covert cognitive processes [ 41 , 46 ]. Importantly, latent variables, which reflect the efficacy of covert cognitive processes, are unambiguously defined (see Appendix B ) and, thereby, may replace the traditional verbal constructs of neuropsychological assessment [ 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. The present study exemplifies that latent variables obtained from computational modeling may provide indicators of shared latent symptoms as well as nosologically specific differentiable facets of latent executive dysfunctions.…”

“…Computational cognitive neuropsychology offers an approach to decompose behavior that was observed on neuropsychological assessment instruments into covert cognitive processes [ 41 , 46 ]. Computational cognitive neuropsychology utilizes mathematical formalization of (1) the assumed covert cognitive processes, and (2) the way in which these processes interact [ 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 ]. Analyzing behavior on a neuropsychological assessment instrument, such as the WCST, via computational modeling allows estimating a set of latent variables, which reflect the efficacy of the assumed covert cognitive processes.…”

Extrinsic Factors Underlying Food Valuation in the Human Brain

“…The findings discussed in this review are broadly consistent with the notion that extrinsic factors of food reward modulate the value signal in the vmPFC through functional connectivity with multiple brain regions that track information about each extrinsic factor. However, more evidence is needed to deepen the understanding of how the multiple types of information become integrated within the brain to compute an overall food value (see Suzuki et al, 2015 ; Suzuki and O’Doherty, 2020 for similar issues in social decision-making). For example, to elucidate the integration process, it would be helpful to examine the nature of functional and anatomical connectivity among the brain regions engaged in the food valuation (e.g., vmPFC and dlPFC).…”

Extrinsic factors underlying food valuation in the human brain

“…The findings discussed in this review are broadly consistent with the notion that extrinsic factors of food reward modulate the value signal in the vmPFC through functional connectivity with multiple brain regions that track information about each extrinsic factor. However, more evidence is needed to deepen the understanding of how the multiple types of information become integrated within the brain to compute an overall food value (see Suzuki et al, 2015;Suzuki and O'Doherty, 2020 for similar issues in social decision-making). For example, to elucidate the integration process, it would be helpful to examine the nature of functional and anatomical connectivity among the brain regions engaged in the food valuation (e.g., vmPFC and dlPFC).…”