CaltechAUTHORS
  A Caltech Library Service

Getting to know you: general and specific neural computations for learning about people

Stanley, Damian A. (2016) Getting to know you: general and specific neural computations for learning about people. Social Cognitive and Affective Neuroscience, 11 (4). pp. 525-536. ISSN 1749-5016. PMCID PMC4814791. http://resolver.caltech.edu/CaltechAUTHORS:20151221-154135846

[img] PDF - Published Version
See Usage Policy.

562Kb

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20151221-154135846

Abstract

Learning about other peoples' attributes, e.g. whether an individual is generous or selfish, is central to human social cognition. It is well documented that a network of cortical regions is reliably activated when we engage social processes. However, little is known about the specific computations performed by these regions or whether such processing is specialized for the social domain. We investigated these questions using a task in which participants (N = 26) learned about four peoples’ generosity by watching them choose to share money with third party partners, or not. In a non-social control condition, participants learned the win/loss rates of four lotteries. fMRI analysis revealed learning-related general (social + non-social) prediction error signals in the dorsomedial and dorsolateral prefrontal cortices (bilaterally), and in the right lateral parietal cortex. Socially specific (social > non-social) prediction error signals were found in the precuneus. Interestingly, the region that exhibited social prediction errors was a distinct subregion of the area in the precuneus and posterior cingulate cortex that exhibited a commonly reported main effect of higher overall activity for social vs non-social stimuli. These findings elucidate the domain—general and—specific computations underlying learning about other people and demonstrate the increased explanatory power of computational approaches to social cognition.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1093/scan/nsv145DOIArticle
http://scan.oxfordjournals.org/content/11/4/525PublisherArticle
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4814791/PubMed CentralArticle
Additional Information:© 2015 The Author. Published by Oxford University Press. Received April 14, 2015. Revision received November 20, 2015. Accepted November 28, 2015. First published online: December 8, 2015. The author thanks Antonio Rangel for contributions to study design and data analysis as well as Ralph Adolphs, Hanah Chapman, Shabnam Hakimi, Catherine Hartley, Cendri Hutcherson, Peter Sokol-Hessner and Bob Spunt for comments on the article. This work was supported by the National Institute of Mental Health at the National Institutes of Health (grant number K01MH099343 to D.A.S.) and by a grant from the Gordon and Betty Moore Foundation to Antonio Rangel. The content is solely the responsibility of the author and does not necessarily represent the official views of the National Institutes of Health. The author declares no competing financial interests. Conflict of interest. None declared.
Funders:
Funding AgencyGrant Number
NIHK01MH099343
Gordon and Betty Moore FoundationUNSPECIFIED
National Institute of Mental Health (NIMH)UNSPECIFIED
Subject Keywords:fMRI; Computational Neuroscience; Trait Learning; Prediction Error; Precuneus
PubMed Central ID:PMC4814791
Record Number:CaltechAUTHORS:20151221-154135846
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20151221-154135846
Official Citation:Damian A. Stanley Getting to know you: general and specific neural computations for learning about people Soc Cogn Affect Neurosci (2016) 11 (4): 525-536 first published online December 8, 2015 doi:10.1093/scan/nsv145
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:63113
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:22 Dec 2015 18:07
Last Modified:18 Jul 2017 19:40

Repository Staff Only: item control page