Individual Differences in Learning Social and Non-Social Network Structures

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 02 Sep 2017Embargo end date: 01 Jan 2017Publisher:Center for Open ScienceJournal:Journal of Experimental Psychology: Learning, Memory, and Cognition, volume 45, pages 253-271 (issn: 0278-7393, eissn: 1939-1285,

Copyright policy )Funded by:NIH | CRCNS: US-France Modeling..., NIH | Multimodal brain maturati..., NSF | CAREER: Linking Graph Top... +5 projects

Authors: Bassett, Danielle; Vettel, Jean; Falk, Emily; Kahn, Ari; Tompson, Steven; Falk, Emily; Kahn, Ari;

doi: 10.31234/osf.io/zs6mc_v1 , 10.1037/xlm0000580 , 10.31234/osf.io/zs6mc , 10.17605/osf.io/zs6mc , 10.48550/arxiv.1709.02852

pmid: 30024255

pmc: PMC6339609

arXiv: 1709.02852

Individual Differences in Learning Social and Non-Social Network Structures

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Abstract

Learning about complex associations between pieces of information enables individuals to quickly adjust their expectations and develop mental models. Yet, the degree to which humans can learn higher-order information about complex associations is not well understood; nor is it known whether the learning process differs for social and non-social information. Here, we employ a paradigm in which the order of stimulus presentation forms temporal associations between the stimuli, collectively constituting a complex network structure. We examined individual differences in the ability to learn network topology for which stimuli were social versus non-social. Although participants were able to learn both social and non-social networks, their performance in social network learning was uncorrelated with their performance in non-social network learning. Importantly, social traits, including social orientation and perspective-taking, uniquely predicted the learning of social networks but not the learning of non-social networks. Taken together, our results suggest that the process of learning higher-order structure in social networks is independent from the process of learning higher-order structure in non-social networks. Our study design provides a promising approach to identify neurophysiological drivers of social network versus non-social network learning, extending our knowledge about the impact of individual differences on these learning processes. Implications for how people learn and adapt to new social contexts that require integration into a new social network are discussed.

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University of California, San Francisco
United States
University of Pennsylvania
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UNIVERSITY OF PENNSYLVANIA
Department of Bioengineering University of California San Diego
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University of California System
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NIH| CRCNS: US-France Modeling & Predicting BCI Learning from Dynamic Networks, NIH| Multimodal brain maturation indices modulating psychopathology and neurocognition, NSF| CAREER: Linking Graph Topology of Learned Information to Behavioral Variability via Dynamics of Functional Brain Networks, NIH| Longitudinal multi-modal neuroimaging of irritability in youth