Individual differences in social information gathering revealed through Bayesian hierarchical models.

Published

Journal Article

As studies of the neural circuits underlying choice expand to include more complicated behaviors, analysis of behaviors elicited in laboratory paradigms has grown increasingly difficult. Social behaviors present a particular challenge, since inter- and intra-individual variation are expected to play key roles. However, due to limitations on data collection, studies must often choose between pooling data across all subjects or using individual subjects' data in isolation. Hierarchical models mediate between these two extremes by modeling individual subjects as drawn from a population distribution, allowing the population at large to serve as prior information about individuals' behavior. Here, we apply this method to data collected across multiple experimental sessions from a set of rhesus macaques performing a social information valuation task. We show that, while the values of social images vary markedly between individuals and between experimental sessions for the same individual, individuals also differentially value particular categories of social images. Furthermore, we demonstrate covariance between values for image categories within individuals and find evidence suggesting that magnitudes of stimulus values tend to diminish over time.

Full Text

Duke Authors

Cited Authors

  • Pearson, JM; Watson, KK; Klein, JT; Ebitz, RB; Platt, ML

Published Date

  • January 2013

Published In

Volume / Issue

  • 7 /

Start / End Page

  • 165 -

PubMed ID

  • 24062635

Pubmed Central ID

  • 24062635

Electronic International Standard Serial Number (EISSN)

  • 1662-453X

International Standard Serial Number (ISSN)

  • 1662-4548

Digital Object Identifier (DOI)

  • 10.3389/fnins.2013.00165

Language

  • eng