Individual differences in social information gathering revealed through Bayesian hierarchical models

John M. Pearson, Karli K. Watson, Jeffrey T. Klein, R. Becket Ebitz, Michael L. Platt

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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

Original languageEnglish (US)
Article number165
JournalFrontiers in Neuroscience
Issue number7 SEP
DOIs
StatePublished - 2013
Externally publishedYes

Keywords

  • Bayesian hierarchical model
  • Pay-per-view
  • Social information

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