From concrete examples to abstract relations: The rostrolateral prefrontal cortex integrates novel examples into relational categories

Tyler Davis, Micah Goldwater, Josue Giron

Research output: Contribution to journalArticle

13 Scopus citations

Abstract

The ability to form relational categories for objects that share few features in common is a hallmark of human cognition. For example, anything that can play a preventative role, from a boulder to poverty, can be a "barrier." However, neurobiological research has focused solely on how people acquire categories defined by features. The present functional magnetic resonance imaging study examines how relational and feature-based category learning compare in well-matched learning tasks. Using a computational model-based approach, we observed a cluster in left rostrolateral prefrontal cortex (rlPFC) that tracked quantitative predictions for the representational distance between test and training examples during relational categorization. Contrastingly, medial and dorsal PFC exhibited graded activation that tracked decision evidence during both feature-based and relational categorization. The results suggest that rlPFC computes an alignment signal that is critical for integrating novel examples during relational categorization whereas other PFC regions support more general decision functions.

Original languageEnglish
Pages (from-to)2652-2670
Number of pages19
JournalCerebral Cortex
Volume27
Issue number4
DOIs
StatePublished - 2017

Keywords

  • Category learning
  • Entropy
  • Reasoning
  • Representational distance
  • Same-different learning

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