Affordable Access

deepdyve-link
Publisher Website

Identifying the neural dynamics of category decisions with computational model-based functional magnetic resonance imaging.

Authors
  • Heffernan, Emily M1
  • Adema, Juliana D1
  • Mack, Michael L2
  • 1 Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON, M5S 3G3, Canada. , (Canada)
  • 2 Department of Psychology, University of Toronto, 100 St. George Street, Toronto, ON, M5S 3G3, Canada. [email protected] , (Canada)
Type
Published Article
Journal
Psychonomic bulletin & review
Publication Date
Oct 01, 2021
Volume
28
Issue
5
Pages
1638–1647
Identifiers
DOI: 10.3758/s13423-021-01939-4
PMID: 33963487
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Successful categorization requires a careful coordination of attention, representation, and decision making. Comprehensive theories that span levels of analysis are key to understanding the computational and neural dynamics of categorization. Here, we build on recent work linking neural representations of category learning to computational models to investigate how category decision making is driven by neural signals across the brain. We uniquely combine functional magnetic resonance imaging with drift diffusion and exemplar-based categorization models to show that trial-by-trial fluctuations in neural activation from regions of occipital, cingulate, and lateral prefrontal cortices are linked to category decisions. Notably, only lateral prefrontal cortex activation was associated with exemplar-based model predictions of trial-by-trial category evidence. We propose that these brain regions underlie distinct functions that contribute to successful category learning. © 2021. The Psychonomic Society, Inc.

Report this publication

Statistics

Seen <100 times