Why more is better: Simultaneous modeling of EEG, fMRI, and behavioral data

Brandon M. Turner, Christian A. Rodriguez, Tony M. Norcia, Samuel McClure, Mark Steyvers

Research output: Contribution to journalArticlepeer-review

37 Scopus citations

Abstract

The need to test a growing number of theories in cognitive science has led to increased interest in inferential methods that integrate multiple data modalities. In this manuscript, we show how a method for integrating three data modalities within a single framework provides (1) more detailed descriptions of cognitive processes and (2) more accurate predictions of unobserved data than less integrative methods. Specifically, we show how combining either EEG and fMRI with a behavioral model can perform substantially better than a behavioral-data-only model in both generative and predictive modeling analyses. We then show how a trivariate model - a model including EEG, fMRI, and behavioral data - outperforms bivariate models in both generative and predictive modeling analyses. Together, these results suggest that within an appropriate modeling framework, more data can be used to better constrain cognitive theory, and to generate more accurate predictions for behavioral and neural data.

Original languageEnglish (US)
Pages (from-to)96-115
Number of pages20
JournalNeuroImage
Volume128
DOIs
StatePublished - Mar 1 2016

Keywords

  • Bayesian modeling
  • EEG
  • FMRI
  • Joint modeling framework
  • Linear ballistic accumulator model

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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