期刊
NEUROIMAGE
卷 128, 期 -, 页码 96-115出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2015.12.030
关键词
Joint modeling framework; Bayesian modeling; EEG; fMRI; Linear Ballistic Accumulator model
资金
- National Science Foundation [1358507]
- Divn Of Social and Economic Sciences
- Direct For Social, Behav & Economic Scie [1613264, 1358507] Funding Source: National Science Foundation
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. (C) 2015 Elsevier Inc. All rights reserved.
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