Mind-reading without the scanner: Behavioural decoding of working memory content
Sophisticated machine learning algorithms have been successfully applied to functional neuroimaging data in order to characterize internal cognitive states. But is it possible to “mind-read” without the scanner? Capitalizing on the robust finding that the contents of working memory guide visual attention toward memory-matching objects, we trained a multivariate pattern classifier on behavioural indices of attentional guidance. Working memory representations were successfully decoded from behaviour alone, both within and between individuals. The current study provides a proof-of-concept for applying machine learning techniques to simple behavioural outputs (e.g., response times) in order to decode information about specific internal cognitive states.
Duke Scholars
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- Experimental Psychology
- 5204 Cognitive and computational psychology
- 1702 Cognitive Sciences
- 1701 Psychology
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Experimental Psychology
- 5204 Cognitive and computational psychology
- 1702 Cognitive Sciences
- 1701 Psychology