Mind-reading without the scanner: Behavioural decoding of working memory content
Published
Journal Article
© 2015 Taylor & Francis. 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.
Full Text
Duke Authors
Cited Authors
- Dowd, EW; Pearson, JM; Egner, T
Published Date
- January 1, 2015
Published In
Volume / Issue
- 23 / 7
Start / End Page
- 862 - 866
Electronic International Standard Serial Number (EISSN)
- 1464-0716
International Standard Serial Number (ISSN)
- 1350-6285
Digital Object Identifier (DOI)
- 10.1080/13506285.2015.1093244
Citation Source
- Scopus