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