Mind-reading without the scanner: Behavioural decoding of working memory content

Abstract

ABSTRACTSophisticated 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.