Closed-loop training of attention with real-time brain imaging

Megan T. DeBettencourt, Jonathan D. Cohen, Ray F. Lee, Kenneth Andrew Norman, Nicholas Turk-Browne

Research output: Contribution to journalArticle

121 Scopus citations

Abstract

Lapses of attention can have negative consequences, including accidents and lost productivity. Here we used closed-loop neurofeedback to improve sustained attention abilities and reduce the frequency of lapses. During a sustained attention task, the focus of attention was monitored in real time with multivariate pattern analysis of whole-brain neuroimaging data. When indicators of an attentional lapse were detected in the brain, we gave human participants feedback by making the task more difficult. Behavioral performance improved after one training session, relative to control participants who received feedback from other participants' brains. This improvement was largest when feedback carried information from a frontoparietal attention network. A neural consequence of training was that the basal ganglia and ventral temporal cortex came to represent attentional states more distinctively. These findings suggest that attentional failures do not reflect an upper limit on cognitive potential and that attention can be trained with appropriate feedback about neural signals.

Original languageEnglish (US)
Pages (from-to)470-478
Number of pages9
JournalNature neuroscience
Volume18
Issue number3
DOIs
StatePublished - Mar 27 2015

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)

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