Decoding and perturbing decision states in real time

Diogo Peixoto (), Jessica R. Verhein (), Roozbeh Kiani, Jonathan C. Kao, Paul Nuyujukian, Chandramouli Chandrasekaran, Julian Brown, Sania Fong, Stephen I. Ryu, Krishna V. Shenoy and William T. Newsome ()
Additional contact information
Diogo Peixoto: Stanford University
Jessica R. Verhein: Stanford University
Roozbeh Kiani: New York University
Jonathan C. Kao: Stanford University
Paul Nuyujukian: Stanford University
Chandramouli Chandrasekaran: Stanford University
Julian Brown: Stanford University
Sania Fong: Stanford University
Stephen I. Ryu: Stanford University
Krishna V. Shenoy: Stanford University
William T. Newsome: Stanford University

Nature, 2021, vol. 591, issue 7851, 604-609

Abstract: Abstract In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment1. The process of deliberation can be described by a time-varying decision variable (DV), decoded from neural population activity, that predicts a subject’s upcoming decision2. Within single trials, however, there are large moment-to-moment fluctuations in the DV, the behavioural significance of which is unclear. Here, using real-time, neural feedback control of stimulus duration, we show that within-trial DV fluctuations, decoded from motor cortex, are tightly linked to decision state in macaques, predicting behavioural choices substantially better than the condition-averaged DV or the visual stimulus alone. Furthermore, robust changes in DV sign have the statistical regularities expected from behavioural studies of changes of mind3. Probing the decision process on single trials with weak stimulus pulses, we find evidence for time-varying absorbing decision bounds, enabling us to distinguish between specific models of decision making.

Date: 2021
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DOI: 10.1038/s41586-020-03181-9

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