Decoding and perturbing decision states in real time

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment 1 . 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 decis...

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Published inNature (London) Vol. 591; no. 7851; pp. 604 - 609
Main Authors Peixoto, Diogo, Verhein, Jessica R., Kiani, Roozbeh, Kao, Jonathan C., Nuyujukian, Paul, Chandrasekaran, Chandramouli, Brown, Julian, Fong, Sania, Ryu, Stephen I., Shenoy, Krishna V., Newsome, William T.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 25.03.2021
Nature Publishing Group
Subjects
631/378/116/2394
631/378/2629/1409
631/378/2632/2634
631/378/2632/2635
631/378/2649/1409
Accuracy
Analysis
Animals
Behavior
Choice Behavior - physiology
Cortex (motor)
Decision making
Decision Making - physiology
Decision tree
Decoding (Reading)
Discrimination, Psychological
Experiments
Feedback control
Fluctuations
Humanities and Social Sciences
Judgment
Macaca - physiology
Macaques
Methods
Models, Neurological
Motion
Motion Perception
multidisciplinary
Perturbation (Mathematics)
Photic Stimulation
Physiological aspects
Population
Real time
Science
Science (multidisciplinary)
Signal processing
Statistical methods
Time Factors
Visual stimuli
United States
Online AccessGet full text
ISSN0028-0836
1476-4687
1476-4687
DOI10.1038/s41586-020-03181-9

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Abstract In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment 1 . 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 decision 2 . 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 mind 3 . 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. In macaque motor cortex, moment-to-moment fluctuations in neurally derived decision variables are tightly linked to decision state and predict behavioural choices with better accuracy than condition-averaged decision variables or the visual stimulus alone, and can be used to distinguish between different models of decision making.
AbstractList In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment 1 . 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 decision 2 . 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 mind 3 . 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. In macaque motor cortex, moment-to-moment fluctuations in neurally derived decision variables are tightly linked to decision state and predict behavioural choices with better accuracy than condition-averaged decision variables or the visual stimulus alone, and can be used to distinguish between different models of decision making.
In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment.sup.1. 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 decision.sup.2. 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 mind.sup.3. 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.
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.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.
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.
In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment.sup.1. 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 decision.sup.2. 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 mind.sup.3. 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. In macaque motor cortex, moment-to-moment fluctuations in neurally derived decision variables are tightly linked to decision state and predict behavioural choices with better accuracy than condition-averaged decision variables or the visual stimulus alone, and can be used to distinguish between different models of decision making.
In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment . 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 decision . 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 mind . 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.
Audience Academic
Author Kao, Jonathan C.
Nuyujukian, Paul
Peixoto, Diogo
Brown, Julian
Verhein, Jessica R.
Shenoy, Krishna V.
Kiani, Roozbeh
Chandrasekaran, Chandramouli
Ryu, Stephen I.
Fong, Sania
Newsome, William T.
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  orcidid: 0000-0002-4370-5022
  surname: Newsome
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/33473215$$D View this record in MEDLINE/PubMed
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Snippet In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment 1 . The process of deliberation...
In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment . The process of deliberation...
In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment.sup.1. The process of...
In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment1. The process of deliberation...
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631/378/2629/1409
631/378/2632/2634
631/378/2632/2635
631/378/2649/1409
Accuracy
Analysis
Animals
Behavior
Choice Behavior - physiology
Cortex (motor)
Decision making
Decision Making - physiology
Decision tree
Decoding (Reading)
Discrimination, Psychological
Experiments
Feedback control
Fluctuations
Humanities and Social Sciences
Judgment
Macaca - physiology
Macaques
Methods
Models, Neurological
Motion
Motion Perception
multidisciplinary
Perturbation (Mathematics)
Photic Stimulation
Physiological aspects
Population
Real time
Science
Science (multidisciplinary)
Signal processing
Statistical methods
Time Factors
Visual stimuli
Title Decoding and perturbing decision states in real time
URI https://link.springer.com/article/10.1038/s41586-020-03181-9
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