Average is optimal: an inverted-U relationship between trial-to-trial brain activity and behavioral performance

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations...

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Bibliographic Details
Published in:PLoS computational biology 2013-11, Vol.9 (11), p.e1003348-e1003348
Main Authors: He, Biyu J, Zempel, John M
Format: Article
Language:English
Subjects:
Adult
Behavior
Behavior - physiology
Behavioral assessment
Brain
Brain - physiology
Brain Mapping - methods
Brain research
Electroencephalography
Female
Humans
Male
Middle Aged
Neurophysiology
NMR
Noise
Nuclear magnetic resonance
Patients
Photic Stimulation
Principal Component Analysis
Reaction Time - physiology
Studies
Task Performance and Analysis
Time series
Young Adult
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Summary:It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance.
ISSN:1553-7358
1553-734X
1553-7358
DOI:10.1371/journal.pcbi.1003348