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Average Is Optimal: An Inverted-U Relationship between Trial-to-Trial Brain Activity and Behavioral Performance

Overview of attention for article published in PLoS Computational Biology, November 2013
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Title
Average Is Optimal: An Inverted-U Relationship between Trial-to-Trial Brain Activity and Behavioral Performance
Published in
PLoS Computational Biology, November 2013
DOI 10.1371/journal.pcbi.1003348
Pubmed ID
Authors

Biyu J. He, John M. Zempel

Abstract

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.

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Geographical breakdown

Country Count As %
United States 5 4%
Germany 2 2%
Netherlands 1 <1%
Switzerland 1 <1%
Unknown 110 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 27%
Researcher 27 23%
Student > Master 17 14%
Student > Bachelor 8 7%
Student > Doctoral Student 4 3%
Other 13 11%
Unknown 18 15%
Readers by discipline Count As %
Neuroscience 24 20%
Psychology 23 19%
Agricultural and Biological Sciences 17 14%
Engineering 11 9%
Medicine and Dentistry 7 6%
Other 15 13%
Unknown 22 18%