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

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  • 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.Author Summary: The human brain is notoriously “noisy”. Even with identical physical sensory inputs and task demands, brain responses and behavioral output vary tremendously from trial to trial. Such brain and behavioral variability and the relationship between them have been the focus of intense neuroscience research for decades. Traditionally, it is thought that the relationship between trial-to-trial brain activity and behavioral performance is monotonic: the highest or lowest brain activity levels are associated with the best behavioral performance. Using invasive recordings in neurosurgical patients, we demonstrate an inverted-U relationship between brain and behavioral variability. Under such a relationship, moderate brain activity is associated with the best performance, while both very low and very high brain activity levels are predictive of compromised performance. These results have significant implications for our understanding of brain functioning. They further support recent theoretical frameworks that view the brain as an active nonlinear dynamical system instead of a passive signal-processing device.

Suggested Citation

  • Biyu J He & John M Zempel, 2013. "Average Is Optimal: An Inverted-U Relationship between Trial-to-Trial Brain Activity and Behavioral Performance," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-14, November.
  • Handle: RePEc:plo:pcbi00:1003348
    DOI: 10.1371/journal.pcbi.1003348
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    1. Yuan-hao Wu & Ella Podvalny & Max Levinson & Biyu J. He, 2024. "Network mechanisms of ongoing brain activity’s influence on conscious visual perception," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    2. Audrey J Sederberg & Aurélie Pala & He J V Zheng & Biyu J He & Garrett B Stanley, 2019. "State-aware detection of sensory stimuli in the cortex of the awake mouse," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-24, May.
    3. Jia-Hou Poh & Mai-Anh T. Vu & Jessica K. Stanek & Abigail Hsiung & Tobias Egner & R. Alison Adcock, 2022. "Hippocampal convergence during anticipatory midbrain activation promotes subsequent memory formation," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    4. Adrián Ponce-Alvarez & Biyu J He & Patric Hagmann & Gustavo Deco, 2015. "Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-26, August.
    5. Alexis T Baria & Brian Maniscalco & Biyu J He, 2017. "Initial-state-dependent, robust, transient neural dynamics encode conscious visual perception," PLOS Computational Biology, Public Library of Science, vol. 13(11), pages 1-29, November.

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