IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1003348.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003348
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1003348&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1003348?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Anthony Randal McIntosh & Natasa Kovacevic & Roxane J Itier, 2008. "Increased Brain Signal Variability Accompanies Lower Behavioral Variability in Development," PLOS Computational Biology, Public Library of Science, vol. 4(7), pages 1-9, July.
    2. J. L. Vincent & G. H. Patel & M. D. Fox & A. Z. Snyder & J. T. Baker & D. C. Van Essen & J. M. Zempel & L. H. Snyder & M. Corbetta & M. E. Raichle, 2007. "Intrinsic functional architecture in the anaesthetized monkey brain," Nature, Nature, vol. 447(7140), pages 83-86, May.
    3. Leslie C. Osborne & Stephen G. Lisberger & William Bialek, 2005. "A sensory source for motor variation," Nature, Nature, vol. 437(7057), pages 412-416, September.
    4. Thilo Womelsdorf & Pascal Fries & Partha P. Mitra & Robert Desimone, 2006. "Gamma-band synchronization in visual cortex predicts speed of change detection," Nature, Nature, vol. 439(7077), pages 733-736, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joseph A Lombardo & Matthew V Macellaio & Bing Liu & Stephanie E Palmer & Leslie C Osborne, 2018. "State dependence of stimulus-induced variability tuning in macaque MT," PLOS Computational Biology, Public Library of Science, vol. 14(10), pages 1-28, October.
    2. Protachevicz, Paulo Ricardo & Borges, Fernando da Silva & Batista, Antonio Marcos & Baptista, Murilo da Silva & Caldas, Iberê Luiz & Macau, Elbert Einstein Nehrer & Lameu, Ewandson Luiz, 2023. "Plastic neural network with transmission delays promotes equivalence between function and structure," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    3. Laura Biagi & Sofia Allegra Crespi & Michela Tosetti & Maria Concetta Morrone, 2015. "BOLD Response Selective to Flow-Motion in Very Young Infants," PLOS Biology, Public Library of Science, vol. 13(9), pages 1-22, September.
    4. Jessie M H Szostakiwskyj & Stephanie E Willatt & Filomeno Cortese & Andrea B Protzner, 2017. "The modulation of EEG variability between internally- and externally-driven cognitive states varies with maturation and task performance," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-27, July.
    5. Adele Diederich & Annette Schomburg & Hans Colonius, 2012. "Saccadic Reaction Times to Audiovisual Stimuli Show Effects of Oscillatory Phase Reset," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-13, October.
    6. Huee Ru Chong & Yadollah Ranjbar-Slamloo & Malcolm Zheng Hao Ho & Xuan Ouyang & Tsukasa Kamigaki, 2023. "Functional alterations of the prefrontal circuit underlying cognitive aging in mice," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    7. Kenta Tominaga & André Lee & Eckart Altenmüller & Fumio Miyazaki & Shinichi Furuya, 2016. "Kinematic Origins of Motor Inconsistency in Expert Pianists," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-15, August.
    8. Alessandra Griffa & Mathieu Mach & Julien Dedelley & Daniel Gutierrez-Barragan & Alessandro Gozzi & Gilles Allali & Joanes Grandjean & Dimitri Ville & Enrico Amico, 2023. "Evidence for increased parallel information transmission in human brain networks compared to macaques and male mice," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    9. Caroline Haimerl & Douglas A. Ruff & Marlene R. Cohen & Cristina Savin & Eero P. Simoncelli, 2023. "Targeted V1 comodulation supports task-adaptive sensory decisions," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    10. Seth W. Egger & Stephen G. Lisberger, 2022. "Neural structure of a sensory decoder for motor control," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    11. Mazen El-Baba & Daniel J Lewis & Zhuo Fang & Adrian M Owen & Stuart M Fogel & J Bruce Morton, 2019. "Functional connectivity dynamics slow with descent from wakefulness to sleep," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-13, December.
    12. Nidhi Seethapathi & Barrett C. Clark & Manoj Srinivasan, 2024. "Exploration-based learning of a stabilizing controller predicts locomotor adaptation," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    13. Anandamohan Ghosh & Y Rho & A R McIntosh & R Kötter & V K Jirsa, 2008. "Noise during Rest Enables the Exploration of the Brain's Dynamic Repertoire," PLOS Computational Biology, Public Library of Science, vol. 4(10), pages 1-12, October.
    14. Dhanya Parameshwaran & Upinder S Bhalla, 2013. "Theta Frequency Background Tunes Transmission but Not Summation of Spiking Responses," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-12, January.
    15. David Florentino Montez & Finnegan J Calabro & Beatriz Luna, 2019. "Working memory improves developmentally as neural processes stabilize," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-15, March.
    16. Chaogan Yan & Dongqiang Liu & Yong He & Qihong Zou & Chaozhe Zhu & Xinian Zuo & Xiangyu Long & Yufeng Zang, 2009. "Spontaneous Brain Activity in the Default Mode Network Is Sensitive to Different Resting-State Conditions with Limited Cognitive Load," PLOS ONE, Public Library of Science, vol. 4(5), pages 1-11, May.
    17. Robert Leech & Gregory Scott & Robin Carhart-Harris & Federico Turkheimer & Simon D Taylor-Robinson & David J Sharp, 2014. "Spatial Dependencies between Large-Scale Brain Networks," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-10, June.
    18. Yoshiki Kaneoke & Tomohiro Donishi & Jun Iwatani & Satoshi Ukai & Kazuhiro Shinosaki & Masaki Terada, 2012. "Variance and Autocorrelation of the Spontaneous Slow Brain Activity," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-10, May.
    19. 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.
    20. Paolo Tommasino & Antonella Maselli & Domenico Campolo & Francesco Lacquaniti & Andrea d’Avella, 2021. "A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-32, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pcbi00:1003348. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ploscompbiol (email available below). General contact details of provider: https://journals.plos.org/ploscompbiol/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.