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Decision-related activity in sensory neurons reflects more than a neuron’s causal effect

Author

Listed:
  • Hendrikje Nienborg

    (Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, 49 Convent Drive, Bethesda, Maryland 20892, USA)

  • Bruce G. Cumming

    (Laboratory of Sensorimotor Research, National Eye Institute, National Institutes of Health, 49 Convent Drive, Bethesda, Maryland 20892, USA)

Abstract

Neurons back to the future The ability of sensory neurons to predict an animal's upcoming decision has generated enormous interest over the past decade, and the impression has grown that the activity of these neurons in some way causes the appropriate decision to be made. Now in a study involving monkeys making choices in a video-based binocular-disparity discrimination task, Hendrikje Nienborg and Bruce Cumming show that this model is too simplistic. Their data reveal an opposite direction of causality: once a decision is made, the decision itself changes the responses of the sensory neurons. Deciding what one sees actively changes what is seen.

Suggested Citation

  • Hendrikje Nienborg & Bruce G. Cumming, 2009. "Decision-related activity in sensory neurons reflects more than a neuron’s causal effect," Nature, Nature, vol. 459(7243), pages 89-92, May.
    • Handle: RePEc:nat:nature:v:459:y:2009:i:7243:d:10.1038_nature07821
    DOI: 10.1038/nature07821
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    Cited by:

    1. João D. Semedo & Anna I. Jasper & Amin Zandvakili & Aravind Krishna & Amir Aschner & Christian K. Machens & Adam Kohn & Byron M. Yu, 2022. "Feedforward and feedback interactions between visual cortical areas use different population activity patterns," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    2. Adnan Rebei, 2019. "Entropic Decision Making," Papers 2001.00122, arXiv.org.
    3. Adrien Wohrer & Christian K Machens, 2015. "On the Number of Neurons and Time Scale of Integration Underlying the Formation of Percepts in the Brain," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-38, March.
    4. Kaushik J Lakshminarasimhan & Alexandre Pouget & Gregory C DeAngelis & Dora E Angelaki & Xaq Pitkow, 2018. "Inferring decoding strategies for multiple correlated neural populations," PLOS Computational Biology, Public Library of Science, vol. 14(9), pages 1-40, September.
    5. Shaiyan Keshvari & Ronald van den Berg & Wei Ji Ma, 2013. "No Evidence for an Item Limit in Change Detection," PLOS Computational Biology, Public Library of Science, vol. 9(2), pages 1-9, February.
    6. Richard D Lange & Ankani Chattoraj & Jeffrey M Beck & Jacob L Yates & Ralf M Haefner, 2021. "A confirmation bias in perceptual decision-making due to hierarchical approximate inference," PLOS Computational Biology, Public Library of Science, vol. 17(11), pages 1-30, November.
    7. Sebastian Bitzer & Jelle Bruineberg & Stefan J Kiebel, 2015. "A Bayesian Attractor Model for Perceptual Decision Making," PLOS Computational Biology, Public Library of Science, vol. 11(8), pages 1-35, August.
    8. Andrew M. Clark & David C. Bradley, 2022. "A neural correlate of perceptual segmentation in macaque middle temporal cortical area," Nature Communications, Nature, vol. 13(1), pages 1-16, December.

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