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A general mechanism for perceptual decision-making in the human brain

Author

Listed:
  • H. R. Heekeren

    (NIH)

  • S. Marrett

    (NIH)

  • P. A. Bandettini

    (NIH
    NIH)

  • L. G. Ungerleider

    (NIH)

Abstract

Findings from single-cell recording studies suggest that a comparison of the outputs of different pools of selectively tuned lower-level sensory neurons may be a general mechanism by which higher-level brain regions compute perceptual decisions. For example, when monkeys must decide whether a noisy field of dots is moving upward or downward, a decision can be formed by computing the difference in responses between lower-level neurons sensitive to upward motion and those sensitive to downward motion1,2,3,4. Here we use functional magnetic resonance imaging and a categorization task in which subjects decide whether an image presented is a face or a house to test whether a similar mechanism is also at work for more complex decisions in the human brain and, if so, where in the brain this computation might be performed. Activity within the left dorsolateral prefrontal cortex is greater during easy decisions than during difficult decisions, covaries with the difference signal between face- and house-selective regions in the ventral temporal cortex, and predicts behavioural performance in the categorization task. These findings show that even for complex object categories, the comparison of the outputs of different pools of selectively tuned neurons could be a general mechanism by which the human brain computes perceptual decisions.

Suggested Citation

  • H. R. Heekeren & S. Marrett & P. A. Bandettini & L. G. Ungerleider, 2004. "A general mechanism for perceptual decision-making in the human brain," Nature, Nature, vol. 431(7010), pages 859-862, October.
    • Handle: RePEc:nat:nature:v:431:y:2004:i:7010:d:10.1038_nature02966
    DOI: 10.1038/nature02966
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    Citations

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    Cited by:

    1. Isabelle Brocas & Juan D Carrillo, 2007. "Reason, Emotion, and Information Processing in the Brain," Levine's Working Paper Archive 122247000000001594, David K. Levine.
    2. Wang, Xinyi & Zhang, Xiyun & Zheng, Muhua & Xu, Leijun & Xu, Kesheng, 2023. "Noise-induced coexisting firing patterns in hybrid-synaptic interacting networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 615(C).
    3. Gabriele De Luca & Thomas J. Lampoltshammer & Shahanaz Parven & Johannes Scholz, 2022. "A Literature Review on the Usage of Agent-Based Modelling to Study Policies for Managing International Migration," Social Sciences, MDPI, vol. 11(8), pages 1-32, August.
    4. 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.
    5. Fisher, Geoffrey, 2021. "A multiattribute attentional drift diffusion model," Organizational Behavior and Human Decision Processes, Elsevier, vol. 165(C), pages 167-182.
    6. Konstantinos Tsetsos & Thomas Pfeffer & Pia Jentgens & Tobias H Donner, 2015. "Action Planning and the Timescale of Evidence Accumulation," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-21, June.
    7. Brocas, Isabelle & Carrillo, Juan D., 2012. "From perception to action: An economic model of brain processes," Games and Economic Behavior, Elsevier, vol. 75(1), pages 81-103.
    8. Michiel van Elk, 2015. "Perceptual Biases in Relation to Paranormal and Conspiracy Beliefs," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-15, June.
    9. Clithero, John A., 2018. "Improving out-of-sample predictions using response times and a model of the decision process," Journal of Economic Behavior & Organization, Elsevier, vol. 148(C), pages 344-375.
    10. Urszula Foryś & Natalia Z. Bielczyk & Katarzyna Piskała & Martyna Płomecka & Jan Poleszczuk, 2017. "Impact of Time Delay in Perceptual Decision-Making: Neuronal Population Modeling Approach," Complexity, Hindawi, vol. 2017, pages 1-14, September.

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