IDEAS home Printed from https://ideas.repec.org/a/plo/pcbi00/1004442.html
   My bibliography  Save this article

A Bayesian Attractor Model for Perceptual Decision Making

Author

Listed:
  • Sebastian Bitzer
  • Jelle Bruineberg
  • Stefan J Kiebel

Abstract

Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks.Author Summary: How do we decide whether a traffic light signals stop or go? Perceptual decision making research investigates how the brain can make these simple but fundamentally important decisions. Current consensus states that the brain solves this task simply by accumulating sensory information over time to make a decision once enough information has been collected. However, there are important, open questions on how exactly this accumulation mechanism operates. For example, recent experimental evidence suggests that the sensory processing receives feedback about the ongoing decision making while standard models typically do not assume such feedback. It is also an open question how people compute their confidence about their decisions. Furthermore, current decision making models usually consider only a single decision and stop modelling once this decision has been made. However, in our natural environment, people change their decisions, for example when a traffic light changes from green to red. Here, we show that one can explain these three aspects of decision making by combining two already existing modelling techniques. This resulting new model can be used to derive novel testable predictions of how the brain makes perceptual decisions.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pcbi00:1004442
    DOI: 10.1371/journal.pcbi.1004442
    as

    Download full text from publisher

    File URL: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004442
    Download Restriction: no

    File URL: https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004442&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pcbi.1004442?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. Tirin Moore & Katherine M. Armstrong, 2003. "Selective gating of visual signals by microstimulation of frontal cortex," Nature, Nature, vol. 421(6921), pages 370-373, January.
    2. Arbora Resulaj & Roozbeh Kiani & Daniel M. Wolpert & Michael N. Shadlen, 2009. "Changes of mind in decision-making," Nature, Nature, vol. 461(7261), pages 263-266, September.
    3. Adam Kepecs & Naoshige Uchida & Hatim A. Zariwala & Zachary F. Mainen, 2008. "Neural correlates, computation and behavioural impact of decision confidence," Nature, Nature, vol. 455(7210), pages 227-231, September.
    4. Robert Legenstein & Wolfgang Maass, 2014. "Ensembles of Spiking Neurons with Noise Support Optimal Probabilistic Inference in a Dynamically Changing Environment," PLOS Computational Biology, Public Library of Science, vol. 10(10), pages 1-27, October.
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    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. Manuel Rausch & Michael Zehetleitner, 2019. "The folded X-pattern is not necessarily a statistical signature of decision confidence," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-18, October.
    2. Andrea Insabato & Mario Pannunzi & Gustavo Deco, 2017. "Multiple Choice Neurodynamical Model of the Uncertain Option Task," PLOS Computational Biology, Public Library of Science, vol. 13(1), pages 1-29, January.
    3. Marina Martinez-Garcia & Andrea Insabato & Mario Pannunzi & Jose L Pardo-Vazquez & Carlos Acuña & Gustavo Deco, 2015. "The Encoding of Decision Difficulty and Movement Time in the Primate Premotor Cortex," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-25, November.
    4. Florent Meyniel & Daniel Schlunegger & Stanislas Dehaene, 2015. "The Sense of Confidence during Probabilistic Learning: A Normative Account," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-25, June.
    5. 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.
    6. Micha Heilbron & Florent Meyniel, 2019. "Confidence resets reveal hierarchical adaptive learning in humans," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-24, April.
    7. Zohar Z Bronfman & Noam Brezis & Marius Usher, 2016. "Non-monotonic Temporal-Weighting Indicates a Dynamically Modulated Evidence-Integration Mechanism," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-21, February.
    8. Leopold Zizlsperger & Thomas Sauvigny & Thomas Haarmeier, 2012. "Selective Attention Increases Choice Certainty in Human Decision Making," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    9. Zhaoran Zhang & Edward Zagha, 2023. "Motor cortex gates distractor stimulus encoding in sensory cortex," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    10. 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.
    11. 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.
    12. Thomas Miconi & Rufin VanRullen, 2016. "A Feedback Model of Attention Explains the Diverse Effects of Attention on Neural Firing Rates and Receptive Field Structure," PLOS Computational Biology, Public Library of Science, vol. 12(2), pages 1-18, February.
    13. Adrian M Haith & David M Huberdeau & John W Krakauer, 2015. "Hedging Your Bets: Intermediate Movements as Optimal Behavior in the Context of an Incomplete Decision," PLOS Computational Biology, Public Library of Science, vol. 11(3), pages 1-21, March.
    14. Laurence Aitchison & Dan Bang & Bahador Bahrami & Peter E Latham, 2015. "Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making," PLOS Computational Biology, Public Library of Science, vol. 11(10), pages 1-23, October.
    15. Ronald H Stevens & Trysha L Galloway, 2022. "Can machine learning be used to forecast the future uncertainty of military teams?," The Journal of Defense Modeling and Simulation, , vol. 19(2), pages 145-158, April.
    16. Wan-Yu Shih & Hsiang-Yu Yu & Cheng-Chia Lee & Chien-Chen Chou & Chien Chen & Paul W. Glimcher & Shih-Wei Wu, 2023. "Electrophysiological population dynamics reveal context dependencies during decision making in human frontal cortex," Nature Communications, Nature, vol. 14(1), pages 1-24, December.
    17. 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.
    18. Florent Meyniel, 2020. "Brain dynamics for confidence-weighted learning," PLOS Computational Biology, Public Library of Science, vol. 16(6), pages 1-27, June.
    19. Baiwei Liu & Anna C. Nobre & Freek van Ede, 2022. "Functional but not obligatory link between microsaccades and neural modulation by covert spatial attention," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    20. Carrillo, Juan & Brocas, Isabelle, 2007. "Reason, Emotion and Information Processing in the Brain," CEPR Discussion Papers 6535, C.E.P.R. Discussion Papers.

    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:pcbi00:1004442. 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.