Tuning the speed-accuracy trade-off to maximize reward rate in multisensory decision-making

  1. Jan Drugowitsch  Is a corresponding author
  2. Gregory C DeAngelis
  3. Dora E Angelaki
  4. Alexandre Pouget
  1. University of Rochester, United States
  2. Baylor College of Medicine, United States

Abstract

For decisions made under time pressure, effective decision making based on uncertain or ambiguous evidence requires efficient accumulation of evidence over time, as well as appropriately balancing speed and accuracy, known as the speed/accuracy trade-off. For simple unimodal stimuli, previous studies have shown that human subjects set their speed/accuracy trade-off to maximize reward rate. We extend this analysis to situations in which information is provided by multiple sensory modalities. Analyzing previously collected data (J. Drugowitsch, DeAngelis, Klier, Angelaki, & Pouget, 2014), we show that human subjects adjust their speed/accuracy trade-off to produce near-optimal reward rates. This trade-off can change rapidly across trials according to the sensory modalities involved, suggesting that it is represented by neural population codes rather than implemented by slow neuronal mechanisms such as gradual changes in synaptic weights. Furthermore, we show that deviations from the optimal speed/accuracy trade-off can be explained by assuming an incomplete gradient-based learning of these trade-offs.

Article and author information

Author details

  1. Jan Drugowitsch

    Department of Brain and Cognitive Sciences, University of Rochester, Rochester, United States
    For correspondence
    jdrugo@gmail.com
    Competing interests
    The authors declare that no competing interests exist.
  2. Gregory C DeAngelis

    Department of Brain and Cognitive Sciences, University of Rochester, Rochester, United States
    Competing interests
    The authors declare that no competing interests exist.
  3. Dora E Angelaki

    Department of Neuroscience, Baylor College of Medicine, Houston, United States
    Competing interests
    The authors declare that no competing interests exist.
  4. Alexandre Pouget

    Department of Brain and Cognitive Sciences, University of Rochester, Rochester, United States
    Competing interests
    The authors declare that no competing interests exist.

Ethics

Human subjects: Human subjects: Informed consent was obtained from all participants and all procedures were reviewed and approved by the Washington University Office of Human Research Protections (OHRP), Institutional Review Board (IRB; IRB ID# 201109183). Consent to publish was not obtained in writing, as it was not required by the IRB, but all subjects were recruited for this purpose and approved verbally. Of the initial seven subjects, three participated in a follow-up experiment roughly 2 years after the initial data collection. Procedures for the follow-up experiment were approved by the Institutional Review Board for Human Subject Research for Baylor College of Medicine and Affiliated Hospitals (BCM IRB, ID# H-29411) and informed consent and consent to publish was given again by all three subjects

Copyright

© 2015, Drugowitsch et al.

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,438
    views
  • 532
    downloads
  • 58
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Jan Drugowitsch
  2. Gregory C DeAngelis
  3. Dora E Angelaki
  4. Alexandre Pouget
(2015)
Tuning the speed-accuracy trade-off to maximize reward rate in multisensory decision-making
eLife 4:e06678.
https://doi.org/10.7554/eLife.06678

Share this article

https://doi.org/10.7554/eLife.06678