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Related Experiment Videos

Evidence for time-variant decision making.

Jochen Ditterich1

  • 1Center for Neuroscience & Section of Neurobiology, Physiology and Behaviour, University of California, Davis, 1544 Newton Ct, Davis, CA 95616, USA. jditterich@ucdavis.edu

The European Journal of Neuroscience
|January 19, 2007
PubMed
Summary
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A time-variant diffusion model better explains perceptual decision-making speed and accuracy than simpler models. This dynamic model, incorporating increasing sensory signal gain, aligns with neural activity and optimizes performance by adjusting decisions during trials.

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Human decision-making is often modeled using diffusion processes where evidence accumulates over time.
  • Previous models struggled to explain response time distributions, especially for errors.

Purpose of the Study:

  • To evaluate if diffusion models explain perceptual decision speed and accuracy.
  • To investigate if these models account for neural activity in the parietal cortex (area LIP) during a random dot motion task.
  • To explore time-variant diffusion models for improved behavioral and neural data fit.

Main Methods:

  • Utilized a reaction-time random dot motion direction discrimination task.
  • Recorded neural activity from the parietal cortex (area LIP) of monkeys.

Related Experiment Videos

  • Compared a simple diffusion model with a time-variant diffusion model, specifically one with time-increasing sensory gain.

    • Main Results:

    • A simple diffusion model explained basic performance but failed on error response times and distributions.
    • A time-variant diffusion model successfully explained psychometric functions, mean response times, and response time distributions.
    • A diffusion process with time-increasing sensory gain best matched behavioral and neural data.

    Conclusions:

    • Time-variant diffusion models offer a more accurate account of perceptual decision-making.
    • Dynamic adjustments in sensory signal gain during a decision optimize performance and reward rate.
    • The brain dynamically adjusts speed-accuracy trade-offs during ongoing decisions, not just between trials.