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Related Experiment Videos

Computational approaches to visual decision making.

Jochen Ditterich1

  • 1Center for Neuroscience, University of California, 1544 Newton Ct, Davis, CA 95616, USA.

Novartis Foundation Symposium
|May 3, 2006
PubMed
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This summary is machine-generated.

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Diffusion models with an urgency mechanism accurately explain perceptual decision-making speed and accuracy. This model, incorporating time-variant sensory gain, aligns with neural activity in monkeys and optimizes reward rate.

Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Decision Science

Background:

  • Diffusion models are used to study decision-making.
  • Their ability to explain perceptual decision speed and accuracy is under investigation.

Purpose of the Study:

  • To test if diffusion models explain perceptual decision speed and accuracy.
  • To investigate if these models explain neural activity in the parietal cortex (area LIP) during decision-making.
  • To explore the role of an urgency mechanism in decision models.

Main Methods:

  • A reaction-time random dot motion direction-discrimination task was used.
  • Computational diffusion models were applied to behavioral data.
  • Neural activity from monkey area LIP was recorded and compared to model predictions.

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Main Results:

  • A simple diffusion model explained psychometric functions and mean response times but not response time distributions.
  • An "urgency mechanism" added to the diffusion model explained response time distributions.
  • A diffusion process with time-variant sensory gain best matched physiological data and model predictions.

Conclusions:

  • Diffusion models with an urgency mechanism, specifically time-variant sensory gain, accurately capture perceptual decision-making.
  • This model explains both behavioral data and neural activity in area LIP.
  • The time-variant decision process optimizes reward rate under trial abortion risk.