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Published on: May 2, 2019
Perceptual decisions between multiple directions of visual motion
Mamiko Niwa1, Jochen Ditterich
1Center for Neuroscience, University of California, Davis, Davis, California 95618, USA.
Summary
This study explores multialternative decision making by analyzing human choices among visual motion directions. A novel computational model explains decisions as a race between evidence accumulators.
Area of Science:
- Cognitive Neuroscience
- Computational Neuroscience
- Decision Neuroscience
Background:
- Perceptual decision making research has primarily focused on binary choices.
- Real-world decisions frequently involve multiple alternatives, necessitating research into these complex scenarios.
- Understanding the neural basis of multialternative decisions is crucial for a complete picture of cognition.
Purpose of the Study:
- To investigate the neural mechanisms underlying human perceptual decision making with multiple alternatives.
- To develop a quantitative dataset and a computational model for multialternative decision tasks.
- To explore how sensory evidence is processed when choosing among several options.
Main Methods:
- Human subjects performed perceptual decisions on visual motion directions using a multicomponent random-dot stimulus.
- Experimental control over the amount of sensory evidence provided for each alternative was implemented.
- A computational model based on a race between multiple integrators to a decision threshold was developed.
Main Results:
- A rich quantitative dataset for multialternative decision making was generated, covering various accuracy and response time levels.
- The developed computational model successfully explained the structure of the behavioral data.
- The model posits that decision-making involves multiple integrators accumulating sensory evidence for each choice.
Conclusions:
- The study provides a framework for understanding multialternative perceptual decisions.
- The race-to-threshold model offers a plausible mechanism for accumulating evidence across multiple options.
- This research advances our understanding of the neural computations involved in complex decision making.
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