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Decision confidence and uncertainty in diffusion models with partially correlated neuronal integrators
1Department of Brain and Cognitive Sciences, University of Rochester, Rochester, NY 14627, USA. rmoreno@bcs.rochester.edu
Neural Computation
|February 10, 2010
Summary
This study introduces a new model for decision-making that naturally incorporates confidence. The model uses correlated neuronal integrators to explain how confidence relates to decision variables and time.
Area of Science:
- Cognitive Neuroscience
- Computational Neuroscience
- Decision Science
Background:
- Diffusion models are widely used for reaction times in human decision-making.
- Evaluating decision confidence within these models remains a challenge.
Purpose of the Study:
- Introduce a novel class of diffusion models to naturally incorporate decision confidence.
- Analytically describe the dependence of confidence on various decision parameters.
Main Methods:
- Developed a broader model class with two partially correlated neuronal integrators.
- Incorporated arbitrarily time-varying decision boundaries.
- Analytically derived confidence dependence and computed marginal confidence for specific correlation cases.
Main Results:
- The new model provides a natural framework for describing decision confidence.
- Decision confidence depends on the losing integrator's state, decision time, boundary dynamics, and neural correlations.
- Marginal confidence was computed for the half-anticorrelated case and compared to independent and fully anticorrelated scenarios.
Conclusions:
- The proposed model extends diffusion theory to include decision confidence.
- This framework offers new insights into the neural mechanisms underlying confidence in decision-making.
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