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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Decision Theory

Background:

  • Integration-to-threshold models are foundational for understanding two-choice perceptual decision making in humans and animals.
  • Extending these models to multiple choices lacks a consensus normative framework, limiting exploration of threshold characteristics.

Purpose of the Study:

  • To develop a normative framework for multiple-choice decision making.
  • To characterize optimal decision boundaries and thresholds in multi-choice scenarios.
  • To inform the debate on stationary versus dynamic decision boundaries.

Main Methods:

  • Utilized sequential Bayesian inference.
  • Conceptualized decision making as an n-dimensional particle diffusion process.
  • Employed simulations within a parameterized subset of time-independent boundaries.

Main Results:

  • Identified optimal decision boundaries as a degenerate family of nonlinear structures.
  • Demonstrated that these boundaries depend on multiple accumulator states and speed-accuracy trade-offs.
  • Showed that optimal boundaries support both stationary and collapsing thresholds.

Conclusions:

  • Proposed a normative theory for multiple-choice decision making.
  • Characterized optimal decision thresholds as nonlinear and state-dependent.
  • Provided insights into the conditions under which stationary or dynamic thresholds are optimal.