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A diffusion model with collapsing decision boundaries does not better explain human decision-making data than a fixed boundary model. Fixed boundaries provided a superior account across multiple discrimination tasks.

Keywords:
Collapsing boundariesDiffusion modelModel selectionResponse time models

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Area of Science:

  • Cognitive psychology
  • Computational neuroscience
  • Decision science

Background:

  • Previous research in animal models suggested decision boundaries collapse over time.
  • This has led to the development of diffusion models incorporating collapsing boundaries.

Purpose of the Study:

  • To investigate if a diffusion model with collapsing boundaries better explains human decision-making data compared to a fixed boundary model.
  • To compare model performance using data from numerosity and motion discrimination experiments.

Main Methods:

  • Utilized data from six experiments (four numerosity, two motion discrimination).
  • Employed two model selection criteria: Bayesian Information Criterion (BIC) and parametric bootstrap cross-fitting method (PBCM).

Main Results:

  • The fixed boundary model was preferred by BIC in all experiments.
  • PBCM favored fixed or negligibly collapsing boundaries in 5 out of 6 experiments.
  • Collapsing boundary model parameters were poorly identified and difficult to recover; boundary estimates were similar to the fixed model.

Conclusions:

  • A diffusion model with collapsing decision boundaries does not offer an improvement over the standard fixed boundary diffusion model for the tested human tasks.
  • The findings challenge the necessity of collapsing boundaries in explaining human perceptual decision-making in these contexts.