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Decision making on spatially continuous scales.

Roger Ratcliff1

  • 1The Ohio State University.

Psychological Review
|November 16, 2018
PubMed
Summary
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This study introduces a novel diffusion model for decision-making in continuous spaces, incorporating spatially continuous Gaussian noise. The model accurately predicts response distributions and times across various tasks and response modes.

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Mathematical Psychology

Background:

  • Decision-making models often simplify evidence accumulation.
  • Limited models capture truly continuous spatial processes in decision-making.

Purpose of the Study:

  • To present and test a new diffusion model for decision-making in continuous space.
  • To incorporate spatially continuous Gaussian noise into sequential sampling models.
  • To validate the model with diverse experimental data.

Main Methods:

  • Developed a sequential sampling diffusion model with continuous spatial evidence and Gaussian noise.
  • Accumulated evidence and noise towards a decision criterion (1D line or 2D plane).
  • Collected experimental data from tasks with coinciding and non-coinciding stimulus-response mapping and various response modes (eye, finger, mouse).

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Main Results:

  • The model successfully accounts for response distributions across positions.
  • The model accurately predicts response time distributions for different choices.
  • Demonstrated applicability to tasks involving direct spatial targeting and abstract identification.

Conclusions:

  • The novel diffusion model effectively represents decision-making in continuous spatial environments.
  • The inclusion of Gaussian process noise allows for modeling truly continuous spatial processes.
  • The model's broad applicability spans various tasks, response modes, and potential technological applications.