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An attentional drift diffusion model over binary-attribute choice.

Geoffrey Fisher1

  • 1Cornell University, Cornell SC Johnson College of Business and Dyson School of Applied Economics and Management, 340C Warren Hall, Ithaca, NY 14853, United States.

Cognition
|June 25, 2017
PubMed
Summary
This summary is machine-generated.

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This study introduces a new model for decision-making, the binary-attribute attentional drift diffusion model (baDDM), which accurately predicts choices and reaction times based on visual attention during complex decisions.

Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Decision Science

Background:

  • Effective decision-making requires integrating information about choice attributes.
  • Complex and rapid choices are influenced by contextual variables affecting attribute attention.
  • Existing models may not fully capture the dynamics of attention in binary choices.

Purpose of the Study:

  • To propose and validate the binary-attribute attentional drift diffusion model (baDDM).
  • To investigate how fluctuations in visual attention impact binary-attribute choice processes.
  • To quantitatively predict choice behavior and reaction times influenced by attention.

Main Methods:

  • Development of the binary-attribute attentional drift diffusion model (baDDM).
Keywords:
AttentionDrift diffusion modelMulti-attribute choicePreferencesSequential sampling models

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  • Conducting an eye-tracking experiment to record visual attention during accept-reject decisions.
  • Quantitative analysis correlating choices, reaction times, and attention patterns.

    • Main Results:

    • The baDDM accurately predicts choices and reaction times in binary-attribute decisions.
    • Eye-tracking data confirms the model's predictions regarding the influence of attention.
    • An attribute-based fixation bias was estimated, showing attention modulates attribute weights.

    Conclusions:

    • The baDDM provides a robust framework for understanding attention's role in binary choices.
    • Visual attention significantly influences the subjective weighting of attributes during decision-making.
    • This research offers insights into the cognitive mechanisms underlying rapid, attribute-based choices.