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GLAMbox: A Python toolbox for investigating the association between gaze allocation and decision behaviour.

Felix Molter1,2,3,4, Armin W Thomas2,3,5,6, Hauke R Heekeren2,3

  • 1WZB Berlin Social Science Center, Berlin, Germany.

Plos One
|December 17, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces GLAMbox, a Python toolbox for analyzing how eye movements (gaze allocation) influence decision-making speed. It enables individual-level analysis of gaze-dependent evidence accumulation in complex choices.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Decision Science

Background:

  • Gaze allocation significantly impacts decision-making speed and choice probability.
  • Individual differences in gaze-choice associations are substantial but understudied.
  • Existing decision models struggle to characterize gaze-choice associations, especially for multi-alternative choices.

Purpose of the Study:

  • To present GLAMbox, a Python toolbox for applying the gaze-weighted linear accumulator (GLAM) model.
  • To facilitate straightforward, individual-level analysis of gaze-choice associations.
  • To extend decision modeling to complex choice scenarios with more than two alternatives.

Main Methods:

  • Developed GLAMbox using Python and PyMC3 for Bayesian parameter estimation.
  • Implemented the gaze-weighted linear accumulator (GLAM) model.
  • Enabled analysis for individual, pooled, or hierarchical models.

Main Results:

  • GLAMbox allows easy application of the GLAM model to experimental choice data.
  • The toolbox facilitates Bayesian parameter estimation for individual differences.
  • It provides tools for predicting choice behavior and visualizing data.

Conclusions:

  • GLAMbox simplifies the analysis of gaze-dependent evidence accumulation in multi-alternative decision tasks.
  • The toolbox supports individual-level characterization of the gaze-choice association.
  • It enhances the study of decision-making by integrating gaze data into computational models.