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Value-based decision making via sequential sampling with hierarchical competition and attentional modulation.

Jaron T Colas1

  • 1Computation and Neural Systems Program, California Institute of Technology, Pasadena, CA, United States of America.

Plos One
|October 28, 2017
PubMed
Summary
This summary is machine-generated.

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Neurally plausible computational models better capture human value-based decision-making than traditional models. A novel model incorporating competition and attention accurately predicts choices and reaction times.

Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Decision Science

Background:

  • Formal dynamical models can explain perceptual and value-based decision-making.
  • Sequential-sampling models (e.g., race, drift-diffusion) are popular but may lack neural feasibility.
  • Connectionist models offer a bridge between mental phenomena and neurophysiology.

Purpose of the Study:

  • To test neurally plausible connectionist models against traditional models for value-based decision-making.
  • To develop and validate a novel computational model for human preferential choices.
  • To analyze the implications for neurophysiological data analysis.

Main Methods:

  • Empirical testing of various computational models with human food choice data.
  • Formulation of a novel six-parameter computational model with mutual inhibition and attentional modulation.

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  • Meta-analysis of related experiments to validate model predictions.

    • Main Results:

    • Neurally plausible models quantitatively and qualitatively outperformed normative models in emulating behavior.
    • The novel six-parameter model demonstrated strong predictive power for value-based choices.
    • Meta-analysis confirmed the robustness of model-predicted trends in human decision-making and reaction times.

    Conclusions:

    • Connectionist models offer superior explanations for value-based decision-making compared to simpler, normative models.
    • The developed computational model provides a more realistic framework for understanding neural computations in choice.
    • Findings support the integration of computational modeling with neurophysiological data analysis.