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RELATING ACCUMULATOR MODEL PARAMETERS AND NEURAL DYNAMICS.

Braden A Purcell1, Thomas J Palmeri2

  • 1New York University.

Journal of Mathematical Psychology
|April 11, 2017
PubMed
Summary
This summary is machine-generated.

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Decision-making models use accumulator dynamics to infer cognitive mechanisms. Analyzing neural dynamics can help, but combining it with behavioral modeling offers the most comprehensive understanding of cognition.

Area of Science:

  • Cognitive science
  • Computational neuroscience
  • Decision-making research

Background:

  • Accumulator models are foundational for understanding decision-making, linking parameters to cognitive mechanisms like evidence accumulation rate and response thresholds.
  • Traditionally, cognitive mechanisms are inferred by fitting model parameters to observed behavior.
  • Recent advances link neural activity to evidence accumulation, suggesting neural dynamics could directly reveal cognitive mechanisms.

Purpose of the Study:

  • To investigate the extent to which decision-making mechanisms can be inferred from neural accumulator dynamics.
  • To understand the impact of noise on the relationship between accumulator dynamics and underlying cognitive mechanisms.
  • To evaluate the utility of analyzing neural dynamics for distinguishing between different decision-making models.

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Main Methods:

  • Simulated accumulator model dynamics were generated by systematically varying model parameters.
  • The relationship between parameterized mechanisms and observable dynamics was analyzed.
  • Model predictions for behavior and neural dynamics were compared across different parameter settings.

Main Results:

  • In some instances, distinct decision-making mechanisms could be inferred directly from simulated dynamics, even when behavioral predictions were identical.
  • However, different parameterized mechanisms sometimes produced highly similar dynamics, limiting direct inference.
  • Noise in evidence accumulation complicates the interpretation of neural dynamics relative to underlying mechanisms.

Conclusions:

  • Analyzing neural dynamics is a valuable tool for resolving ambiguity in behavioral models.
  • Inferences drawn solely from neural dynamics may be limited due to potential mimicry of dynamics by different mechanisms.
  • Simultaneous modeling of both behavior and neural dynamics provides the most robust approach for understanding decision-making and other cognitive processes.