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Connecting process models to response times through Bayesian hierarchical regression analysis.

Thea Behrens1,2, Adrian Kühn1,2, Frank Jäkel3,4

  • 1Institute of Psychology, Technical University of Darmstadt, Darmstadt, Germany.

Behavior Research Methods
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Summary
This summary is machine-generated.

This study introduces a novel method to analyze cognitive process models using response-time data. By estimating elementary information processing (EIP) step durations, researchers can gain psychological insights into task performance.

Keywords:
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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Psychometrics

Background:

  • Process models are crucial for understanding cognitive tasks by detailing mental operations.
  • Analyzing response-time data offers insights into the speed and nature of these operations.
  • Current methods may lack precise psychological interpretations of model parameters.

Purpose of the Study:

  • To demonstrate a method for analyzing response-time data using process models.
  • To obtain parameter estimates with clear psychological interpretations.
  • To estimate the duration of elementary information processing (EIP) steps.

Main Methods:

  • Utilizing process models that generate counts of EIP steps per trial.
  • Modeling EIP step durations as random variables from a gamma distribution.
  • Employing Bayesian hierarchical models and probabilistic programming for data analysis.

Main Results:

  • Response time spread naturally increases with higher EIP step counts.
  • Successfully estimated EIP step durations for individual participants.
  • Applied the method to children's addition tasks and Sudoku response times, handling latent EIP counts.

Conclusions:

  • The proposed approach bridges cognitive modeling and statistical inference.
  • This method provides a flexible framework for analyzing diverse cognitive tasks.
  • The approach is expected to be widely applicable across various research domains.