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Assessing the practical differences between model selection methods in inferences about choice response time tasks.

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  • 1Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands. nathan.j.evans@uon.edu.au.

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Summary
This summary is machine-generated.

Comparing model selection methods for evidence accumulation models (EAMs) reveals that simpler methods like AIC and BIC align with more complex ones for the linear ballistic accumulator (LBA). Researchers can confidently use simpler techniques for LBA analysis but must justify their chosen model selection class.

Keywords:
Bayes factorsDecision-makingModel selectionPredictive accuracyResponse time modeling

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Area of Science:

  • Cognitive Psychology
  • Computational Neuroscience
  • Decision Science

Background:

  • Evidence accumulation models (EAMs) are crucial for understanding rapid decision-making by analyzing response time distributions.
  • Selecting between competing theoretical accounts within EAMs is challenging due to model flexibility and the lack of a universally optimal selection method.
  • Existing model selection techniques vary in computational feasibility and theoretical grounding.

Purpose of the Study:

  • To systematically compare nine different model selection methods for EAMs.
  • To evaluate the performance of these methods using the linear ballistic accumulator (LBA) model in both simulation and empirical data.
  • To determine the consistency of inferences across different classes of model selection criteria.

Main Methods:

  • Conducted a large-scale simulation study using the linear ballistic accumulator (LBA) model.
  • Applied nine distinct model selection methods to simulated and empirical data from Dutilh et al. (2018).
  • Categorized methods into 'predictive accuracy' (AIC, DIC, WAIC) and 'Bayes factor' (BIC, Bayes factors) classes.

Main Results:

  • Predictive accuracy and Bayes factor classes of methods yielded divergent inferences in numerous cases.
  • Simpler methods, such as the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), showed high consistency with their more complex counterparts.
  • The choice of model selection class significantly impacts the inferences drawn from EAMs.

Conclusions:

  • Researchers can confidently employ simpler parameter-counting methods like AIC and BIC for the LBA model.
  • Careful consideration and justification of the chosen model selection method class are essential due to potential differences in outcomes.
  • The study highlights the importance of understanding the implications of different model selection strategies in cognitive modeling.