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A Method for Investigating Age-related Differences in the Functional Connectivity of Cognitive Control Networks Associated with Dimensional Change Card Sort Performance
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Validation and Comparison of Non-stationary Cognitive Models: A Diffusion Model Application.

Lukas Schumacher1, Martin Schnuerch2, Andreas Voss1

  • 1Institute of Psychology, Heidelberg University, Heidelberg, Germany.

Computational Brain & Behavior
|July 16, 2026
PubMed
Summary

Superstatistics provide a flexible framework for modeling non-stationary dynamics in cognitive processes. This study experimentally validates superstatistics, showing they accurately capture cognitive fluctuations during decision-making tasks.

Keywords:
Amortized Bayesian inferenceCognitive process modelsDecision-makingDynamics in cognitionSuperstatistics

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Area of Science:

  • Cognitive science
  • Computational neuroscience
  • Statistical modeling

Background:

  • Cognitive processes exhibit dynamic, non-stationary behavior across various timescales.
  • Superstatistics offer a novel framework to integrate these non-stationary dynamics into cognitive models.
  • Existing cognitive models often assume stationary parameters, limiting their ability to capture real-world cognitive fluctuations.

Purpose of the Study:

  • To experimentally validate the application of superstatistics in cognitive modeling.
  • To compare the performance of four non-stationary diffusion decision models.
  • To introduce advanced deep learning techniques for analyzing time-varying parameters in cognitive models.

Main Methods:

  • Designed a perceptual decision-making task with manipulated difficulty and speed-accuracy trade-offs.
  • Employed superstatistics to model non-stationary dynamics in decision-making.
  • Utilized deep learning for amortized Bayesian estimation and model comparison.
  • Assessed model fit by comparing inferred parameter trajectories with experimental manipulations.

Main Results:

  • Transition models integrating gradual and abrupt parameter shifts best fit the empirical data.
  • Inferred parameter trajectories closely mirrored the experimental manipulation sequences.
  • Posterior re-simulations confirmed the models' ability to reproduce key data patterns.
  • Validated superstatistics as a tool for capturing cognitive non-stationarity.

Conclusions:

  • The validated superstatistics framework accurately reflects actual changes in psychological constructs.
  • Non-stationary dynamics inferred from the models correspond to empirical task manipulations.
  • This experimental validation supports the broad adoption of superstatistics in cognitive science and related fields.