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Modelling human behaviour in cognitive tasks with latent dynamical systems.

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We developed task-DyVA, a deep learning model for analyzing response times in cognitive tasks. This framework captures individual differences and reveals a stability-flexibility trade-off underlying task-switching costs.

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Area of Science:

  • Cognitive psychology
  • Computational neuroscience
  • Machine learning

Background:

  • Response time data are crucial for understanding cognitive processes.
  • Existing models often rely on strong assumptions or are limited to single trials.

Purpose of the Study:

  • Introduce task-DyVA, a novel deep learning framework.
  • Model sequences of response times from individual subjects.
  • Discover interpretable cognitive theories from behavioral data.

Main Methods:

  • Utilized a deep learning framework (task-DyVA) with expressive dynamical systems.
  • Trained models on response time sequences from a task-switching dataset.
  • Employed perturbation experiments and latent dynamics analysis.

Main Results:

  • Captured subject-specific behavioral differences with high temporal precision.
  • Successfully modeled task-switching costs.
  • Found evidence for a stability-flexibility trade-off in cognitive control.

Conclusions:

  • task-DyVA provides a powerful tool for analyzing complex response time data.
  • The framework supports the discovery of dynamic, interpretable cognitive theories.
  • Revealed underlying mechanisms of cognitive flexibility and stability.