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Model Virtues in Computational Cognitive Neuroscience.

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A lack of criteria for evaluating computational models in cognitive neuroscience is addressed by identifying six key "model virtues." These virtues, including empirical accuracy and plausibility, are interrelated and context-dependent for modelers.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Modeling

Background:

  • Computational models are abundant in cognitive neuroscience.
  • A clear framework for evaluating these models is lacking.
  • This study addresses the need for criteria to assess model quality.

Purpose of the Study:

  • To identify and examine desirable qualities (virtues) of computational models.
  • To investigate the interrelationships between these virtues.
  • To provide a framework for evaluating computational models in cognitive control.

Main Methods:

  • Focused on the field of cognitive control.
  • Identified six key model virtues: empirical accuracy, empirical scope, functional analysis, causal detail, biological plausibility, and psychological plausibility.
  • Analyzed published Stroop modeling work and conducted qualitative interviews with expert modelers.

Main Results:

  • Model virtues are interrelated.
  • The perceived value of each virtue is dependent on the modeler's specific goals.
  • These interdependencies and goal-dependencies are often not explicitly stated in the literature.

Conclusions:

  • Researchers should be more explicit about their modeling choices and their justifications.
  • The identified virtues offer a starting point for a more rigorous evaluation framework.
  • The findings aim to stimulate discussion on model evaluation across cognitive neuroscience.