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Reasoning Goals and Representational Decisions in Computational Cognitive Neuroscience: Lessons From the Drift
Ari Khoudary1,2,3, Megan A K Peters1,2,3,4,5, Aaron M Bornstein1,2,3
1Department of Cognitive Sciences, University of California, Irvine, Irvine, California, USA.
Researchers need a philosophical toolkit to understand computational cognitive models, like the drift diffusion model (DDM). This toolkit helps interpret models based on user goals, resolving debates about decision thresholds.
Area of Science:
- Cognitive Neuroscience
- Philosophy of Science
- Computational Psychiatry
Background:
- Computational cognitive models enhance rigor in cognitive neuroscience.
- Understanding model application and interpretation is crucial for researchers.
- A philosophical approach is needed to address meta-scientific challenges.
Purpose of the Study:
- Develop a philosophical toolkit for computational cognitive models.
- Clarify the role of user reasoning goals in model application.
- Analyze the drift diffusion model (DDM) and decision threshold debates.
Main Methods:
- Philosophical analysis of modeling practices.
- Conceptual analysis of the drift diffusion model (DDM).
- Examination of user reasoning goals and model development communities.
Main Results:
- Two DDM structures offer complementary explanations for speeded choice behavior.
- Model explanations (parsimonious, normative) reflect user communities (psychometricians, decision scientists).
- A heuristic is provided for selecting appropriate DDM structures.
Conclusions:
- Philosophy provides a framework for understanding and applying computational cognitive models.
- User reasoning goals are central to model interpretation and application.
- This work resolves debates on DDM decision thresholds by highlighting complementary explanations.
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