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Taming the beast: extracting generalizable knowledge from computational models of cognition
Matthew R Nassar1, Michael J Frank1
1Department of Cognitive, Linguistic and Psychological Sciences, Brown Institute for Brain Science, Brown University, Providence RI 02912-1821.
Current Opinion in Behavioral Sciences
|August 31, 2016
Summary
Computational modeling aids theory building in cognitive science and neuroscience. However, fixed assumptions in models can bias results, necessitating careful interpretation strategies to avoid misleading data analysis.
Area of Science:
- Cognitive Science
- Computational Neuroscience
Background:
- Generalizing knowledge from experimental data necessitates robust theories.
- Computational modeling provides quantitative methods for theory generation and testing in cognition and neural processing.
Purpose of the Study:
- To discuss the challenges and potential biases in interpreting experimental data through computational models.
- To present strategies for avoiding misinterpretation in model-based data analysis.
Main Methods:
- Review of theoretical issues in computational modeling.
- Discussion of strategies to mitigate bias in model-based analyses.
Main Results:
- Model-based analyses are contingent on underlying assumptions, which can lead to biased interpretations if not met.
- New methods are emerging to minimize the negative impact of model-based biases.
Conclusions:
- Careful consideration of model assumptions is crucial for accurate interpretation of experimental data.
- Implementing strategies to avoid misinterpretation enhances the reliability of computational modeling in cognitive and neural sciences.
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