Towards Replication in Computational Cognitive Modeling: A Machine Learning Perspective
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This commentary highlights parallels between proposed cognitive modeling practices and machine learning reproducibility efforts, discussing overlapping practices and associated challenges like open science adoption and scalability.
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Abstract
The suggestions proposed by Lee et al. to improve cognitive modeling practices have significant parallels to the current best practices for improving reproducibility in the field of Machine Learning. In the current commentary on `Robust modeling in cognitive science', we highlight the practices that overlap and discuss how similar proposals have produced novel ongoing challenges, including cultural change towards open science, the scalability and interpretability of required practices, and the downstream effects of having robust practices that are fully transparent. Through this, we hope to inform future practices in computational modeling work with a broader scope.
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