TY - JOUR
T1 - Towards Replication in Computational Cognitive Modeling
T2 - A Machine Learning Perspective
AU - Emmery, Chris
AU - Kádár, Ákos
AU - Wiltshire, Travis J
AU - Hendrickson, Andrew T
PY - 2019/7/31
Y1 - 2019/7/31
N2 - 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.
AB - 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.
KW - Reproducibility
KW - Machine Learning
KW - Cogntive modeling
KW - Cognitive psychology
UR - https://research.tilburguniversity.edu/en/publications/fe96bb90-135b-4206-ad5b-81ee09dbe498
U2 - 10.31234/osf.io/9y72b
DO - 10.31234/osf.io/9y72b
M3 - Review article
JO - Computational Brain & Behavior
JF - Computational Brain & Behavior
SN - 2522-0861
ER -