Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-26T15:30:35.354Z Has data issue: false hasContentIssue false
  • English
  • Français

Publishing fast and slow: A path toward generalizability in psychology and AI

Published online by Cambridge University Press:  10 February 2022

Andrew K. Lampinen Open the ORCID record for Andrew K. Lampinen [Opens in a new window] ,
Stephanie C. Y. Chan ,
Adam Santoro  and
Felix Hill
Andrew K. Lampinen
Affiliation:
DeepMind, LondonN1C 4DN, UK. lampinen@google.com scychan@google.com adamsantoro@google.com felixhill@google.com
Stephanie C. Y. Chan
Affiliation:
DeepMind, LondonN1C 4DN, UK. lampinen@google.com scychan@google.com adamsantoro@google.com felixhill@google.com
Adam Santoro
Affiliation:
DeepMind, LondonN1C 4DN, UK. lampinen@google.com scychan@google.com adamsantoro@google.com felixhill@google.com
Felix Hill
Affiliation:
DeepMind, LondonN1C 4DN, UK. lampinen@google.com scychan@google.com adamsantoro@google.com felixhill@google.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Artificial intelligence (AI) shares many generalizability challenges with psychology. But the fields publish differently. AI publishes fast, through rapid preprint sharing and conference publications. Psychology publishes more slowly, but creates integrative reviews and meta-analyses. We discuss the complementary advantages of each strategy, and suggest that incorporating both types of strategies could lead to more generalizable research in both fields.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 45 , 2022 , e26
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Bouthillier, X., Delaunay, P., Bronzi, M., Trofimov, A., Nichyporuk, B., Szeto, J., … Vincent, P. (2021). Accounting for variance in machine learning benchmarks. arXiv preprint arxiv:2103.03098.Google Scholar
Grootswagers, T., & Robinson, A. K. (2021). Overfitting the literature to one set of stimuli and data. arXiv preprint arXiv:2102.09729.Google Scholar
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., & Meger, D. (2018). Deep reinforcement learning that matters. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (pp. 3207–3214). February 2–7, 2108. New Orleans, Louisiana.Google Scholar
Hermann, K. L., Chen, T., & Kornblith, S. (2020). The origins and prevalence of texture bias in convolutional neural networks. Advances in Neural Information Processing Systems.Google Scholar
Hill, F., Lampinen, A., Schneider, R., Clark, S., Botvinick, M., McClelland, J. L., & Santoro, A. (2020). Environmental drivers of systematicity and generalization in a situated agent. In International Conference on Learning Representations. Retrieved from https://openreview.net/pdf?id=SklGryBtwr.Google Scholar
McShane, B. B., & Böckenholt, U. (2017). Single-paper meta-analysis: Benefits for study summary, theory testing, and replicability. Journal of Consumer Research, 43(6), 1048–1063.Google Scholar
Weinberger, K. (2020). On the importance of deconstruction. Presentation, NeurIPS 2020 Retrospective Workshop.Google Scholar