Hostname: page-component-f554764f5-nt87m Total loading time: 0 Render date: 2025-04-22T00:47:53.379Z Has data issue: false hasContentIssue false
  • English
  • Français

Challenges of meta-learning and rational analysis in large worlds

Published online by Cambridge University Press:  23 September 2024

Margherita Calderan Open the ORCID record for Margherita Calderan [Opens in a new window]  and
Antonino Visalli Open the ORCID record for Antonino Visalli [Opens in a new window]
Margherita Calderan
Affiliation:
Department of Developmental Psychology and Socialisation, University of Padova, Italy [email protected]
Antonino Visalli*
Affiliation:
IRCCS San Camillo Hospital, Venice, Italy [email protected]
*
*Corresponding author.
Get access Rights & Permissions [Opens in a new window]

Abstract

We challenge Binz et al.'s claim of meta-learned model superiority over Bayesian inference for large world problems. While comparing Bayesian priors to model-training decisions, we question meta-learning feature exclusivity. We assert no special justification for rational Bayesian solutions to large world problems, advocating exploring diverse theoretical frameworks beyond rational analysis of cognition for research advancement.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 47 , 2024 , e148
Check for updates
Copyright
Copyright © The Author(s), 2024. 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.)

Article purchase

Temporarily unavailable

References

Anderson, J. R. (1991). Is human cognition adaptive? Behavioral and Brain Sciences, 14(3), 471–485.CrossRefGoogle Scholar
Beaumont, M. A. (2010). Approximate Bayesian computation in evolution and ecology. Annual Review of Ecology, Evolution, and Systematics, 41, 379–406.CrossRefGoogle Scholar
Binmore, K. (2007). Rational decisions in large worlds. Annales d'Economie et de Statistique, 86, 25–41.CrossRefGoogle Scholar
Cranmer, K., Brehmer, J., & Louppe, G. (2020). The frontier of simulation-based inference. Proceedings of the National Academy of Sciences, 117(48), 30055–30062.CrossRefGoogle ScholarPubMed
Dimitrakakis, C., & Ortner, R. (2022). Decision making under uncertainty and reinforcement learning: Theory and algorithms (Vol. 223). Springer Nature.CrossRefGoogle Scholar
Friston, K. J., Da Costa, L., Sajid, N., Heins, C., Ueltzhöffer, K., Pavliotis, G. A., & Parr, T. (2023). The free energy principle made simpler but not too simple. Physics Reports, 1024, 1–29.CrossRefGoogle Scholar
Friston, K. J., & Stephan, K. E. (2007). Free-energy and the brain. Synthese, 159, 417–458.CrossRefGoogle ScholarPubMed
Grant, E., Finn, C., Levine, S., Darrell, T., & Griffiths, T. (2018). Recasting gradient-based meta-learning as hierarchical Bayes. arXiv preprint arXiv:1801.08930.Google Scholar
Griffiths, T. L., Callaway, F., Chang, M. B., Grant, E., Krueger, P. M., & Lieder, F. (2019). Doing more with less: Meta-reasoning and meta-learning in humans and machines. Current Opinion in Behavioral Sciences, 29, 24–30.CrossRefGoogle Scholar
Kemp, C., Perfors, A., & Tenenbaum, J. B. (2007). Learning overhypotheses with hierarchical Bayesian models. Developmental Science, 10(3), 307–321.CrossRefGoogle ScholarPubMed
Kochenderfer, M. J. (2015). Decision making under uncertainty: Theory and application. MIT press.CrossRefGoogle Scholar
Li, M. Y., Callaway, F., Thompson, W. D., Adams, R. P., & Griffiths, T. L. (2023). Learning to learn functions. Cognitive Science, 47(4), e13262.CrossRefGoogle ScholarPubMed
Lucas, C. G., Griffiths, T. L., Williams, J. J., & Kalish, M. L. (2015). A rational model of function learning. Psychonomic Bulletin & Review, 22(5), 1193–1215.CrossRefGoogle ScholarPubMed
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., & Lakshminarayanan, B. (2021). Normalizing flows for probabilistic modeling and inference. The Journal of Machine Learning Research, 22(1), 2617–2680.Google Scholar
Savage, L. J. (1972). The foundations of statistics. Courier Corporation.Google Scholar