Statistical learning subserves a higher purpose : novelty detection in an information foraging system
- Author
- Ram Frost, Louisa Bogaerts (UGent) , Arthur G. Samuel, James S. Magnuson, Lori L. Holt and Morten H. Christiansen
- Organization
- Project
- Abstract
- Statistical learning (SL) is typically assumed to be a core mechanism by which organisms learn covarying structures and recurrent patterns in the environment, with the main purpose of facilitating processing of expected events. Within this theoretical framework, the environment is viewed as relatively stable, and SL "captures" the regularities therein through implicit unsupervised learning by mere exposure. Focusing primarily on language-the domain in which SL theory has been most influential-we review evidence that the environment is far from fixed: It is dynamic, in continual flux, and learners are far from passive absorbers of regularities; they interact with their environments, thereby selecting and even altering the patterns they learn from. We therefore argue for an alternative cognitive architecture, where SL serves as a subcomponent of an information foraging (IF) system. IF aims to detect and assimilate novel recurrent patterns in the input that deviate from randomness, for which SL supplies a baseline. The broad implications of this viewpoint and their relevance to recent debates in cognitive neuroscience are discussed.
- Keywords
- statistical learning, language, recurrent regularities, information foraging, novelty detection, UNIFORM WORD-LENGTH, PHONOTACTIC CONSTRAINTS, SPEECH-PERCEPTION, CURIOSITY, EXPECTATION, EXPERIENCE, ADAPTATION, NETWORKS, REPRESENTATIONS, CONVERSATION
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-01JN3QCVWX4FQVABYWAZNYZ7GS
- MLA
- Frost, Ram, et al. “Statistical Learning Subserves a Higher Purpose : Novelty Detection in an Information Foraging System.” PSYCHOLOGICAL REVIEW, 2025, doi:10.1037/rev0000547.
- APA
- Frost, R., Bogaerts, L., Samuel, A. G., Magnuson, J. S., Holt, L. L., & Christiansen, M. H. (2025). Statistical learning subserves a higher purpose : novelty detection in an information foraging system. PSYCHOLOGICAL REVIEW. https://doi.org/10.1037/rev0000547
- Chicago author-date
- Frost, Ram, Louisa Bogaerts, Arthur G. Samuel, James S. Magnuson, Lori L. Holt, and Morten H. Christiansen. 2025. “Statistical Learning Subserves a Higher Purpose : Novelty Detection in an Information Foraging System.” PSYCHOLOGICAL REVIEW. https://doi.org/10.1037/rev0000547.
- Chicago author-date (all authors)
- Frost, Ram, Louisa Bogaerts, Arthur G. Samuel, James S. Magnuson, Lori L. Holt, and Morten H. Christiansen. 2025. “Statistical Learning Subserves a Higher Purpose : Novelty Detection in an Information Foraging System.” PSYCHOLOGICAL REVIEW. doi:10.1037/rev0000547.
- Vancouver
- 1.Frost R, Bogaerts L, Samuel AG, Magnuson JS, Holt LL, Christiansen MH. Statistical learning subserves a higher purpose : novelty detection in an information foraging system. PSYCHOLOGICAL REVIEW. 2025;
- IEEE
- [1]R. Frost, L. Bogaerts, A. G. Samuel, J. S. Magnuson, L. L. Holt, and M. H. Christiansen, “Statistical learning subserves a higher purpose : novelty detection in an information foraging system,” PSYCHOLOGICAL REVIEW, 2025.
@article{01JN3QCVWX4FQVABYWAZNYZ7GS,
abstract = {{Statistical learning (SL) is typically assumed to be a core mechanism by which organisms learn covarying structures and recurrent patterns in the environment, with the main purpose of facilitating processing of expected events. Within this theoretical framework, the environment is viewed as relatively stable, and SL "captures" the regularities therein through implicit unsupervised learning by mere exposure. Focusing primarily on language-the domain in which SL theory has been most influential-we review evidence that the environment is far from fixed: It is dynamic, in continual flux, and learners are far from passive absorbers of regularities; they interact with their environments, thereby selecting and even altering the patterns they learn from. We therefore argue for an alternative cognitive architecture, where SL serves as a subcomponent of an information foraging (IF) system. IF aims to detect and assimilate novel recurrent patterns in the input that deviate from randomness, for which SL supplies a baseline. The broad implications of this viewpoint and their relevance to recent debates in cognitive neuroscience are discussed.}},
author = {{Frost, Ram and Bogaerts, Louisa and Samuel, Arthur G. and Magnuson, James S. and Holt, Lori L. and Christiansen, Morten H.}},
issn = {{0033-295X}},
journal = {{PSYCHOLOGICAL REVIEW}},
keywords = {{statistical learning,language,recurrent regularities,information foraging,novelty detection,UNIFORM WORD-LENGTH,PHONOTACTIC CONSTRAINTS,SPEECH-PERCEPTION,CURIOSITY,EXPECTATION,EXPERIENCE,ADAPTATION,NETWORKS,REPRESENTATIONS,CONVERSATION}},
language = {{eng}},
title = {{Statistical learning subserves a higher purpose : novelty detection in an information foraging system}},
url = {{http://doi.org/10.1037/rev0000547}},
year = {{2025}},
}
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