Simplicity: a unifying principle in cognitive science?
Abstract
Much of perception, learning and high-level cognition involves finding patterns in data. But there are always infinitely many patterns compatible with any finite amount of data. How does the cognitive system choose 'sensible' patterns? A long tradition in epistemology, philosophy of science, and mathematical and computational theories of learning argues that patterns 'should' be chosen according to how simply they explain the data. This article reviews research exploring the idea that simplicity drives a wide range of cognitive processes. We outline mathematical theory, computational results and empirical data that underpin this viewpoint.
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Fonseca, A., & Rodrigues, S. E. (2017) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2017.09.001 · Cited by 59 · see citing works
The Computational Origin of Representation
Piantadosi, S. T. (2020) · Minds and Machines · doi:10.1007/s11023-020-09540-9 · Cited by 62 · see citing works
Simplicity and informativeness in semantic category systems
Carr, J. W., Smith, K., Culbertson, J., & Kirby, S. (2020) · Cognition · doi:10.1016/j.cognition.2020.104289 · Cited by 56 · see citing works
Bridging the gap between theory and practice of approximate Bayesian inference
Kwisthout, J., & van Rooij, I. (2013) · Cognitive Systems Research · doi:10.1016/j.cogsys.2012.12.008 · Cited by 56 · see citing works
The complexity of human cooperation under indirect reciprocity
Santos, F. P., Pacheco, J. M., & Santos, F. C. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2020.0291 · Cited by 56 · see citing works
Cook, J., Bedford, D., & Mandia, S. A. (2014) · Journal of Geoscience Education · doi:10.5408/13-071.1 · Cited by 55 · see citing works
Contextual predictability shapes signal autonomy
Winters, J., Kirby, S., & Smith, K. (2018) · Cognition · doi:10.1016/j.cognition.2018.03.002 · Cited by 54 · see citing works
Ease of learning explains semantic universals
Steinert‐Threlkeld, S., & Szymanik, J. (2019) · Cognition · doi:10.1016/j.cognition.2019.104076 · Cited by 53 · see citing works
The perceptual relevance of balance, evenness, and entropy in musical rhythms
Milne, A. J., & Herff, S. A. (2020) · Cognition · doi:10.1016/j.cognition.2020.104233 · Cited by 53 · see citing works
Changing What You See by Changing What You Know: The Role of Attention
Lupyan, G. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00553 · Cited by 53 · see citing works
Vickers, D., Lee, M. D., Dry, M. J., & Hughes, P. (2003) · Memory & Cognition · doi:10.3758/bf03196130 · Cited by 52 · see citing works
Curiosity and the economics of attention
Wojtowicz, Z., & Loewenstein, G. F. (2020) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.09.002 · Cited by 51 · see citing works
One model for the learning of language
Yang, Y., & Piantadosi, S. T. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2021865119 · Cited by 50 · see citing works
Interventions to Dispel Neuromyths in Educational Settings—A Review
Rousseau, L. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.719692 · Cited by 50 · see citing works
Cultural Transmission and Evolution of Melodic Structures in Multi-generational Signaling Games
Lumaca, M., & Baggio, G. (2017) · Artificial Life · doi:10.1162/artl_a_00238 · Cited by 49 · see citing works
From likely to likable: The role of statistical typicality in human social assessment of faces
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The Logical Problem of Language Acquisition: A Probabilistic Perspective
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The optimality of attaching unlinked labels to unlinked meanings
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Perceptual estimation obeys Occam's razor
Gershman, S. J., & Niv, Y. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00623 · Cited by 46 · see citing works
Raviv, L., Kloots, M. D. H., & Meyer, A. (2021) · Cognition · doi:10.1016/j.cognition.2021.104620 · Cited by 26 · see citing works
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Aspects of cognitive style and programming.
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- Authors
- Chater, N., & Vitányi, P.
- Venue
- Trends in Cognitive Sciences
- Publisher
- Elsevier BV
- Published
- 2003-01
- Volume
- 7
- Issue
- 1
- Pages
- 19-22
- ISSN
- 1364-6613 (ISSN-L)
- OpenAlex
- W2093777421
- Cited by
- 494
- References
- 44
- Citations by year
- 2026: 11 · 2025: 32 · 2024: 22 · 2023: 33 · 2022: 56 · 2021: 42 · 2020: 29 · 2019: 30 · 2018: 24 · 2017: 24 · 2016: 22 · 2015: 20 · 2014: 15 · 2013: 17 · 2012: 19
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Trends in Cognitive Sciences (publishedVersion)
- PubMed (publishedVersion)
- UvA-DARE (University of Amsterdam) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computability, Logic, AI Algorithms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Keywords
- simplicity, pattern selection, inductive reasoning, epistemology, philosophy of science, perception, generalization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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