The Bias–Variance Tradeoff in Cognitive Science
Open accessAbstract
The bias–variance tradeoff is a theoretical concept that suggests machine learning algorithms are susceptible to two kinds of error, with some algorithms tending to suffer from one more than the other. In this letter, we claim that the bias–variance tradeoff is a general concept that can be applied to human cognition as well, and we discuss implications for research in cognitive science. In particular, we show how various strands of research in cognitive science can be interpreted in light of the bias–variance tradeoff, giving insight into individual differences in learning, the nature of cognitive processes, and debates in cognitive science research.
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“15 From a mathematical point of view, noise (the joint probability distribution between training and test/inference samples), bias, and variance are the three components that lead to model performance degradation and negatively affect generalization to new data.”
- Authors
- Doroudi, S., & Rastegar, S. A.
- Affiliations
- University of California, Irvine (US)
- R.I.S.E. Academy, San Jose
- Corresponding author
- Doroudi, Shayan
- ORCID
- Doroudi, Shayan
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 2023-01
- Volume
- 47
- Issue
- 1
- Pages
- e13241-e13241
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W4317480115
- Cited by
- 9
- References
- 11
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 2
- FWCI
- 1.34
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cognitive Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Keywords
- bias-variance trade-off, human cognition, individual differences in learning, cognitive processes, generalization error
- MeSH terms
- Machine Learning, Algorithms, Cognition, Humans, Bias
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