Statistical Learning: From Acquiring Specific Items to Forming General Rules
Open accessAbstract
Statistical learning is a rapid and robust mechanism that enables adults and infants to extract patterns embedded in both language and visual domains. Statistical learning operates implicitly, without instruction, through mere exposure to a set of input stimuli. However, much of what learners must acquire about a structured domain consists of principles or rules that can be applied to novel inputs. It has been claimed that statistical learning and rule learning are separate mechanisms; in this article, however, we review evidence and provide a unifying perspective that argues for a single statistical-learning mechanism that accounts for both the learning of input stimuli and the generalization of learned patterns to novel instances. The balance between instance-learning and generalization is based on two factors: the strength of perceptual and cognitive biases that highlight structural regularities, and the consistency of elements’ contexts (unique vs. overlapping) in the input.
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In implicit statistical learning tasks, participants can differentiate well-formed sequences from those that are not, but cannot verbalize the basis for their judgments.
“Studies found that participants can differentiate well-formed sequences from those that are not, but cannot verbalize the basis for their judgments (Aslin & Newport, 2012;Romberg & Saffran, 2010).”
From Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse (2024) · cites this paper as Aslin & Newport, 2012
In “Statistical learning in language and vision” (page unavailable), the paper reports that learners discriminate words from other syllable sequences and do so without awareness of computing the relevant statistic. It describes these as implicit decisions, but does not report that participants were unable to verbalize the basis for their judgments. (LLM, conf=0.86)
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Cites
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“Studies found that participants can differentiate well-formed sequences from those that are not, but cannot verbalize the basis for their judgments (Aslin & Newport, 2012; Romberg & Saffran, 2010).”
- Authors
- Aslin, R. N., & Newport, E. L.
- Affiliations
- University of Rochester
- University of Rochester (US)
- ORCID
- Newport, Elissa L.
- Venue
- Current Directions in Psychological Science
- Publisher
- SAGE Publications
- Published
- 2012-05-30
- Volume
- 21
- Issue
- 3
- Pages
- 170-176
- ISSN
- 0963-7214 (ISSN-L)
- 1467-8721
- PMC
- PMC3758750
- OpenAlex
- W2136401406
- Cited by
- 348
- References
- 1
- Citations by year
- 2026: 15 · 2025: 20 · 2024: 24 · 2023: 16 · 2022: 20 · 2021: 18 · 2020: 50 · 2019: 37 · 2018: 23 · 2017: 33 · 2016: 26 · 2015: 27 · 2014: 23 · 2013: 15 · 2012: 1
- FWCI
- 42.51
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Current Directions in Psychological Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Deafness and Other Communication Disorders (R01DC000167)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD037082)
- Topics
- Social Sciences › Psychology › Developmental and Educational Psychology › Language Development and Disorders
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
- Social Sciences › Psychology › Developmental and Educational Psychology › Reading and Literacy Development
- Keywords
- statistical learning, implicit learning, rule induction, perceptual bias, structural regularity, language acquisition, infant language acquisition, context consistency
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