ReviewPhilosophical transactions of the Royal Society of London. Series B, Biological sciences2017
Towards a theory of individual differences in statistical learning.
Review in Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed.
- Individual Differences in Distributional Learning for Speech: What's Ideal for Ideal Observers?Journal of speech, language, and hearing research : JSLHR · 2020Trial
- Re-Evaluating the Role of Statistical Learning: Domain-General Predictors of Language in Typical and Atypical Development.Cognitive science · 2026Article
- MC-MIFA: a causal-aware hybrid state space framework for robust multimodal student engagement analysis.Scientific reports · 2026Article
- Second-Language Learning Facilitates Non-Adjacent Dependency Learning: Effects Moderated by Specific Language.Cognitive science · 2026Article
- Task-dependent learning of non-adjacent dependencies: Success in a familiarity rating task but failure in a two-alternative forced-choice task.Psychonomic bulletin & review · 2025Article
- Addressing the theory crisis in statistical learning research.NPJ science of learning · 2025Review
- Taking time: Auditory statistical learning benefits from distributed exposure.Psychonomic bulletin & review · 2025Article
- Speech Stream Composition Affects Statistical Learning: Behavioral and Neural Evidence.Brain sciences · 2025Article
- Memory representations are flexibly adapted to orthographic systems: A comparison of English and Hebrew.Brain research · 2024Article
- Dissociation Between Linguistic and Nonlinguistic Statistical Learning in Children with Autism.Journal of autism and developmental disorders · 2024Article
- IDLaS-NL - A platform for running customized studies on individual differences in Dutch language skills via the Internet.Behavior research methods · 2024Article
- Semantic and Syntactic Predictions in Reading Aloud: Are Good Predictors Good Statistical Learners?Journal of cognition · 2024Article
- Elucidating a statistical learning brain network: Coordinate-based meta-analyses and functional connectivity profiles of artificial grammar learning in healthy adults.Imaging neuroscience (Cambridge, Mass.) · 2024Article
- The Domain-Specific Neural Basis of Auditory Statistical Learning in 5-7-Year-Old Children.Neurobiology of language (Cambridge, Mass.) · 2024Article
- Individual prediction tendencies do not generalize across modalities.Psychophysiology · 2024Article
- The complexity of measuring reliability in learning tasks: An illustration using the Alternating Serial Reaction Time Task.Behavior research methods · 2024Article
- Cortical speech tracking is related to individual prediction tendencies.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- No evidence for spatial suppression due to across-trial distractor learning in visual search.Attention, perception & psychophysics · 2023Article
- Learning at your brain's rhythm: individualized entrainment boosts learning for perceptual decisions.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Implicit learning and individual differences in speech recognition: an exploratory study.Frontiers in psychology · 2023Article
1 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
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Abstract
In recent years, statistical learning (SL) research has seen a growing interest in tracking individual performance in SL tasks, mainly as a predictor of linguistic abilities. We review studies from this line of research and outline three presuppositions underlying the experimental approach they employ: (i) that SL is a unified theoretical construct; (ii) that current SL tasks are interchangeable, and equally valid for assessing SL ability; and (iii) that performance in the standard forced-choice test in the task is a good proxy of SL ability. We argue that these three critical presuppositions are subject to a number of theoretical and empirical issues. First, SL shows patterns of modality- and informational-specificity, suggesting that SL cannot be treated as a unified construct. Second, different SL tasks may tap into separate sub-components of SL that are not necessarily interchangeable. Third, the commonly used forced-choice tests in most SL tasks are subject to inherent limitations and confounds. As a first step, we offer a methodological approach that explicitly spells out a potential set of different SL dimensions, allowing for better transparency in choosing a specific SL task as a predictor of a given linguistic outcome. We then offer possible methodological solutions for better tracking and measuring SL ability. Taken together, these discussions provide a novel theoretical and methodological approach for assessing individual differences in SL, with clear testable predictions.This article is part of the themed issue 'New frontiers for statistical learning in the cognitive sciences'.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.