The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Statistical learning as an individual ability: Theoretical perspectives and empirical evidence
Open accessAbstract
Although the power of statistical learning (SL) in explaining a wide range of linguistic functions is gaining increasing support, relatively little research has focused on this theoretical construct from the perspective of individual differences. However, to be able to reliably link individual differences in a given ability such as language learning to individual differences in SL, three critical theoretical questions should be posed: Is SL a componential or unified ability? Is it nested within other general cognitive abilities? Is it a stable capacity of an individual? Following an initial mapping sentence outlining the possible dimensions of SL, we employed a battery of SL tasks in the visual and auditory modalities, using verbal and non-verbal stimuli, with adjacent and non-adjacent contingencies. SL tasks were administered along with general cognitive tasks in a within-subject design at two time points to explore our theoretical questions. We found that SL, as measured by some tasks, is a stable and reliable capacity of an individual. Moreover, we found SL to be independent of general cognitive abilities such as intelligence or working memory. However, SL is not a unified capacity, so that individual sensitivity to conditional probabilities is not uniform across modalities and stimuli.
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Statistical learning by 8-month-old infants
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996) · Science · doi:10.1126/science.274.5294.1926 · Cited by 5,659
Manual for the Wechsler Adult Intelligence Scale.
Wechsler, D. (1955) · Psychological Corp. eBooks · Cited by 4,941
Praat, a system for doing phonetics by computer
Boersma, P. (2002) · UvA-DARE (University of Amsterdam) · Cited by 4,417
(1998) · Cited by 4,172
Costs of a predictible switch between simple cognitive tasks.
Rogers, R. D., & Monsell, S. (1995) · Journal of Experimental Psychology: General · doi:10.1037/0096-3445.124.2.207 · Cited by 3,170
DMDX: A Windows display program with millisecond accuracy
Forster, K. I., & Forster, J. C. (2003) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03195503 · Cited by 2,759
Implicit learning of artificial grammars
Reber, A. S. (1967) · Journal of Verbal Learning and Verbal Behavior · doi:10.1016/s0022-5371(67)80149-x · Cited by 2,026
Implicit learning and tacit knowledge: an essay on the cognitive unconscious
(1994) · Choice Reviews Online · doi:10.5860/choice.31-3490 · Cited by 1,429
Rule Learning by Seven-Month-Old Infants
Marcus, G. F., Vijayan, S., Bandi Rao, S., & Vishton, P. M. (1999) · Science · doi:10.1126/science.283.5398.77 · Cited by 1,244
Visual statistical learning in infancy: evidence for a domain general learning mechanism
Kirkham, N. Z., Slemmer, J. A., & Johnson, S. P. (2002) · Cognition · doi:10.1016/s0010-0277(02)00004-5 · Cited by 1,072
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Atzil, S., Gao, W., Fradkin, I., & Barrett, L. F. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0384-6 · Cited by 415
Emotion words, emotion concepts, and emotional development in children: A constructionist hypothesis
Hoemann, K., Xu, F., & Barrett, L. F. (2019) · Developmental Psychology · doi:10.1037/dev0000686 · Cited by 354
Implicit Statistical Learning: A Tale of Two Literatures
Christiansen, M. H. (2019) · Topics in Cognitive Science · doi:10.1111/tops.12332 · Cited by 167
Bio-behavioral synchrony promotes the development of conceptualized emotions
Atzil, S., & Gendron, M. (2017) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2017.07.009 · Cited by 120
THE CONTRIBUTIONS OF IMPLICIT-STATISTICAL LEARNING APTITUDE TO IMPLICIT SECOND-LANGUAGE KNOWLEDGE
Godfroid, A., & Kim, K. M. (2021) · Studies in Second Language Acquisition · doi:10.1017/s0272263121000085 · Cited by 51
Metadata
- Authors
- Siegelman, N., & Frost, R.
- Venue
- Journal of Memory and Language
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 81
- Pages
- 105-120
- ISSN
- 0749-596X (ISSN-L)
- OpenAlex
- W1995982951
- Cited by
- 317
- References
- 61
- FWCI
- 13.64
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.