The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Statistical Learning Within and Between Modalities: Pitting Abstract Against Stimulus-Specific Representations
Abstract
When learners encode sequential patterns and generalize their knowledge to novel instances, are they relying on abstract or stimulus-specific representations? Research on artificial grammar learning (AGL) has shown transfer of learning from one stimulus set to another, and such findings have encouraged the view that statistical learning is mediated by abstract representations that are independent of the sense modality or perceptual features of the stimuli. Using a novel modification of the standard AGL paradigm, we obtained data to the contrary. These experiments pitted abstract processing against stimulus-specific learning. The findings show that statistical learning results in knowledge that is stimulus-specific rather than abstract. They show furthermore that learning can proceed in parallel for multiple input streams along separate perceptual dimensions or sense modalities. We conclude that learning sequential structure and generalizing to novel stimuli inherently involve learning mechanisms that are closely tied to the perceptual characteristics of the input.
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Cites
Statistical Power Analysis for the Behavioral Sciences
Muller, K. (1989) · Technometrics · doi:10.1080/00401706.1989.10488618 · Cited by 99,167
Statistical Power Analysis for the Behavioral Sciences (2nd ed.).
Lachenbruch, P. A., & Cohen, J. (1989) · Journal of the American Statistical Association · doi:10.2307/2290095 · Cited by 37,395
Fodor, J. A. (1975) · Medical Entomology and Zoology · Cited by 4,588
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
Computation and Cognition: Toward a Foundation for Cognitive Science
Pylyshyn, Z. W. (1984) · The MIT Press eBooks · doi:10.7551/mitpress/2004.001.0001 · Cited by 2,025
Implicit Learning and Tacit Knowledge
Reber, A. S. (2003) · Essential Sources in the Scientific Study of Consciousness · doi:10.7551/mitpress/2834.003.0047 · Cited by 2,001
Glenberg, A. M. (1997) · Behavioral and Brain Sciences · doi:10.1017/s0140525x97000010 · Cited by 1,658
Implicit learning and tacit knowledge
Reber, A. S. (1989) · Journal of Experimental Psychology: General · doi:10.1037/0096-3445.118.3.219 · Cited by 1,581
Statistical learning of tone sequences by human infants and adults
Saffran, J. R., Johnson, E. K., Aslin, R. N., & Newport, E. L. (1999) · Cognition · doi:10.1016/s0010-0277(98)00075-4 · Cited by 1,429
Grounding conceptual knowledge in modality-specific systems
Barsalou, L. W., Kyle Simmons, W., Barbey, A. K., & Wilson, C. D. (2003) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(02)00029-3 · Cited by 1,256
Cited by
Crossmodal correspondences: A tutorial review
Spence, C. (2011) · Attention, Perception, & Psychophysics · doi:10.3758/s13414-010-0073-7 · Cited by 1,618
Implicit learning and statistical learning: one phenomenon, two approaches
Perruchet, P., & Pacton, S. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2006.03.006 · Cited by 830
Turk-Browne, N. B., Scholl, B. J., Chun, M. M., & Johnson, M. K. (2009) · Journal of Cognitive Neuroscience · doi:10.1162/jocn.2009.21131 · Cited by 523
Implicit statistical learning in language processing: Word predictability is the key☆
Conway, C. M., Bauernschmidt, A., Huang, S. S., & Pisoni, D. B. (2010) · Cognition · doi:10.1016/j.cognition.2009.10.009 · Cited by 340
Self-citationLearning to integrate arbitrary signals from vision and touch
Ernst, M. O. (2007) · Journal of Vision · doi:10.1167/7.5.7 · Cited by 332
Metadata
- Authors
- Conway, C. M., & Christiansen, M. H.
- Venue
- Psychological Science
- Publisher
- SAGE Publications
- Published
- 2006-10
- Volume
- 17
- Issue
- 10
- Pages
- 905-912
- ISSN
- 0956-7976 (ISSN-L)
- 1467-9280
- OpenAlex
- W2147832298
- Cited by
- 294
- References
- 46
- FWCI
- 6.76
- Type
- journal article
- Language
- en
- Open access
- closed
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