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- Jenny R. Saffran
- Department of Psychology, University of Wisconsin–Madison, Madison, Wisconsin 53706;
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- Natasha Z. Kirkham
- Department of Psychological Sciences, Birkbeck, University of London, London WC1E 7HX, United Kingdom;
書誌事項
- 公開日
- 2018-01-04
- 権利情報
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- http://www.annualreviews.org/licenses/tdm
- DOI
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- 10.1146/annurev-psych-122216-011805
- 公開者
- Annual Reviews
この論文をさがす
説明
<jats:p>Perception involves making sense of a dynamic, multimodal environment. In the absence of mechanisms capable of exploiting the statistical patterns in the natural world, infants would face an insurmountable computational problem. Infant statistical learning mechanisms facilitate the detection of structure. These abilities allow the infant to compute across elements in their environmental input, extracting patterns for further processing and subsequent learning. In this selective review, we summarize findings that show that statistical learning is both a broad and flexible mechanism (supporting learning from different modalities across many different content areas) and input specific (shifting computations depending on the type of input and goal of learning). We suggest that statistical learning not only provides a framework for studying language development and object knowledge in constrained laboratory settings, but also allows researchers to tackle real-world problems, such as multilingualism, the role of ever-changing learning environments, and differential developmental trajectories.</jats:p>
収録刊行物
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- Annual Review of Psychology
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Annual Review of Psychology 69 (1), 181-203, 2018-01-04
Annual Reviews
