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Cited by 795

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

The Automaticity of Visual Statistical Learning.

Turk-Browne, Nicholas B., Jungé, Justin A., Scholl, Brian J.·2005·Journal of Experimental Psychology: General

Abstract

The visual environment contains massive amounts of information involving the relations between objects in space and time, and recent studies of visual statistical learning (VSL) have suggested that this information can be automatically extracted by the visual system. The experiments reported in this article explore the automaticity of VSL in several ways, using both explicit familiarity and implicit response-time measures. The results demonstrate that (a) the input to VSL is gated by selective attention, (b) VSL is nevertheless an implicit process because it operates during a cover task and without awareness of the underlying statistical patterns, and (c) VSL constructs abstracted representations that are then invariant to changes in extraneous surface features. These results fuel the conclusion that VSL both is and is not automatic: It requires attention to select the relevant population of stimuli, but the resulting learning then occurs without intent or awareness.

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Metadata

Authors
Turk-Browne, N. B., Jungé, J. A., & Scholl, B. J.
Venue
Journal of Experimental Psychology: General
Published
2005
Volume
134
Issue
4
Pages
552-564
ISSN
  • 0096-3445 (ISSN-L)
  • 1939-2222
OpenAlex
W2033537108
Cited by
795
References
6
FWCI
6.23
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: The Automaticity of Visual Statistical Learning. https://app.cochat.ai/papers/10.1037/0096-3445.134.4.552
The paper
Turk-Browne, N. B., Jungé, J. A., & Scholl, B. J. (2005). The Automaticity of Visual Statistical Learning. Journal of Experimental Psychology: General. https://doi.org/10.1037/0096-3445.134.4.552

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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.