Multidimensional visual statistical learning

Nicholas B Turk-Browne; Phillip J Isola; Brian J Scholl; Teresa A Treat

doi:10.1037/0278-7393.34.2.399

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Multidimensional visual statistical learning

Journal article

Peer reviewed

Multidimensional visual statistical learning

Nicholas B Turk-Browne, Phillip J Isola, Brian J Scholl and Teresa A Treat

Journal of experimental psychology. Learning, memory, and cognition, Vol.34(2), pp.399-407

03/2008

DOI: 10.1037/0278-7393.34.2.399

PMID: 18315414

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Abstract

Recent studies of visual statistical learning (VSL) have demonstrated that statistical regularities in sequences of visual stimuli can be automatically extracted, even without intent or awareness. Despite much work on this topic, however, several fundamental questions remain about the nature of VSL. In particular, previous experiments have not explored the underlying units over which VSL operates. In a sequence of colored shapes, for example, does VSL operate over each feature dimension independently, or over multidimensional objects in which color and shape are bound together? The studies reported here demonstrate that VSL can be both object-based and feature-based, in systematic ways based on how different feature dimensions covary. For example, when each shape covaried perfectly with a particular color, VSL was object-based: Observers expressed robust VSL for colored-shape sub-sequences at test but failed when the test items consisted of monochromatic shapes or color patches. When shape and color pairs were partially decoupled during learning, however, VSL operated over features: Observers expressed robust VSL when the feature dimensions were tested separately. These results suggest that VSL is object-based, but that sensitivity to feature correlations in multidimensional sequences (possibly another form of VSL) may in turn help define what counts as an object.

Association Learning

Color Perception

Discrimination Learning

Humans

Motion Perception

Orientation

Pattern Recognition, Visual

Perceptual Masking

Probability Learning

Problem Solving

Psychophysics

Details

Title: Subtitle: Multidimensional visual statistical learning
Creators: Nicholas B Turk-Browne - Department of Psychology, Yale University, New Haven, CT 06520-8205, USA. nicholas.turk-browne@yale.edu
Phillip J Isola
Brian J Scholl
Teresa A Treat
Resource Type: Journal article
Publication Details: Journal of experimental psychology. Learning, memory, and cognition, Vol.34(2), pp.399-407
DOI: 10.1037/0278-7393.34.2.399
PMID: 18315414
ISSN: 0278-7393
eISSN: 1939-1285
Grant note: DOI: 10.13039/501100000038, name: Natural Sciences and Engineering Research Council; name: National Science Foundation, award: BCS-0132444
Language: English
Date published: 03/2008
Academic Unit: Psychological and Brain Sciences
Record Identifier: 9984213394702771

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