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Autor/inn/enTurk-Browne, Nicholas B.; Scholl, Brian J.; Chun, Marvin M.; Johnson, Marcia K.
TitelNeural Evidence of Statistical Learning: Efficient Detection of Visual Regularities without Awareness
QuelleIn: Journal of Cognitive Neuroscience, 21 (2009) 10, S.1934-1945 (12 Seiten)
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN0898-929X
DOI10.1162/jocn.2009.21131
SchlagwörterFamiliarity; Associative Learning; Statistics; Responses; Cognitive Processes; Diagnostic Tests; Task Analysis; Brain Hemisphere Functions; Memory; Learning Processes
AbstractOur environment contains regularities distributed in space and time that can be detected by way of statistical learning. This unsupervised learning occurs without intent or awareness, but little is known about how it relates to other types of learning, how it affects perceptual processing, and how quickly it can occur. Here we use fMRI during statistical learning to explore these questions. Participants viewed statistically structured versus unstructured sequences of shapes while performing a task unrelated to the structure. Robust neural responses to statistical structure were observed, and these responses were notable in four ways: First, responses to structure were observed in the striatum and medial temporal lobe, suggesting that statistical learning may be related to other forms of associative learning and relational memory. Second, statistical regularities yielded greater activation in category-specific visual regions (object-selective lateral occipital cortex and word-selective ventral occipito-temporal cortex), demonstrating that these regions are sensitive to information distributed in time. Third, evidence of learning emerged early during familiarization, showing that statistical learning can operate very quickly and with little exposure. Finally, neural signatures of learning were dissociable from subsequent explicit familiarity, suggesting that learning can occur in the absence of awareness. Overall, our findings help elucidate the underlying nature of statistical learning. (As Provided).
AnmerkungenMIT Press. Circulation Department, Five Cambridge Center, Cambridge, MA 02142. Tel: 617-253-2889; Fax: 617-577-1545; e-mail: journals-orders@mit.edu; Web site: http://www.mitpressjournals.org/loi/jocn
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2017/4/10
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