Journal Article10.1006/JECP.1998.2442
Implicit sequence learning in children
TL;DR: The results show no age-related difference in the serial reaction time performance, which is consistent with the idea that implicit learning abilities may be efficient early in development.
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About: This article is published in Journal of Experimental Child Psychology. The article was published on 01 Jun 1998. The article focuses on the topics: Implicit learning & Sequence learning.
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References
Attentional requirements of learning: Evidence from performance measures
Mary Jo Nissen,Peter Bullemer +1 more
TL;DR: This article investigated whether performance measures would also show a strong dependence on attention and found that patients with Korsakoff's syndrome learned the sequence despite their lack of awareness of the repeating pattern.
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•Book
Beyond Modularity: A Developmental Perspective on Cognitive Science
Annette Karmiloff-Smith
- 01 Jan 1992
TL;DR: In this paper, a Hegelian synthesis of Piagetian constructivism and Fodorian modularity in terms of the author's own model of representational ''representational ''
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Implicit learning of artificial grammars
TL;DR: This paper investigated the process by which Ss respond to the statistical nature of the stimulus array, a process defined as "implicit learning" and found that Ss were learning to respond to general grammatical nature of stimuli, rather than learning according to specific coding systems imposed upon the stimuli.
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Implicit Learning and Tacit Knowledge: An Essay on the Cognitive Unconscious
Arthur S. Reber
- 01 Jul 1993
TL;DR: Reber as mentioned in this paper presents a highly readable account of the cognitive unconscious, focusing in particular on the problem of implicit learning, defined as the acquisition of knowledge that takes place independently of the conscious attempts to learn and largely in the absence of explicit knowledge about what was acquired.
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Characteristics of dissociable human learning systems
David R. Shanks,Mark F. St. John +1 more
TL;DR: The distinction between instance and rule learning is a sound and meaningful way of taxonomizing human learning and various computational models of these two forms of learning are discussed.
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