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Titolo:
ASSOCIATIVE CHUNK STRENGTH IN ARTIFICIAL GRAMMAR LEARNING
Autore:
MEULEMANS T; VANDERLINDEN M;
Indirizzi:
FAC PSYCHOL,SERV NEUROPSYCHOL,BLVD RECTORAT B33 B-4000 LIEGE BELGIUM UNIV LIEGE,NEUROSPSYCHOL UNIT LIEGE BELGIUM
Titolo Testata:
Journal of experimental psychology. Learning, memory, and cognition
fascicolo: 4, volume: 23, anno: 1997,
pagine: 1007 - 1028
SICI:
0278-7393(1997)23:4<1007:ACSIAG>2.0.ZU;2-T
Fonte:
ISI
Lingua:
ENG
Soggetto:
INTERACTIVE TASKS; TACIT KNOWLEDGE; IMPLICIT; SYSTEMS; CLASSIFICATION; PERFORMANCE; INFORMATION; ACQUISITION; JUDGMENT; MEMORY;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Science Citation Index Expanded
Citazioni:
39
Recensione:
Indirizzi per estratti:
Citazione:
T. Meulemans e M. Vanderlinden, "ASSOCIATIVE CHUNK STRENGTH IN ARTIFICIAL GRAMMAR LEARNING", Journal of experimental psychology. Learning, memory, and cognition, 23(4), 1997, pp. 1007-1028

Abstract

In 4 experiments, adherence to grammatical rules and associative chunk strength (including different measures, each calculated on the basisof the frequency with which bigrams and trigrams present in the test strings appeared in the learning strings) were manipulated independently in the test phase of an artificial grammar learning task. When participants learned few items of the grammar (Experiments 1A and 2A), theassociated items were more often classified as grammatical than the nonassociated ones. On the other hand, when the learning phase includedmost of the grammatical items (Experiments 1B and 2B), the only effect observed was an effect of grammaticality. These results suggest that, depending on the specific constraints of the tasks, knowledge based on bigrams and trigrams and knowledge based on the abstraction of the grammatical structure can be used for the classification task.

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Documento generato il 28/09/20 alle ore 04:43:39