Splitting the variance of statistical learning performance: A parametric investigation of exposure duration and transitional probabilities.
Psychon Bull Rev
; 23(4): 1250-6, 2016 08.
Article
em En
| MEDLINE
| ID: mdl-26743060
ABSTRACT
What determines individuals' efficacy in detecting regularities in visual statistical learning? Our theoretical starting point assumes that the variance in performance of statistical learning (SL) can be split into the variance related to efficiency in encoding representations within a modality and the variance related to the relative computational efficiency of detecting the distributional properties of the encoded representations. Using a novel methodology, we dissociated encoding from higher-order learning factors, by independently manipulating exposure duration and transitional probabilities in a stream of visual shapes. Our results show that the encoding of shapes and the retrieving of their transitional probabilities are not independent and additive processes, but interact to jointly determine SL performance. The theoretical implications of these findings for a mechanistic explanation of SL are discussed.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Aprendizagem por Probabilidade
/
Resolução de Problemas
/
Tempo de Reação
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Aprendizagem por Associação
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Percepção de Forma
Limite:
Adult
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Female
/
Humans
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Male
Idioma:
En
Revista:
Psychon Bull Rev
Assunto da revista:
PSICOLOGIA
Ano de publicação:
2016
Tipo de documento:
Article
País de afiliação:
França