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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

On Sequence Learning Models: Open-loop Control Not Strictly Guided by Hick's Law

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Autor(es):
Pavao, Rodrigo ; Savietto, Joice P. ; Sato, Joao R. ; Xavier, Gilberto F. ; Helene, Andre F.
Número total de Autores: 5
Tipo de documento: Artigo Científico
Fonte: SCIENTIFIC REPORTS; v. 6, MAR 15 2016.
Citações Web of Science: 0
Resumo

According to the Hick's law, reaction times increase linearly with the uncertainty of target stimuli. We tested the generality of this law by measuring reaction times in a human sequence learning protocol involving serial target locations which differed in transition probability and global entropy. Our results showed that sigmoid functions better describe the relationship between reaction times and uncertainty when compared to linear functions. Sequence predictability was estimated by distinct statistical predictors: conditional probability, conditional entropy, joint probability and joint entropy measures. Conditional predictors relate to closed-loop control models describing that performance is guided by on-line access to past sequence structure to predict next location. Differently, joint predictors relate to open-loop control models assuming global access of sequence structure, requiring no constant monitoring. We tested which of these predictors better describe performance on the sequence learning protocol. Results suggest that joint predictors are more accurate than conditional predictors to track performance. In conclusion, sequence learning is better described as an open-loop process which is not precisely predicted by Hick's law. (AU)

Processo FAPESP: 13/07699-0 - Centro de Pesquisa, Inovação e Difusão em Neuromatemática - NeuroMat
Beneficiário:Jefferson Antonio Galves
Linha de fomento: Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs