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HUMSENTPROC

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Uncovering the nature of human sentence processing a computational experimental...

Uncovering the nature of human sentence processing a computational experimental approach "The project’s research objective is to further our understanding of how people process language. This is accomplished by developing and implementing a range of computational models of sentence processing, each embedding different... ver más

31/08/2012

UNIVERSITY COLLEGE...

180K€

Presupuesto del proyecto: 180K€

Líder del proyecto

UNIVERSITY COLLEGE LONDON No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Ver 2 Participantes

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2012-08-31 No tenemos la información de la convocatoria

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Información adicional privada

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2 Participantes

UNIVERSITY COLLEGE LONDON

179.60K€ | Lider

UNIVERSITY COLLEGE LONDON

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Líder del proyecto

UNIVERSITY COLLEGE LONDON No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 180K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto "The project’s research objective is to further our understanding of how people process language. This is accomplished by developing and implementing a range of computational models of sentence processing, each embedding different psychological mechanisms. Next, the models' ability to account for a large body of experimental data is evaluated. On the basis of how the model(s) fit the data, we can isolate the most plausible psychological mechanisms for sentence processing. The notion of ""word surprisal"" is used to link the models to the data. Formally, word surprisal is defined in terms of informativeness of a word in sentence context. Quantitative measures of surprisal can therefore be extracted from different probabilistic language models. Empirically, surprisal can be viewed as the extent to which the word came unexpected to a reader or listener, having a measurable effect during sentence processing. To capture both aspects of word surprisal, the project combines psycholinguistic experimentation and computational modelling. On the experimental side, word-reading times and ERP data will be collected to obtain measurements of the surprise experienced by readers on encountering words. On the modelling side, several probabilistic sentence-processing models will be implemented to obtain estimates of word surprisal which serve to predict the empirical data. Matching the model's surprisal estimates to the experimental findings will identify the model(s) that provide(s) the most accurate description of human sentence processing. This will inspire the development of a more advanced model that simulates sentence processing more adequately. The main training objective is for the applicant to obtain the necessary knowledge, skills and experience to set up and perform psycholinguistic reading-time and EEG experiments. This complements his current expertise in computational methods, putting him in an ideal position to perform further integrative research in cognitive science."

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