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The Bayesian information-theoretical model of language processing in the brain


The aim of my research project is to link two levels of explanation of linguistics processing that have traditionally been disconnected: behavioural responses and neuro-biological theories. I am addressing this problem through a combination of techniques taken from a wide range of disciplines: computational linguistics, cognitive psychology, artificial intelligence, statistical inference, and cognitive neuroscience.

My proposed approach is to employ computational inference techniques to uncover the detailed link between a detailed neuro-physiological theory of language processing (Pulverm\"uller,~1999) and several previously reported behavioural effects. For this purpose, I have recently developed the Bayesian Information-Theoretical model of lexical processing (BIT; Moscoso del Prado, Kostic, & Filipovic-Djurdjevic, 2006) a set of statistical and information-theoretical tools that enable us to make quantitative predictions on behavioural responses in word recognition based on an underlying neuro-physiological theory, and link those same measures to neuroimaging results.

In the current project I intend to extend this approach in two aspects:
(1) In its current estate, the BIT model only deals with the morphosemantic aspects of word recognition. In order for the model to provide a unified theory of lexical processing, the orthographic aspects of word identification should also be taken into account.
(2) As well as accounting for word recognition, the model should also be able to account for the wealth of results that are available on the lexical aspects of language production.

In order to achieve these goals, I propose to combine computational modelling, behavioural and neuroimaging experiments. To ensure the generality of the model, these studies will be performed on three different languages, with very different morphological and orthographical properties: French, Arabic, and Serbian.

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3, Rue Michel-Ange

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