Home > Events > Linguistics Colloquium: Jonathan Brennan (Michigan)
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Linguistics Colloquium: Jonathan Brennan (Michigan)

Time: 
Friday, October 20, 2017 - 3:00 PM to 4:30 PM
Location: 
1310A Marie Mount Hall

Title: Linking brain signals with grammar using neuro-computational models

 Abstract: The cognitive neuroscience of language has made only limited contact with syntactic theory, in part because the relationship between grammatical knowledge and neural signals is indirect and often under-specified. This talk describes how neuro-computational models of sentence processing can be used to rigorously link theories of grammar with brain mechanisms. Such models explicitly describe the cognitive representations that are constructed during sentence comprehension, and quantify how such representations modulate measurable brain signals. The talk presents a set of such of models which parametrically vary in how grammatical knowledge constrains expectations about upcoming linguistic input, and how complex phrase-structures are built incrementally. These models are tested against electroencephalography and electocorticography data that are collected while participants perform a simple and natural task: listening to an audiobook story. In comparing the fit between alternative models and the measured brain data, the results constrain theories of both the syntactic representations that are created incrementally during comprehension, and the algorithms by which such representations are formed.

Bio: Jonathan Brennan studies the mental structures and computations used to understand words and sentences, with a focus on how these processes are implemented in the brain. His research uses formal computational models of language comprehension to investigate the neural correlates of basic cognitive computations such as lexical access, syntax, and semantics with electroencephalography (EEG), magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI).

He has a particular interest in experimental methods that are as natural as possible, such as having participants read or listen to a story, to focus on sentence processing as it occurs during every-day language use. Naturalistic techniques are especially suitable for the investigation of language comprehension in populations, such as children with autism spectrum disorder, for which standard experimental tasks may not be appropriate. He directs the Computational Neurolinguistics Lab at the University of Michigan.