This project will explore the neural mechanisms by which contextual predictions in language processing are integrated with incoming information. Predictive mechanisms provide an important solution to the challenges presented by linguistic input, which is often noisy, rapid, and variable. Much recent work suggests that language comprehenders use context to make predictions. These predictions are likely to impact multiple stages of language processing. This project aims at dissociating the neural mechanisms underlying predictive effects on lexical access and lexical selection. Predictions are likely to result in facilitated lexical access when fulfilled. Conversely, predictions may lead to increased demands on selection mechanisms when they are not fulfilled, due to the conflict between the evidence provided by top-down and bottom-up information. Determining the time course and cortical areas underlying the impacts of contextual prediction on access and selection mechanisms is necessary for understanding how comprehenders use context to process language more efficiently and why some populations seem less able to do this than others. Multimodal imaging techniques will be used to spatially and temporally dissociate the effects of prediction on lexical access and lexical selection. Minimum Norms methods of source localization will be used to directly integrate concurrent MEG/EEG and fMRI datasets. A semantic priming paradigm will be used to investigate predictions based on stored semantic associations, while a sentence context paradigm will be used to investigate predictions based on sentence- and discourse-level representations. Prediction strength and the degree to which predictions are fulfilled will be manipulated. These studies will test the hypothesis that strong fulfilled predictions result in facilitated lexical access and that strong unfulfilled predictions result in increased demands on lexical selection. Based on prior electrophysiological and neuroimaging work, these effects are expected to be associated with distinct spatiotemporal neural signatures. A third study will use these findings to test the hypothesis that the impairments in contextual processing in language that have been observed in schizophrenia are due to a specific deficit in the use of context for lexical selection. The multimodal approach is a critical aspect of the project. While EEG and MEG have excellent temporal resolution, functional distinctions between neighboring areas of cortex cannot be easily resolved by current MEG/EEG localization techniques. Multimodal recordings will make it possible to use information from EEG and MEG to constrain the interpretation of the fMRI data, and vice versa. For the current project, this approach will allow mechanisms such as predictive lexical activation and selection to be successfully disentangled.
Using contextual information to interpret upcoming input is a critical part of successful language comprehension, and deficits in use of language context have been reported in a number of groups, including patients with autism and schizophrenia and patients with damage to left inferior frontal areas. This project uses multimodal neuroimaging methods to investigate the effects of contextual prediction on different stages of language comprehension. A better understanding of this network will aid in determining the source of such deficits and thus will help lead to development of more optimal rehabilitation approaches.
|Lau, Ellen F; Weber, Kirsten; Gramfort, Alexandre et al. (2016) Spatiotemporal Signatures of Lexical-Semantic Prediction. Cereb Cortex 26:1377-87|
|Weber, Kirsten; Lau, Ellen F; Stillerman, Benjamin et al. (2016) The Yin and the Yang of Prediction: An fMRI Study of Semantic Predictive Processing. PLoS One 11:e0148637|
|Lau, Ellen F; Gramfort, Alexandre; Hämäläinen, Matti S et al. (2013) Automatic semantic facilitation in anterior temporal cortex revealed through multimodal neuroimaging. J Neurosci 33:17174-81|
|Lau, Ellen F; Holcomb, Phillip J; Kuperberg, Gina R (2013) Dissociating N400 effects of prediction from association in single-word contexts. J Cogn Neurosci 25:484-502|