Fatma Deniz: Integrating hypothesis- and data-driven approaches to study the neurobiology of natural language

University of Berkeley and TU Berlin

Natural language is strongly context-dependent and can be perceived through different sensory modalities. For example, humans can easily comprehend the meaning of complex narratives presented through auditory speech, written text, or visual images. To understand how complex language-related information is represented in the human brain there is a necessity to map the different linguistic and non-linguistic information perceived under different modalities across the cerebral cortex. In order to map this various information to the brain, I suggest following a data-driven approach and observing the human brain performing tasks in its naturalistic setting, designing quantitative models that transform real-world stimuli into specific features, and building predictive models that can relate these features to brain responses. In this talk, I will first present how we can integrate linguistic hypotheses into data-driven models by creating specific features that can transform different levels of linguistic information in stimuli presented in different modalities. I will then present neuroimaging experiments that bridge the gap between tightly controlled, non-naturalistic experiments and naturalistic experiments, and thereby demonstrate how the results obtained from data-driven naturalistic experiments can generalize to the results obtained from non-naturalistic experiments.

 

Guests are welcome! If you are interested, please contact us for the zoom link.

 

Organized by

Klaus Obermayer / Margret Franke