Abstract:
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. To map this information to the brain, I suggest following a data-driven, naturalistic approach and observing the human brain performing tasks in its naturalistic setting, designing quantitative models that transform real-world stimuli into specific hypothesis-related features, and building predictive models that can relate these features to brain responses. In my talk, I will present models of brain responses collected using functional magnetic resonance imaging while human participants listened to or read natural narrative stories. Using natural text and vector representations derived from natural language processing tools I will present how we can study language processing in the human brain across modalities, in different levels of temporal granularity, and across languages.

Bio:
Fatma Deniz is a junior group leader and a principal investigator at the Technical University Berlin and UC Berkeley. Prior to that, she was a Moore-Sloan Data Science Fellow at Berkeley Institute for Data Science, a postdoctoral fellow at the International Computer Science Institute in Berkeley, and Helen Wills Neuroscience Institute. She is interested in how complex information is encoded in the brain and uses machine learning approaches to fit computational models to large-scale brain data. Her current work focuses on cross-lingual and cross-modal natural language representations in the human brain and machines. She did her Ph.D. at the Bernstein Center for Computational Neuroscience in Berlin. She got a bachelor’s and master’s degrees in Computer Science from the Technical University Munich and worked at the California Institute of Technology during her master’s thesis. As an advocate of reproducible research practices, she is the co-editor of the book titled “The Practice of Reproducible Research”. Her work spans the fields of machine learning, human cognition, and neuroscience.