Computational Cognitive Neuroscience Seminar

  • BIDS Data Science Fellow
  • Berkeley Institute for Data Science
  • University of California at Berkeley

The effect of modality and context on the brain representation of natural language

Natural language is strongly context-dependent and can be perceived through different sensory modalities. For example, humans can easily comprehend the meaning of language presented through auditory speech or written text. However, how the human brain represents natural language in different modalities is still unclear. Using natural text and vector representations derived from natural language processing methods I will first present a modeling framework to study language processing in the human brain across modalities. I will then discuss how contextual effects modulate the representation of word meaning in the human brain and will present preliminary work of applying the same modeling framework to answer the question of how different languages are represented in the brains of bilinguals.

Fatma Deniz is a joint postdoctoral researcher at the Gallant Lab in UC Berkeley’s Helen Wills Neuroscience Institute and the International Computer Science Institute. She is interested in how sensory information is encoded in the brain and uses machine learning approaches to fit computational models to large-scale brain data acquired using functional magnetic resonance imaging (fMRI). Fatma works at the intersection between computer science, linguistics, music, and neuroscience. Her current focus is on the cross-modal representation of language in the human brain. In addition, she works on improving internet security applications using knowledge gained from cognitive neuroscience (MooneyAuth Project). She is an enthusiastic teacher for Berkeley's data-8 connector course Data Science for Cognitive Neuroscience (Fall2016 and Spring2017) and an instroctor in Software Carpentry, where she teaches scientific computing. As an advocate of reproducible research practices she is the co-editor of the book titled “The Practice of Reproducible Research”. As a data science fellow, she is interested in teaching and reproducible research and sees herself as a connector between diverse domains. She is a passionate coder, runner, baker, and cello player.

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