Incorporating context with neural language models

In this episode, I talk with Alexander Huth, Assistant Professor of Neuroscience and Computer Science at the University of Texas, Austin, about his work using functional imaging and advanced computational methods to model how the brain processes language and represents meaning.

Huth AG, Nishimoto S, Vu AT, Gallant JL. A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron 2012; 76: 1210-24. [doi]

Huth AG, de Heer WA, Griffiths TL, Theunissen FE, Gallant JL. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 2016; 532: 453-8. [doi]

Jain S, Huth AG. Incorporating context into language encoding models for fMRI. Proceedings of the 32nd International Conference on Neural Information Processing Systems 2018, pp. 6629-38. [doi]

Tang J, LeBel A, Jain S, Huth AG. Semantic reconstruction of continuous language from non-invasive brain recordings. Nat Neurosci in press. [doi]