Huth Lab

Our ✨Large-scale fMRI dataset on naturalistic language✨is now out in Nature Scientific Data! MANY hours (5 to 16 per indiv) on MANY ppl (8 indiv)! We also included code to fit encoding models exactly like all @HuthLab papers! Pls read @alex_ander's awesome thread on... 1/2

Our language decoding paper (@AmandaLeBel3 @shaileeejain @alex_ander) is out! We found that it is possible to use functional MRI scans to predict the words that a user was hearing or imagining when the scans were collected nature.com/articles/s4159…

Why are language models so great at encoding brain responses to natural language? In our new paper (bit.ly/3Ua8MLK), we explore two new correlates of encoding performance and their implications on cognitive theories of language processing. (1/12)

very excited to share our paper on reconstructing language from non-invasive brain recordings! we introduce a decoder that takes in fMRI recordings and generates continuous language descriptions of perceived speech, imagined speech, and possibly much more biorxiv.org/content/10.110…

New Dataset Alert! I'm very happy to official announce a naturalistic language fMRI dataset now available! This dataset includes 8 participants listening to 5 hours each of the moth radio hour. biorxiv.org/content/10.110…

Excited to attend my first #SNL2020 today and learn from *real* neuroscientists! Come hear me talk about some new research on **Discovering patterns of semantic integration across the🧠** :) Slide session B (11:30-13:00 hrs PT): 2020.neurolang.org/?p=slides_b

Cool poster from @shaileeejain and @alex_ander using LSTM models to include contextual information in language encoding models! Finally some actual neuroscience @ #NeurIPS2018

It was the first preprint, now it’s gonna be the first paper from our lab!

ayy it's the first preprint from my lab! 🎉 @shaileeejain uses LSTM language models to generate features that incorporate context & are better at predicting brain activity than word embeddings twitter.com/biorxiv_neursc…