20 citations · 23 across the 5 of their papers we have counts for
6 papers · 1 filter
Fine-tuning language encoding models on slow fMRI improves prediction for fast ECoG
Aditya R. Vaidya, Richard J. Antonello, Alexander G. Huth
Neuroscientists have recently turned to intracranial brain recording methods, like electrocorticography (ECoG), for human experiments because of the fine spatial and temporal resol…
Abstraction Induces the Brain Alignment of Language and Speech Models
Emily Cheng, Aditya R. Vaidya, Richard Antonello
Research has repeatedly demonstrated that intermediate hidden states extracted from large language models and speech audio models predict measured brain response to natural languag…
BrainWavLM: Fine-tuning Speech Representations with Brain Responses to Language
Nishitha Vattikonda, Aditya R. Vaidya, Richard J. Antonello +1
Speech encoding models use auditory representations to predict how the human brain responds to spoken language stimuli. Most performant encoding models linearly map the hidden stat…
Humans and language models diverge when predicting repeating text
Aditya R. Vaidya, Javier Turek, Alexander G. Huth
Language models that are trained on the next-word prediction task have been shown to accurately model human behavior in word prediction and reading speed. In contrast with these fi…
Scaling laws for language encoding models in fMRI
Richard Antonello, Aditya Vaidya, Alexander G. Huth
Representations from transformer-based unidirectional language models are known to be effective at predicting brain responses to natural language. However, most studies comparing l…
Self-supervised models of audio effectively explain human cortical responses to speech
Aditya R. Vaidya, Shailee Jain, Alexander G. Huth
Self-supervised language models are very effective at predicting high-level cortical responses during language comprehension. However, the best current models of lower-level audito…