Scaling laws for language encoding models in fMRI
Authors: name: Richard Antonello, name: Aditya Vaidya, name: 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 language models to brains have used GPT-2 or
similarly sized language models. Here we tested whether larger open-source
models such as those from the OPT and LLaMA families are better at predicting
brain responses recorded using fMRI. Mirroring scaling results from other
contexts, we found that brain prediction performance scales log-linearly with
model size from 125M to 30B parameter models, with ~15% increased encoding
performance as measured by correlation with a held-out test set across 3
subjects. Similar log-linear behavior was observed when scaling the size of the
fMRI training set. We also characterized scaling for acoustic encoding models
that use HuBERT, WavLM, and Whisper, and we found comparable improvements with
model size. A noise ceiling analysis of these large, high-performance encoding
models showed that performance is nearing the theoretical maximum for brain
areas such as the precuneus and higher auditory cortex. These results suggest
that increasing scale in both models and data will yield incredibly effective
models of language processing in the brain, enabling better scientific
understanding as well as applications such as decoding.