Do Feature Representations from Different Language Models Affect Accuracy of Brain Encoding Models' Predictions?

We investigate the impact of feature respresentations derived from different language models on brain encoding models, which are designed to predict brain states from linguistic stimuli. This study aims to determine whether the variances in the feature respresentations of language models, originating from their distinct encoder/decoder architectures, training data quality and quantity, and parameter sizes, affect their predictive accuracy on brain states. By examining how these feature respresentations influence brain encoding models, we identify specific brain regions where the predictability of brain activity is consistently influenced across various models, thereby uncovering similarities in their predictive effectiveness.