Mapping Timescales of Cortical Language Processing

Natural language contains information that must be integrated over multiple timescales. To understand how the human brain represents this information, one approach is to build encoding models that predict fMRI responses to natural language using representations extracted from neural network language models (LMs). However, these LM-derived representations do not explicitly separate information at different timescales, making it difficult to interpret the encoding models. Here I will discuss how a language model can be engineered to explicitly represent different timescales, and how this model can be used to map representations in the human cortex.