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.