A new paper co-authored by Jonathan Brennan was published in the Annual Review of Linguistics in January 2022. The paper, Neuro-computational models of language processing, reviews the state-of-the-art in how computational models can be used to understand the brain bases of language.
Read the abstract below.
Efforts to understand the brain bases of language face the Mapping Problem: At what level do linguistic computations and representations connect to human neurobiology? We review one approach to this problem that relies on rigorously defined computational models to specify the links between linguistic features and neural signals. Such tools can be used to estimate linguistic predictions, model linguistic features, and specify a sequence of processing steps that may be quantitatively fit to neural signals collected while participants use language. Progress has been helped by advances in machine learning, attention to linguistically interpretable models, and openly shared data sets that allow researchers to compare and contrast a variety of models. We describe one such data set in detail in the Supplementary Appendix.