We have a new paper reviewing the state-of-the-art in neurocomputational models for sentence understanding. The basic take-aways are: (1) we have made tangible progress on the Mapping Problem linking linguistic constructs to neural signals, (2) This progress is underwritten by interpretable computational models, and (3) progess will only be sustained if we broaden the domain of our models to better match the diversity of the world’s languages.
Hale, J., Campenelli, L., Li, J., Bhattasali, S., Pallier, C., & Brennan, J. (2022). Neuro-computational models of language processing. Annual Review of Linguistics, 8. https://doi.org/10.1146/annurev-linguistics-051421-020803
Abstract:
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.