LeBel, A. | Search bioRxiv

bioRxiv2021

Voxelwise encoding models show that cerebellar language representations are highly conceptual

There is a growing body of research demonstrating that the cerebellum is involved in language understanding. Early theories assumed that the cerebellum is involved in low-level language processing. However, those theories are at odds with recent work demonstrating cerebellar activation during cognitive tasks. Using natural language stimuli and an encoding model framework, we performed an fMRI experiment where subjects passively listened to five hours of natural language stimuli which allowed us to analyze language processing in the cerebellum with higher precision than previous work. We used this data to fit voxelwise encoding models with five different feature spaces that span the hierarchy of language processing from acoustic input to high-level conceptual processing. Examining the prediction performance of these models on separate BOLD data shows that cerebellar responses to language are almost entirely explained by high-level conceptual language features rather than low-level acoustic or phonemic features. Additionally, we found that the cerebellum has a higher proportion of voxels that represent social semantic categories, which include "social" and "people" words, and lower representations of all other semantic categories, including "mental", "concrete", and "place" words, than cortex. This suggests that the cerebellum is representing language at a conceptual level with a preference for social information. Significance StatementRecent work has demonstrated that, beyond its typical role in motor planning, the cerebellum is implicated in a wide variety of tasks including language. However, little is known about the language representations in the cerebellum, or how those representations compare to cortex. Using voxelwise encoding models and natural language fMRI data, we demonstrate here that language representations are significantly different in the cerebellum as compared to cortex. Cerebellum language representations are almost entirely semantic, and the cerebellum contains over-representation of social semantic information as compared to cortex. These results suggest that the cerebellum is not involved in language processing per se, but cognitive processing more generally.

neuroscience↗

bioRxiv2020

Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech

Natural language contains information at 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. In this work we construct interpretable multi-timescale representations by forcing individual units in an LSTM LM to integrate information over specific temporal scales. This allows us to explicitly and directly map the timescale of information encoded by each individual fMRI voxel. Further, the standard fMRI encoding procedure does not account for varying temporal properties in the encoding features. We modify the procedure so that it can capture both short- and long-timescale information. This approach outperforms other encoding models, particularly for voxels that represent long-timescale information. It also provides a finer-grained map of timescale information in the human language pathway. This serves as a framework for future work investigating temporal hierarchies across artificial and biological language systems.

neuroscience↗