Robust Evaluation of Language–Brain Encoding Experiments

Robust Evaluation of Language–Brain Encoding Experiments


Language-brain encoding experiments evaluate the ability of language models to predict brain responses elicited by language stimuli. The evaluation scenarios for this task have not yet been standardized which makes it difficult to compare and interpret results. We perform a series of evaluation experiments with a consistent encoding setup and compute the results for multiple fMRI datasets. In addition, we test the sensitivity of the evaluation measures to randomized data and analyze the effect of voxel selection methods. Our experimental framework is publicly available to make modelling decisions more transparent and support reproducibility for future comparisons.

to appear in International Journal of Computational Linguistics and Applications

The work has been presented at the “International Conference on Computational Linguistics and Intelligent Text Processing 2019” in La Rochelle, France.

Lisa Beinborn
Assistant Prof for Natural Language Processing