# Bayesian Modeling of Language-Evoked Event-Related Potentials

arXiv: https://arxiv.org/abs/2207.03392 · HTML: https://mlsanity.com/paper/2207.03392/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 7 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Pointer Sentinel Mixture Models](https://mlsanity.com/paper/1609.07843/) (2016)
- [Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC](https://mlsanity.com/paper/1507.04544/) (2015)
- [Understanding language-elicited EEG data by predicting it from a fine-tuned language model](https://mlsanity.com/paper/1904.01548/) (2019)

## Built on by (most cited first, 1 of 1)

- [Investigating the Timescales of Language Processing with EEG and Language Models](https://mlsanity.com/paper/2406.19884/) (2024)

## Abstract (arXiv)

Bayesian hierarchical models are well-suited to analyzing the often noisy data from electroencephalography experiments in cognitive neuroscience: these models provide an intuitive framework to account for structures and correlations in the data, and they allow a straightforward handling of uncertainty. In a typical neurolinguistic experiment, event-related potentials show only very small effect sizes and frequentist approaches to data analysis fail to establish the significance of some of these effects. Here, we present a Bayesian approach to analyzing event-related potentials using as an example data from an experiment which relates word surprisal and neural response. Our model is able to estimate the effect of word surprisal on most components of the event-related potential and provides a richer description of the data. The Bayesian framework also allows easier comparison between estimates based on surprisal values calculated using different language models.