Bayesian Modeling of Language-Evoked Event-Related Potentials

Davide Turco*, Conor Houghton

*Corresponding author for this work

Research output: Contribution to conferenceConference Paper

7 Downloads (Pure)


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.
Original languageEnglish
Publication statusPublished - 28 Aug 2022
Event2022 Conference on Cognitive Computational Neuroscience - San Francisco, United States
Duration: 25 Aug 202228 Aug 2022


Conference2022 Conference on Cognitive Computational Neuroscience
Abbreviated titleCCN 2022
Country/TerritoryUnited States
CitySan Francisco
Internet address


Dive into the research topics of 'Bayesian Modeling of Language-Evoked Event-Related Potentials'. Together they form a unique fingerprint.

Cite this