Neural evidence for Bayesian trial-by-trial adaptation on the N400 during semantic priming

Abstract

When semantic information is activated by a context prior to new bottom-up input (i.e. when a word is predicted), semantic processing of that incoming word is typically facilitated, attenuating the amplitude of the N400 event related potential (ERP) - a direct neural measure of semantic processing. N400 modulation is observed even when the context is a single semantically related "prime" word. This so-called "N400 semantic priming effect" is sensitive to the probability of encountering a related prime-target pair within an experimental block, suggesting that participants may be adapting the strength of their predictions to the predictive validity of their broader experimental environment. We formalize this adaptation using a Bayesian learning model that estimates and updates the probability of encountering a related versus an unrelated prime-target pair on each successive trial. We found that our model's trial-by-trial estimates of target word probability accounted for significant variance in trial-by-trial N400 amplitude. These findings suggest that Bayesian principles contribute to how comprehenders adapt their semantic predictions to the statistical structure of their broader environment, with implications for the functional significance of the N400 component and the predictive nature of language processing.

Keywords: Adaptation; Expected uncertainty; Language comprehension; N400; Precision; Prediction; Unexpected uncertainty.

Figure 1.

Sample beliefs μ (probability of a related trial) over the course of Block 2 of the experiment at different values of precision parameter ν. Higher precision (i.e. more certainty in prior beliefs) leads to slower adaptation to the new environment.

Figure 2.

N400 amplitudes over trials for related and unrelated trials, averaged over all participant data in Block 2.

Figure 3.

The output of the rational adaptor model for each participant, calculated based on their idiosyncratic trial ordering in Block 2

Figure 4.

Log-likelihoods of fitted regression models testing the rational adaptor model with different prior values of the precision parameter ν. Larger (i.e. less negative) log-likelihoods indicate better fit. A prior strength of ν = 77 optimizes the fit of the rational adaptor model to the empirical N400 data.