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Related Experiment Video

Updated: May 5, 2026

Investigating the Effects of Antipsychotics and Schizotypy on the N400 Using Event-Related Potentials and Semantic Categorization

Investigating the Effects of Antipsychotics and Schizotypy on the N400 Using Event-Related Potentials and Semantic Categorization

Published on: November 19, 2014

Strong Prediction: Language Model Surprisal Explains Multiple N400 Effects.

James A Michaelov¹, Megan D Bardolph¹, Cyma K Van Petten²

¹Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA.

Neurobiology of Language (Cambridge, Mass.)

|April 22, 2024

Summary

This summary is machine-generated.

This study reveals that word predictability, not just semantic similarity, best explains the N400 brain response during language processing. Machine learning models confirm predictability is key to understanding language comprehension.

Keywords:

ERPs N400 distributional semantics neural language models predictive coding

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Published on: May 9, 2019

Area of Science:

Cognitive Neuroscience
Computational Linguistics
Psycholinguistics

Background:

The N400 brain response is a key indicator of language processing.
Existing theories debate whether N400 amplitude reflects prediction, semantic similarity, or both.

Purpose of the Study:

To investigate which theoretical account of the N400 is best supported by empirical evidence.
To differentiate the roles of predictability and semantic similarity in N400 generation.

Main Methods:

Utilized GPT-3, a large language model, to quantify word predictability (surprisal).
Employed GloVe and fastText vector representations to measure contextual semantic similarity.
Constructed regression models predicting single-trial N400 amplitudes from these linguistic variables.

Main Results:

GPT-3 surprisal emerged as the strongest predictor of N400 amplitude.
Statistical model comparison favored predictability over semantic similarity alone.
N400 effects attributed to expectancy, plausibility, and semantic similarity were unified under predictability.

Conclusions:

Word predictability is the primary driver of N400 amplitude.
Findings support predictive coding theories within the human language network.
This research offers a unified account of factors influencing the N400.