On the Mathematical Relationship Between Contextual Probability and N400 Amplitude.

Accounts of human language comprehension propose different mathematical relationships between the contextual probability of a word and how difficult it is to process, including linear, logarithmic, and super-logarithmic ones. However, the empirical evidence favoring any of these over the others is mixed, appearing to vary depending on the index of processing difficulty used and the approach taken to calculate contextual probability. To help disentangle these results, we focus on the mathematical relationship between corpus-derived contextual probability and the N400, a neural index of processing difficulty.

Strong Prediction: Language Model Surprisal Explains Multiple N400 Effects.

Theoretical accounts of the N400 are divided as to whether the amplitude of the N400 response to a stimulus reflects the extent to which the stimulus was predicted, the extent to which the stimulus is semantically similar to its preceding context, or both. We use state-of-the-art machine learning tools to investigate which of these three accounts is best supported by the evidence. GPT-3, a neural language model trained to compute the conditional probability of any word based on the words that precede it, was used to operationalize contextual predictability.

Ignoring the alternatives: The N400 is sensitive to stimulus preactivation alone.

The N400 component of the event-related brain potential is a neural signal of processing difficulty. In the language domain, it is widely believed to be sensitive to the degree to which a given word or its semantic features have been preactivated in the brain based on the preceding context. However, it has also been shown that the brain often preactivates many words in parallel.