Contextual Semantic Relevance and Word Surprisal Predict N400 and P600 Dynamics During Naturalistic Reading
arXiv:2607.04107v2 Announce Type: replace Abstract: Word surprisal is a well-established computational predictor of human neural responses during language comprehension, but it remains less clear whether local semantic fit explains neural response variation beyond lexical expectation during naturalistic reading. Using the Dublin EEG-based Reading Experiment Corpus (DERCo), this study examined whether contextual semantic relevance predicts word-locked EEG activity in the N400 and P600 windows. Contextual semantic relevance was computed as an attention-aware measure of how strongly a target word
This study advances the understanding of how AI-derived measures of language fit predict human brain responses, reflecting ongoing synergy between AI and cognitive neuroscience research.
Improved computational models of language processing that accurately predict human brain activity could lead to more nuanced and effective AI language models and interfaces.
This particular research refines the metrics used to correlate AI language models with human cognitive processes, specifically by incorporating contextual semantic relevance beyond lexical expectation.
- · AI researchers
- · Cognitive neuroscience
- · NLP developers
More accurate predictive models for language comprehension will emerge.
AI systems could become more adept at generating human-like and contextually appropriate language.
These insights might enable more intuitive and less cognitively demanding human-computer interaction based on language.
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Read at arXiv cs.CL