Structural syntactic prediction measured with ELAN: evidence from ERPs

Area of Science:

• Psycholinguistics
• Cognitive Neuroscience
• Computational Linguistics

Background:

• Syntactic structure is crucial for sentence comprehension.
• Filler-gap dependencies pose processing challenges.
• The role of semantic properties like animacy in syntactic parsing is debated.

Purpose of the Study:

• To investigate the timing and mechanism of argument structure information use in sentence processing.
• To determine if animacy, a semantic property, influences syntactic structure building during filler-gap dependency resolution.
• To examine the neural correlates of animacy expectation violations.

Main Methods:

• Utilized event-related potentials (ERPs) to measure brain activity.
• Designed sentences with filler-gap dependencies featuring expected vs. unexpected noun animacy.
• Analyzed ERPs, specifically the Early Left Anterior Negativity (ELAN) and P600 components, time-locked to critical nouns.

Main Results:

• Unexpected noun animacy elicited a significant Early Left Anterior Negativity (ELAN)/P600 complex compared to expected animacy.
• The ELAN component reflected violations of animacy expectations, not just grammatical category errors.
• ERP data indicated sensitivity to animacy properties during syntactic structure formation.

Conclusions:

• The language comprehension system actively predicts semantic properties like animacy.
• Animacy information is integrated early in processing to guide syntactic structure building.
• ERPs provide insights into the predictive nature of human sentence comprehension.