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Does a walk-through video help the parser down the garden-path? A visually enhanced self-paced reading study in Dutch
Sara Shoghi1,2, Seçkin Arslan3, Roelien Bastiaanse4
1International Doctorate in Experimental Approaches to Language and Brain (IDEALAB), University of Groningen, Netherlands/University of Newcastle, United Kingdom/University of Potsdam, Germany and Macquarie University, Sydney, NSW, Australia.
Language parsing integrates context and verb structure. Visual cues influence sentence attachment preferences, but only for verbs with two arguments, showing early integration of information sources.
Area of Science:
- Psycholinguistics
- Cognitive Science
- Computational Linguistics
Background:
- Sentence processing involves assigning structure to unfolding language.
- Debate exists on when different information sources influence parsing decisions.
- Attachment preferences (e.g., high vs. low) are known but their timing is unclear.
Purpose of the Study:
- To investigate the nature of initial parsing decisions in sentence processing.
- To examine how attachment type and verb argument structure interact.
- To determine the role of non-linguistic context in early language analysis.
Main Methods:
- A self-paced reading task was employed using globally ambiguous Dutch sentences.
- Constructions featured double locative prepositional phrases (PPs) with high/low attachment ambiguity.
- Visual context (animation clips) and verb argument structure (2- vs. 3-argument) were manipulated.
Main Results:
- Parsing decisions were modulated by visual context, dependent on verb argument structure.
- Visual context overrode the high attachment preference only for 2-argument verbs.
- High attachment preference persisted for 3-argument verbs, indicated by longer reading times for low attachment.
Conclusions:
- The language processing system actively integrates linguistic and non-linguistic information from initial stages.
- Contextual influence on parsing is modulated by verb argument complexity.
- Findings contribute to understanding serial versus parallel models of sentence processing.
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