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A Multiple-Channel Model of Task-Dependent Ambiguity Resolution in Sentence Comprehension
Pavel Logačev1, Shravan Vasishth1
1Department of Linguistics, University of Potsdam.
Cognitive Science
|April 1, 2015
Summary
The ambiguity advantage in sentence comprehension is challenged. New research shows ambiguous sentences can be read slower than unambiguous ones, suggesting task-dependent parsing strategies influence reading speed.
Area of Science:
- Psycholinguistics
- Cognitive Science
- Computational Linguistics
Background:
- The ambiguity advantage suggests ambiguous sentences are read faster than unambiguous ones.
- Classical sentence comprehension theories struggle to explain this phenomenon.
- The unrestricted race model (URM) posits non-deterministic parsing.
Purpose of the Study:
- To re-evaluate the unrestricted race model (URM) against task-dependent parsing models.
- To investigate whether task demands influence sentence comprehension strategies.
- To develop a quantitative model of task-driven disambiguation.
Main Methods:
- Simulations were used to test the URM against existing data.
- A German self-paced reading study was conducted to gather new experimental evidence.
- A novel quantitative model was developed and tested.
Main Results:
- Simulations indicated that existing data supporting task-dependent parsing could also be explained by the URM.
- Experimental results demonstrated that ambiguous sentences can be read slower than unambiguous ones under specific conditions.
- The developed quantitative model successfully explained both previous and new findings.
Conclusions:
- The URM is not necessarily invalidated by previous findings.
- Evidence supports the claim that sentence comprehension can be task-dependent.
- A unified quantitative model accounts for both URM predictions and task-driven effects in parsing.
Keywords:
AmbiguityCognitive modelingGood-enough processingParallel processingSentence processingURMUnderspecificationUnrestricted race modelMore Related Videos
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