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Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
Published on: November 30, 2018
Putting lexical constraints in context into the visual-world paradigm.
Jared M Novick1, Sharon L Thompson-Schill, John C Trueswell
1Department of Psychology and Institute for Research in Cognitive Science, University of Pennsylvania, Suite 302C, 3401 Walnut Street, Philadelphia, PA 19104, USA. jnovick@casl.umd.edu
Cognition
|February 19, 2008
Summary
Listeners sometimes misinterpret ambiguous sentences, favoring visual cues over word meaning. Individual differences in processing style influence how people resolve these ambiguities in spoken language.
Area of Science:
- Psycholinguistics
- Cognitive Science
- Computational Linguistics
Background:
- Prior eye-tracking studies suggest visual context can override lexical bias in spoken sentence comprehension.
- Constraint-based parsing theories predict uncertainty when lexical and visual cues conflict.
Purpose of the Study:
- To re-examine how listeners process ambiguous sentences with conflicting lexical and visual information.
- To investigate the role of individual differences in sentence processing strategies.
Main Methods:
- Eye-tracking during spoken sentence comprehension with manipulated referents and spatial cues (Experiment 1).
- A reading study assessing ambiguity resolution strategies (Experiment 2).
- Comparison of performance between eye-tracking and reading tasks.
Main Results:
- Listeners only rapidly converged on the intended referent when the modifier phrase was unambiguous.
- Ambiguous trials led to transient consideration of competitor referents.
- Individuals relying on lexical bias in reading showed greater sensitivity to both lexical and contextual cues in the eye-tracking task.
Conclusions:
- Spoken sentence processing is influenced by a dynamic interplay of lexical and visual-contextual constraints.
- Individual differences in processing strategies impact ambiguity resolution.
- Findings support multiple-constraint theories of sentence processing and cross-validate methodologies.
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