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Interpreting Quantifier Scope Ambiguity: Evidence of Heuristic First, Algorithmic Second Processing

Overview of attention for article published in PLOS ONE, November 2013
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Title
Interpreting Quantifier Scope Ambiguity: Evidence of Heuristic First, Algorithmic Second Processing
Published in
PLOS ONE, November 2013
DOI 10.1371/journal.pone.0081461
Pubmed ID
Authors

Veena D. Dwivedi

Abstract

The present work suggests that sentence processing requires both heuristic and algorithmic processing streams, where the heuristic processing strategy precedes the algorithmic phase. This conclusion is based on three self-paced reading experiments in which the processing of two-sentence discourses was investigated, where context sentences exhibited quantifier scope ambiguity. Experiment 1 demonstrates that such sentences are processed in a shallow manner. Experiment 2 uses the same stimuli as Experiment 1 but adds questions to ensure deeper processing. Results indicate that reading times are consistent with a lexical-pragmatic interpretation of number associated with context sentences, but responses to questions are consistent with the algorithmic computation of quantifier scope. Experiment 3 shows the same pattern of results as Experiment 2, despite using stimuli with different lexical-pragmatic biases. These effects suggest that language processing can be superficial, and that deeper processing, which is sensitive to structure, only occurs if required. Implications for recent studies of quantifier scope ambiguity are discussed.

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The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
Germany 1 3%
Unknown 35 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 18%
Student > Bachelor 5 13%
Professor > Associate Professor 4 11%
Student > Doctoral Student 3 8%
Researcher 3 8%
Other 6 16%
Unknown 10 26%
Readers by discipline Count As %
Linguistics 14 37%
Psychology 5 13%
Neuroscience 2 5%
Computer Science 2 5%
Medicine and Dentistry 2 5%
Other 2 5%
Unknown 11 29%