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Published on: October 14, 2021
Comprehension of simple quantifiers: empirical evaluation of a computational model
Jakub Szymanik1, Marcin Zajenkowski
1Institute for Logic, Language and Computation, University of Amsterdam Department of Psychology, University of Warsaw.
Cognitive Science
|May 14, 2011
Summary
This study links computational complexity to how humans understand quantifiers. Findings show that the brain processes quantifiers differently based on their computational complexity, supporting cognitive constraints on language.
Area of Science:
- Cognitive Science
- Computational Linguistics
- Psycholinguistics
Background:
- Investigates the computational verification of natural language quantifiers.
- Builds upon existing neuropsychological studies of quantifier processing.
- Addresses the cognitive reality of computational complexity in language.
Purpose of the Study:
- To provide empirical evidence linking computational complexity predictions with cognitive processes.
- To extend the experimental settings of previous neuropsychological investigations.
- To explore the psychological relevance of computational distinctions between quantifiers.
Main Methods:
- Empirical study comparing processing times for different quantifier types.
- Analysis of quantifier verification using computational models (finite-automata, push-down automata).
- Comparison of computational complexity predictions with cognitive performance data.
Main Results:
- Demonstrates a significant link between computational complexity and cognitive processing time for quantifiers.
- Shows that the distinction between finite-automata and push-down automata quantifiers is psychologically relevant.
- Provides empirical support for the computational complexity hypothesis in language understanding.
Conclusions:
- Human linguistic abilities are constrained by computational complexity.
- Computational models offer valuable insights into the cognitive mechanisms of language.
- Neuroimaging hypotheses can be refined and explained through computational complexity.
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