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Information density and dependency length as complementary cognitive models
1, Norfolk, VA, USA, mxcollins@gmail.com.
Journal of Psycholinguistic Research
|October 1, 2013
Summary
Syntactic alternations in English are easier to process when they follow principles of uniform information density and minimize dependency length, according to psycholinguistic models and user preference data.
Area of Science:
- Psycholinguistics
- Computational Linguistics
- Cognitive Science
Background:
- English exhibits syntactic alternations, allowing variations in sentence structure.
- Understanding the cognitive mechanisms behind preferences for certain alternations is crucial.
Purpose of the Study:
- Investigate the relationship between syntactic alternations and cognitive processing difficulty.
- Evaluate the predictive power of psycholinguistic models on human preferences for alternations.
Main Methods:
- Review of three models: Maximum Per Word Surprisal, Uniform Information Density (UID), and Dependency Length Minimization (DLM).
- Experiment using Amazon Mechanical Turk to gather native speaker judgments on syntactic alternation naturalness.
- Logistic regression analysis to identify predictors of human preferences.
Main Results:
- Both Uniform Information Density (UID) and Dependency Length Minimization (DLM) significantly predict human preferences for syntactic alternations.
- The study identified key factors influencing the ease of processing for different syntactic constructions.
Conclusions:
- Syntactic alternations that align with uniform information density and minimize dependency length are processed more easily.
- These findings contribute to understanding the cognitive underpinnings of language processing and syntactic variation.
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