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Interactive property attribution in concept combination.

Z Estes1, S Glucksberg

  • 1Department of Psychology, Princeton University, NJ 08544-1010, USA. zcestes@princeton.edu

Memory & Cognition
|March 14, 2000
PubMed
Summary

People understand noun-noun compounds by interacting properties, not just concept similarity. Our interactive property attribution model better explains how compound meaning is derived.

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Area of Science:

  • Cognitive Psychology
  • Linguistics
  • Computational Linguistics

Background:

  • Attributive noun-noun compounds are common in language.
  • Existing models focus on constituent concept similarity for interpretation.

Purpose of the Study:

  • To propose and test an alternative model for understanding noun-noun compounds.
  • To investigate the role of interactive property attribution versus constituent similarity.

Main Methods:

  • Empirically contrasted alignment-and-comparison models with an interactive property attribution model.
  • Held constituent similarity constant while manipulating feature salience and dimension relevance.

Main Results:

  • Compounds with high feature salience-relevance interaction were interpreted via property attribution.
  • Compounds lacking this interaction, despite similar constituent similarity, were not.
  • The interactive property attribution model showed higher predictive accuracy.

Conclusions:

  • The interaction between modifier features and head dimensions is crucial for interpreting noun-noun compounds.
  • This challenges the primacy of constituent similarity in compound interpretation.
  • An interactive property attribution model offers a more accurate account.

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