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Word meaning is both categorical and continuous.

Sean Trott1, Benjamin Bergen1

  • 1Department of Cognitive Science, University of California San Diego.

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Word meanings are understood through both discrete categories and continuous representations. New hybrid theories integrating these views best explain behavioral data, suggesting word meaning is both categorical and continuous.

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Area of Science:

  • Cognitive Science
  • Psycholinguistics
  • Computational Linguistics

Background:

  • Traditional theories of word meaning fall into two camps: categorical (discrete senses) and continuous (trajectories in state space).
  • Both categorical and continuous theories face empirical challenges in explaining how humans process word meaning.

Purpose of the Study:

  • To introduce and test novel hybrid theories of word meaning that integrate discrete and continuous representations.
  • To reconcile the dynamic, context-dependent nature of word meaning with evidence for category-like structures in lexical knowledge.

Main Methods:

  • Developed two novel hybrid theories of word meaning.
  • Conducted two behavioral experiments.
  • Employed an analytical approach using neural language models to evaluate competing theories.

Main Results:

  • Experimental results were best explained by a hybrid account combining discrete sense representations with a continuous meaning space.
  • This hybrid model successfully accommodates both context-dependent meaning shifts and categorical lexical structures.
  • Computational implementations of the hybrid account demonstrated significant predictive power.

Conclusions:

  • Human word meaning is best characterized as both categorical and continuous.
  • Hybrid models integrating discrete and gradient representations offer a more comprehensive explanation for lexical ambiguity.
  • Future research should investigate the emergence of discrete sense representations and their role in cognitive processes.