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Metaphor comprehension: a computational theory.

¹Department of Psychology, University of Colorado, Boulder 80309-0344, USA. wkintsch@psych.colorado.edu

Psychonomic Bulletin & Review

|July 26, 2000

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This study models metaphor comprehension by treating metaphorical predication like literal predication within a semantic space. This framework explains experimental findings like metaphor nonreversibility and priming effects.

Area of Science:

Cognitive Science
Computational Linguistics
Psycholinguistics

Background:

Metaphor comprehension is complex, involving interactions between topic and vehicle terms.
Meaning representation is crucial for understanding these interactions.

Purpose of the Study:

To propose a computational model for metaphor comprehension.
To demonstrate how semantic space and vector operations can model metaphorical predication.

Main Methods:

Representing meaning as vectors in a high-dimensional semantic space.
Modeling predication as a modification of topic vectors using vehicle features.
Evaluating metaphor vectors by comparison within the semantic space.

Main Results:

Related Experiment Videos

The model treats metaphorical predication identically to literal predication.
The framework successfully simulates experimental results like metaphor nonreversibility.
The model accounts for priming effects between metaphors and literal statements.

Conclusions:

A vector-based semantic model can effectively explain metaphor comprehension mechanisms.
Metaphorical and literal predication share underlying computational principles.
This approach provides a testable framework for psycholinguistic research on figurative language.