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Evaluating the random representation assumption of lexical semantics in cognitive models
Brendan T Johns1, Michael N Jones
1Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, USA. johns4@indiana.edu
Psychonomic Bulletin & Review
|November 2, 2010
Summary
Cognitive models often assume random word representations, but this study finds semantic similarity is skewed, not symmetric. This challenges models relying on random word meanings and impacts cognitive research conclusions.
Area of Science:
- Cognitive Science
- Computational Linguistics
- Psycholinguistics
Background:
- Cognitive models frequently use random representations to approximate lexical semantics.
- This approach assumes semantic similarity is symmetrically distributed across words.
Purpose of the Study:
- To evaluate the assumption of symmetric semantic similarity distribution in cognitive models.
- To compare real semantic similarity distributions with those generated by random representations.
Main Methods:
- Computed similarity distributions for randomly selected words using established semantic measures.
- Compared these distributions against those derived from random representations used in cognitive models.
Main Results:
- Semantic similarity distributions from all tested measures were positively skewed.
- This contrasts with the symmetric normal distributions assumed by random representations.
Conclusions:
- The assumption of symmetric semantic similarity in random representations is likely false.
- This finding may have significant implications for cognitive process models that rely on such representations.
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