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Updated: Jul 20, 2026
13:51
Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
Word frequency effects in high-dimensional co-occurrence models: A new approach
1Department of Psychology, University of Alberta, Edmonton, Alberta, Canada. cyrus.shaoul@ualberta.ca
Behavior Research Methods
|September 8, 2006
Summary
The Hyperspace Analog to Language (HAL) model was enhanced to predict human word recognition. The new HiDEx system improves lexical decision reaction times by refining word co-occurrence measures.
Area of Science:
- Computational linguistics
- Cognitive psychology
- Psycholinguistics
Background:
- The Hyperspace Analog to Language (HAL) model uses global word co-occurrence to represent lexical semantics.
- Understanding word relationships is crucial for modeling human language processing.
Purpose of the Study:
- To extend the HAL model with improved measures of word co-occurrence.
- To investigate the predictive power of these enhanced measures on human lexical decision reaction times.
Main Methods:
- Implemented the High Dimensional Explorer (HiDEx) system, enhancing the HAL model.
- HiDEx removes orthographic frequency effects from distance calculations.
- HiDEx calculates NCount (number of neighbors) within a specified distance.
Main Results:
- Enhanced HAL model (HiDEx) provides refined measures of word neighborhood density.
- Word neighborhood density measures from HiDEx reliably predict human lexical decision reaction times.
- The removal of orthographic frequency improves the predictive accuracy of the model.
Conclusions:
- The HiDEx system offers a more accurate computational model of lexical semantics.
- Refined word neighborhood density is a key factor in predicting human word recognition.
- This approach advances computational models of psycholinguistics and language processing.
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