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Updated: Jun 2, 2026

A Semantic Priming Event-related Potential (ERP) Task to Study Lexico-semantic and Visuo-semantic Processing in Autism Spectrum Disorder

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Published on: April 12, 2018

Entropy, semantic relatedness and proximity.

Lance W Hahn¹, Robert M Sivley

¹Department of Psychology, Western Kentucky University, 1906 College Heights Blvd. #21030, Bowling Green, KY 42101, USA. Lance.Hahn@WKU.edu

Behavior Research Methods

|May 11, 2011

Summary

This summary is machine-generated.

Local word interactions, previously overlooked, are computationally analyzed using information theory. Findings reveal semantic meaning in word pairs and reduced language entropy in sequences, offering new insights into communication.

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Published on: May 9, 2019

Area of Science:

Cognitive Psychology
Computational Linguistics
Information Theory

Background:

Psychological research has largely ignored local word interactions due to practical limitations.
Information theory provides a framework for understanding the utility of word interactions in communication.
Advances in computation enable the analysis of word-word interactions in large text corpora.

Purpose of the Study:

To computationally examine local word-word interactions using information theory.
To investigate the relationship between semantic associativity and word pair probabilities.
To analyze the entropy of words and word sequences in language.

Main Methods:

Utilized Brants and Franz's (2006) dataset to compute conditional probabilities for 62,474 word pairs.
Calculated entropy for 9,917 words from Nelson, McEvoy, and Schreiber's (2004) free association norms.
Correlated semantic associativity with computed probabilities and word entropy.

Main Results:

Semantic associativity showed a moderate correlation with word pair probabilities, particularly for non-adjacent words.
A correlation was observed between the number of semantic associates for a word and its entropy.
Language entropy decreases significantly from single words (11 bits) to four-word sequences (6 bits per word).

Conclusions:

Local word interactions contain valuable semantic information, supporting information theory's principles.
Computational methods can overcome previous practical challenges in studying word interactions.
The study quantifies language entropy reduction in sequences, offering insights into linguistic structure and communication efficiency.