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Exploring the dynamics of Shannon's information and iconicity in language processing and lexeme evolution
Alexander Kilpatrick1, Rikke L Bundgaard-Nielsen2
1Center for Language Research, University of Aizu, Aizuwakamatsu, Japan.
Plos One
|April 29, 2025
Summary
This study reveals how word structure impacts cognitive processing and language evolution. More predictable word sounds, like those in iconic words, are easier to process, influencing how language changes over time.
Area of Science:
- Psycholinguistics
- Cognitive Linguistics
- Computational Linguistics
Background:
- Iconic words, possessing form-meaning resemblance, offer cognitive processing advantages.
- The Iconic Treadmill Hypothesis suggests evolving iconic words move towards arbitrariness.
- Existing models like Cognitive Load Theory and Lossy Context Surprisal explain sentence-level processing.
Purpose of the Study:
- To investigate the interplay between information content (Shannon's information) and word form (iconicity) in American English.
- To test predictions regarding cognitive processing and lexeme evolution using bigram surprisal.
- To extend sentence-level processing models to the bigram level for psycholinguistic analysis.
Main Methods:
- Calculating phonemic bigram surprisal in a large spoken American English corpus.
- Cross-referencing surprisal data with iconicity ratings of words.
- Applying and extending sentence-level language processing models (Cognitive Load Theory, Lossy Context Surprisal Model) to bigram analysis.
Main Results:
- Words with improbable phonemic sequences (high surprisal) incur a cognitive processing disadvantage.
- Despite initial processing difficulty, improbable words show enhanced long-term memory retention.
- Predictability of bigram sequences in evolving words may offset cognitive costs of processing arbitrary words.
Conclusions:
- Cognitive limitations in language processing significantly shape lexeme evolution.
- The balance between information predictability and processing effort influences word retention and change.
- Findings contribute to understanding the cognitive underpinnings of language change and structure.
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