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The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
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The cognitive roots of regularization in language.
Vanessa Ferdinand1, Simon Kirby2, Kenny Smith2
1Santa Fe Institute, United States; Centre for Language Evolution, University of Edinburgh, United Kingdom.
Cognition
|December 21, 2018
Summary
Cognitive load and linguistic stimuli independently cause regularization, reducing language variability. Entropy measures better detect this regularization than frequency analyses, impacting language evolution predictions.
Area of Science:
- Cognitive Science
- Linguistics
- Computational Linguistics
Background:
- Regularization in language learning describes output variability being less than input variability.
- Cognitive biases can influence how learners process and produce linguistic data.
Purpose of the Study:
- To investigate independent sources of regularization bias in artificial language learning.
- To determine if cognitive load and linguistic stimuli differentially affect regularization.
- To assess the efficacy of entropy measures versus frequency analyses in identifying regularization.
Main Methods:
- An artificial language learning experiment was conducted.
- Two sources of regularization bias were examined: domain-general (cognitive load) and domain-specific (linguistic stimuli).
- Entropy reduction was used to formalize and measure regularization.
Main Results:
- Both cognitive load and linguistic stimuli independently modulate frequency information encoding and production.
- Only production-side modulations lead to regularization, reducing output variability.
- Entropy measures proved more effective than frequency-based analyses for identifying regularization behavior.
Conclusions:
- Cognitive constraints interact with cultural evolution in complex ways.
- While learning biases inform language evolution, micro-level regularization processes do not directly predict macro-level linguistic regularity.
- Future models of cultural transmission should account for these nuanced interactions.
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