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Monotone Quantifiers Emerge via Iterated Learning
Fausto Carcassi1, Shane Steinert-Threlkeld2, Jakub Szymanik1
1Department of Linguistics, University of Amsterdam.
Cognitive Science
|August 11, 2021
Summary
Semantic universals, like monotonicity, are common across languages. This study explains the emergence of monotonicity in quantifiers through cultural evolution using neural network agents in an iterated learning model.
Area of Science:
- Linguistics
- Cognitive Science
- Evolutionary Biology
Background:
- Natural languages share semantic universals.
- The monotonicity universal is a prominent semantic property.
- Previous work linked monotonicity to learnability.
Purpose of the Study:
- To explain the emergence of the monotonicity universal.
- To provide a cultural evolution perspective on quantifier development.
- To investigate reliable quantifier evolution in iterated learning.
Main Methods:
- Utilized an iterated learning paradigm.
- Employed neural networks as agents.
- Simulated the cultural evolution of quantifiers.
Main Results:
- Quantifiers satisfying the monotonicity universal evolved reliably.
- Demonstrated the emergence of monotonicity through cultural evolution.
- Neural network agents successfully evolved monotonic quantifiers.
Conclusions:
- Cultural evolution provides a robust explanation for the monotonicity universal.
- The iterated learning model with neural networks reliably produces monotonic quantifiers.
- This research bridges semantic universals, learnability, and cultural evolution.
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