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Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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Monotone Quantifiers Emerge via Iterated Learning.

Fausto Carcassi1, Shane Steinert-Threlkeld2, Jakub Szymanik1

  • 1Department of Linguistics, University of Amsterdam.

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PubMed
Summary
This summary is machine-generated.

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.

Keywords:
Cultural evolutionGeneralized quantifiersIterated learningNeural networksSemantic universals

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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.