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Related Concept Videos

Language Development01:22

Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Experience is Instrumental in Tuning a Link Between Language and Cognition: Evidence from 6- to 7- Month-Old Infants' Object Categorization
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One model for the learning of language.

Yuan Yang1, Steven T Piantadosi2

  • 1College of Computing, Georgia Institute of Technology, Atlanta, GA 30332.

Proceedings of the National Academy of Sciences of the United States of America
|January 25, 2022
PubMed
Summary

This study introduces an unconstrained learning system capable of acquiring natural language structures from positive evidence alone. The model successfully identifies latent generative systems across diverse formal languages, demonstrating robust language acquisition capabilities.

Keywords:
computational linguisticsformal language theorylearning theoryprogram induction

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Area of Science:

  • Computational Linguistics
  • Cognitive Science
  • Machine Learning

Background:

  • Understanding the learning systems capable of acquiring natural language is a central goal in linguistics and cognitive science.
  • Previous research suggested that language acquisition necessitates highly constrained hypothesis spaces due to computational demands.

Purpose of the Study:

  • To describe and demonstrate a maximally unconstrained learning system that can acquire natural language structures.
  • To show that such a system can learn from positive evidence alone, challenging previous assumptions.

Main Methods:

  • Developed a learning system operating over the space of all computations.
  • Tested the model's ability to induce latent generative systems from positive evidence across 74 distinct formal languages.
  • Included languages from regular, context-free, and context-sensitive complexity classes, as well as those studied in experimental work.

Main Results:

  • The unconstrained learning model successfully acquired key structures present in natural language from positive evidence in almost all cases.
  • Demonstrated successful induction of latent systems for regular, context-free, and context-sensitive formal languages.
  • Showcased the model's ability to learn from small amounts of data, including languages from experimental studies.

Conclusions:

  • Relatively small amounts of positive evidence are sufficient for learning rich classes of generative computations over structures.
  • The developed model offers an idealized framework for formalizing additional cognitive constraints and biases in language acquisition.