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Related Concept Videos

Language and Cognition01:27

Language and Cognition

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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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Language Development01:22

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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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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
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Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Predictability and Variation in Language Are Differentially Affected by Learning and Production.

Aislinn Keogh1, Simon Kirby1, Jennifer Culbertson1

  • 1Centre for Language Evolution, University of Edinburgh.

Cognitive Science
|April 2, 2024
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Summary

Cognitive working memory limits impact language regularization. Taxing working memory during language production causes variation loss, while learning biases better explain predictable changes in artificial languages.

Keywords:
Artificial language learningLanguage evolutionLanguage productionRegularizationUrn modelWorking memory

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Area of Science:

  • Cognitive Science
  • Linguistics
  • Psychology

Background:

  • Human cognition, particularly working memory, influences language structure.
  • Languages tend to lose complex or difficult structures over time.
  • Regularization, the reduction of linguistic variability, is a documented language change process.

Purpose of the Study:

  • To investigate the role of working memory limitations in driving linguistic regularization.
  • To determine if working memory constraints during learning or production influence regularization.
  • To explore the mechanisms underlying the regularization of artificial languages.

Main Methods:

  • An artificial language learning experiment was conducted.
  • Working memory was experimentally taxed during either language learning or production phases.
  • Computational modeling was used to analyze the observed linguistic variation and predictability.

Main Results:

  • Taxing working memory during language production led to the complete loss of linguistic variation.
  • Learning biases, rather than working memory limitations during learning, better explained the predictability of variation.
  • A computational model suggested a self-priming mechanism for the production effect.

Conclusions:

  • Working memory limitations during language production can eliminate variation.
  • Learning biases are a more significant driver of predictable regularization than working memory constraints.
  • The findings contribute to understanding the cognitive underpinnings of language change.