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Related Concept Videos

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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Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of information more...
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Related Experiment Video

Updated: Jul 19, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
05:48

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis

Published on: August 9, 2024

Rapid learning of syllable classes from a perceptually continuous speech stream.

Ansgar D Endress1, Luca L Bonatti

  • 1Laboratoire de Sciences Cognitives et Psycholinguistique, EHESS-ENS-CNRS, Paris, France. ansgar.endress@polytechnique.org

Cognition
|November 7, 2006
PubMed
Summary

Participants can learn artificial language structure from brief exposure, suggesting distinct rapid and slow learning mechanisms. This challenges purely associative learning theories and supports dual-process models for speech perception.

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Last Updated: Jul 19, 2026

Memorization-Based Training and Testing Paradigm for Robust Vocal Identity Recognition in Expressive Speech Using Event-Related Potentials Analysis
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Area of Science:

  • Cognitive Science
  • Psycholinguistics
  • Computational Neuroscience

Background:

  • Language acquisition requires learning word structures and dependencies from fluent speech.
  • Existing learning theories offer different predictions regarding the extraction of linguistic rules.

Purpose of the Study:

  • To investigate how humans learn word structure and syllable dependencies from artificial speech streams.
  • To differentiate between associative learning and dual-mechanism models of speech processing.

Main Methods:

  • Familiarization with a short, artificial, subliminally bracketed speech stream.
  • Analysis of participants' sensitivity to word structure and syllable co-occurrence statistics.
  • Comparison of results with predictions from associative theories and dual-mechanism models.
  • Validation through neural network simulations.

Main Results:

  • Participants learned word structure, including syllable classes in specific positions, even from brief exposure.
  • Preference for structurally correct items decreased with longer familiarization, supporting a dual-mechanism model.
  • Participants demonstrated sensitivity to co-occurrence statistics among non-adjacent syllables.
  • Neural network simulations struggled to replicate findings with purely associative schemes.

Conclusions:

  • Speech processing involves at least two distinct on-line learning mechanisms: a rapid one for structural information and a slower one for statistical regularities.
  • Findings challenge purely associative learning models and provide evidence for separate mechanisms in speech structure extraction.
  • The study highlights the brain's capacity for complex statistical computations during language acquisition.