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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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Related Experiment Video

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Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing.

Bozena Pajak1, Alex B Fine2, Dave F Kleinschmidt3

  • 1Duolingo, Inc.

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Summary
This summary is machine-generated.

This study introduces a new framework for second and additional language (L2/Ln) acquisition. It proposes that socio-indexical information guides language learning by inferring hierarchical structures, unifying first (L1) and L2/Ln processing.

Keywords:
hierarchical probabilistic inferencesecond language acquisitionspeech adaptationstatistical learning

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Area of Science:

  • Psycholinguistics
  • Second Language Acquisition
  • Sociolinguistics

Background:

  • First language (L1) processing relies on implicit probabilistic knowledge of how linguistic categories covary with socio-indexical variables.
  • Socio-indexical information, such as talker identity and dialect, influences language use and perception.

Purpose of the Study:

  • To propose a novel framework for second and additional language (L2/Ln) acquisition.
  • To integrate L1 processing and L2/Ln acquisition under a unified probabilistic inference model.
  • To explain crosslinguistic influences in L2/Ln learning.

Main Methods:

  • Reviewing evidence for socio-indexical knowledge in L1 processing.
  • Proposing a model where L2/Ln learning infers hierarchical language structures using socio-indexical cues.
  • Describing how this structure guides acquisition and is revised by input.

Main Results:

  • L2/Ln acquisition is framed as probabilistic inference over socio-indexical structure.
  • A unified model for L1 processing and L2/Ln acquisition is presented.
  • The framework accommodates gradient and continuous crosslinguistic transfer.

Conclusions:

  • Socio-indexical information is crucial for both L1 processing and L2/Ln acquisition.
  • Language learning involves inferring and revising hierarchical structures based on social and linguistic input.
  • This perspective offers new insights into the mechanisms of multilingualism.