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Language and Cognition01:27

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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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Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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Natural language processing and the Now-or-Never bottleneck.

Carlos Gómez-Rodríguez1

  • 1LyS (Language and Information Society) Research Group,Departamento de Computación,Universidade da Coruña,Campus de Elviña,15071, A Coruña,Spain.cgomezr@udc.eshttp://www.grupolys.org/~cgomezr.

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New natural language processing models align with the Now-or-Never bottleneck framework, improving efficiency for web-scale data. This research bridges computational linguistics and cognitive science, supporting a key theory of human language processing.

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Area of Science:

  • Computational Linguistics
  • Cognitive Science
  • Natural Language Processing

Background:

  • The need for efficient natural language processing (NLP) tools to manage vast web-scale data is critical.
  • Existing NLP models face challenges in processing large datasets effectively.
  • The Now-or-Never bottleneck framework offers a theoretical model for human language processing.

Purpose of the Study:

  • To develop and evaluate novel NLP models.
  • To assess the alignment of these models with the Now-or-Never bottleneck framework.
  • To explore the intersection of computational linguistics and cognitive science.

Main Methods:

  • Development of advanced computational models for natural language processing.
  • Empirical testing of model performance against web-scale datasets.
  • Comparative analysis of model features with predictions from the Now-or-Never bottleneck framework.

Main Results:

  • The developed NLP models demonstrated a remarkable match with the expected features of human language processing under the Now-or-Never framework.
  • Model efficiency in handling web-scale data was significantly improved.
  • The findings provide empirical support for the Now-or-Never bottleneck framework.

Conclusions:

  • The study validates the Now-or-Never bottleneck framework as a relevant model for understanding human language processing.
  • The research highlights the potential of integrating computational linguistics with cognitive science for advancing NLP.
  • The developed models offer a more efficient approach to processing large-scale language data.