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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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Higher Mental Functions of the Brain: Language01:10

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
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Components of Language01:24

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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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Language01:16

Language

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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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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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The Representativeness Heuristic02:13

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The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.
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Related Experiment Video

Updated: Jan 15, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Discourse-Aware Language Representation.

Zhuosheng Zhang, Siru Ouyang, Hai Zhao

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |October 14, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a discourse-aware framework to improve conversational reading comprehension by modeling sentence relationships beyond linear sequences. The approach enhances understanding of complex interactions within text for better model performance.

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    Area of Science:

    • Natural Language Processing
    • Artificial Intelligence
    • Computational Linguistics

    Background:

    • Transformer models typically process text sequentially, limiting their ability to capture complex inter-sentence relationships.
    • Existing methods often overlook the rich interactive structures inherent in natural language discourse.

    Purpose of the Study:

    • To develop a discourse-aware framework for enhanced conversational reading comprehension.
    • To bridge the gap between sequential text modeling and the interactive nature of dialogues.

    Main Methods:

    • Dividing context into elementary discourse units (EDUs).
    • Implementing sequential EDU encoding, discourse-aware masking, and discourse graph networks.
    • Evaluating on multi-turn response selection, conversational question answering, and machine reading tasks.

    Main Results:

    • The proposed discourse-aware framework significantly outperforms existing methods.
    • The approach effectively captures intricate discourse relationships and improves reasoning interpretability.
    • The method demonstrates efficacy across various backbone pre-trained language models (PLMs) and domains.

    Conclusions:

    • Discourse-aware modeling is crucial for advancing conversational reading comprehension.
    • The framework offers a more nuanced understanding of text interactions, leading to superior performance.
    • This approach provides a foundation for more interpretable and robust natural language understanding systems.