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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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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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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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Sensation typically is the process by which the sensory receptors and sense organs detect stimuli from the internal and external environment and transmit this information to the central nervous system for processing.
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Related Experiment Video

Updated: Jan 16, 2026

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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A Survey on Video Temporal Grounding With Multimodal Large Language Model.

Jianlong Wu, Wei Liu, Ye Liu

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    Multimodal large language models (MLLMs) are advancing video temporal grounding (VTG) for better video understanding. This survey reviews VTG-MLLMs, covering their roles, training, and feature processing.

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    Area of Science:

    • Artificial Intelligence
    • Computer Vision
    • Natural Language Processing

    Background:

    • Video temporal grounding (VTG) is crucial for fine-grained video understanding.
    • Multimodal large language models (MLLMs) show superior comprehension and reasoning, driving advancements in VTG.
    • Existing surveys lack focus on the specific advancements and applications of VTG-MLLMs.

    Purpose of the Study:

    • To systematically review and categorize current research on VTG-MLLMs.
    • To provide a comprehensive overview of the field, addressing a gap in existing literature.
    • To analyze the functional roles, training paradigms, and feature processing techniques in VTG-MLLMs.

    Main Methods:

    • A three-dimensional taxonomy is used: MLLM functional roles, training paradigms, and video feature processing.
    • Analysis of architectural significance, temporal reasoning strategies, and spatiotemporal representation effectiveness.
    • Discussion of benchmark datasets, evaluation protocols, and empirical findings.

    Main Results:

    • VTG-MLLMs surpass traditional fine-tuned methods in performance and generalization (zero-shot, multi-task, multi-domain).
    • MLLMs' architectural roles, training strategies, and feature processing significantly impact VTG effectiveness.
    • Current research demonstrates the growing capabilities and potential of MLLM-driven VTG.

    Conclusions:

    • VTG-MLLMs represent a significant leap in fine-grained video understanding.
    • Further research is needed to address existing limitations and explore promising future directions.
    • This survey provides a foundational framework for understanding and advancing VTG-MLLM research.