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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 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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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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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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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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How Visually Literate Are Large Language Models? Reflections on Recent Advances and Future Directions.

Alexander Bendeck, John Stasko, Rahul C Basole

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    |November 11, 2025
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    Summary
    This summary is machine-generated.

    Large language models (LLMs) show promise in creating and interpreting data visualizations, but face challenges in usability and analytical tasks compared to humans.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Artificial Intelligence

    Background:

    • Large language models (LLMs) are increasingly utilized for visualization tasks.
    • These models demonstrate potential for "visual literacy" in both generation and interpretation.

    Purpose of the Study:

    • To review the current state of LLM visualization literacy.
    • To explore future directions for LLM applications in data visualization.

    Main Methods:

    • Analysis of LLM capabilities in visualization generation (e.g., natural language interfaces for authoring).
    • Evaluation of LLM performance in visualization interpretation (e.g., answering questions, synthesizing information, detecting misleading designs).

    Main Results:

    • LLMs show potential in generating visualizations via natural language but have usability issues.
    • Models can perform basic interpretation tasks but struggle with complex analytics and human-like understanding.
    • Discrepancies exist between LLM and human interpretation of visualizations.

    Conclusions:

    • LLMs offer advancements in visualization generation and interpretation.
    • Further research is needed to address limitations in analytical tasks and human-aligned understanding.
    • Future work should focus on improving LLM visualization literacy for more robust applications.