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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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Related Experiment Video

Updated: Aug 6, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

The debate over understanding in AI's large language models.

Melanie Mitchell¹, David C Krakauer¹

¹Santa Fe Institute, Santa Fe, NM 87501.

Proceedings of the National Academy of Sciences of the United States of America

|March 21, 2023

Summary

Large language models (LLMs) are debated for their humanlike understanding. This research explores arguments for and against LLM comprehension, proposing an extended science of intelligence to analyze distinct cognitive modes.

Keywords:

artificial intelligence large language models understanding

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Area of Science:

Artificial Intelligence (AI)
Cognitive Science
Philosophy of Mind

Background:

A significant debate exists within the AI research community regarding the extent to which large pretrained language models (LLMs) exhibit genuine understanding.
This debate centers on whether LLMs comprehend language in a humanlike manner, including the physical and social contexts that language represents.

Purpose of the Study:

To survey the current arguments for and against the claim that large pretrained language models understand language and its encoded contexts.
To identify key questions for the broader sciences of intelligence prompted by this debate.
To propose the development of an extended science of intelligence capable of analyzing diverse modes of understanding.

Main Methods:

Literature review and synthesis of arguments presented in the artificial intelligence research community.
Analysis of the implications of LLM capabilities for the broader scientific study of intelligence.
Conceptual framework development for an extended science of intelligence.

Main Results:

The study outlines the core tenets of the debate surrounding LLM understanding, presenting both supportive and critical viewpoints.
It highlights emergent questions concerning the nature of intelligence, understanding, and cognition in both biological and artificial systems.
The research identifies the need for a more comprehensive framework to study different forms of understanding.

Conclusions:

An extended science of intelligence can offer valuable insights into the distinct modes of understanding, their respective strengths, and limitations.
This extended science will be crucial for addressing the complex challenge of integrating diverse cognitive capabilities, both human and artificial.
The findings suggest a path forward for a more nuanced understanding of artificial intelligence and its relationship to general intelligence.