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Related Concept Videos

Language and Cognition01:27

Language and Cognition

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.
Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
Components of Language01:24

Components of Language

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. “eh”). Phonemes combine to...

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Related Experiment Video

Updated: May 21, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

Large language models pass a standard three-party Turing test.

Cameron R Jones1,2, Benjamin K Bergen1

  • 1Department of Cognitive Science, University of California San Diego, La Jolla, CA 92093.

Proceedings of the National Academy of Sciences of the United States of America
|May 19, 2026
PubMed
Summary
This summary is machine-generated.

Advanced artificial intelligence systems, like GPT-4.5, can now pass the Turing test when prompted to act human. Human evaluators focused on socio-emotional cues, not just intelligence, when distinguishing humans from AI.

Keywords:
AITuring testhuman-AI interactionlarge language modelssocial cognition

Related Experiment Videos

Last Updated: May 21, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

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

Published on: December 6, 2024

Area of Science:

  • Artificial Intelligence
  • Cognitive Science
  • Human-Computer Interaction

Background:

  • The Turing test is a benchmark for machine intelligence, assessing a machine's ability to exhibit human-like conversation.
  • Distinguishing between human and machine intelligence remains a key challenge in AI research.
  • Previous evaluations of AI conversational abilities have yielded mixed results.

Purpose of the Study:

  • To empirically evaluate the performance of leading large language models (LLMs) in Turing tests.
  • To assess whether AI systems can be perceived as human by human evaluators under specific conditions.
  • To investigate the criteria humans use when differentiating between humans and AI in conversation.

Main Methods:

  • Conducted two randomized, controlled, preregistered Turing tests with independent participant populations.
  • Participants engaged in simultaneous 5-minute conversations with a human and an AI system (ELIZA, GPT-4o, LLaMa-3.1-405B, GPT-4.5).
  • Evaluated AI performance with and without a "PERSONA" prompt designed to elicit humanlike behavior, and replicated findings in 15-minute games.

Main Results:

  • GPT-4.5, when prompted to adopt a human persona, was identified as human 73% of the time, surpassing human performance.
  • LLaMa-3.1 achieved a 56% "human" identification rate with the persona prompt.
  • Without persona prompts, GPT-4.5 and LLaMa-3.1 performed significantly worse, with identification rates of 38% and 36%, respectively, similar to baseline models.

Conclusions:

  • Modern AI systems, particularly when prompted, can empirically pass a standard three-party Turing test.
  • Human judgment in these tests relies more on socio-emotional and stylistic factors than traditional intelligence metrics.
  • These findings have significant implications for understanding AI capabilities, societal impact, and the uniqueness of human behavior.