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Language and Cognition01:27

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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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Rational Emotive Behavior Therapy01:24

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Cognitive-behavioral therapies (CBTs) are grounded in the belief that our thoughts profoundly influence our emotions and actions. Advocates of CBT emphasize three core assumptions: first, that cognitions are identifiable and measurable; second, that they are central to psychological functioning; and third, that irrational or maladaptive beliefs can be replaced with rational and adaptive ones. This transformative approach to therapy has paved the way for specific models such as Albert...
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Cognitivism01:17

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Cognitive psychology emerged as a significant field in the mid-20th century. It focused on understanding humans' internal mental processes. This approach emphasizes how people perceive, remember, think, and solve problems—elements critical to human cognition.
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Language01:16

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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.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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Reason and Intuition01:37

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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Related Experiment Video

Updated: Jun 18, 2025

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

Published on: December 6, 2024

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(Ir)rationality and cognitive biases in large language models.

Olivia Macmillan-Scott1, Mirco Musolesi1,2

  • 1Department of Computer Science, University College London, London, UK.

Royal Society Open Science
|August 5, 2024
PubMed
Summary

Large language models (LLMs) exhibit irrationality in reasoning tasks, similar to humans. However, their errors and inconsistencies differ from human biases, indicating unique model limitations.

Keywords:
cognitive biaslarge language modelsrationality

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Area of Science:

  • Cognitive Psychology
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Large language models (LLMs) are known to inherit human biases from training data.
  • The extent to which LLMs exhibit rational reasoning, and how it compares to human rationality, is not well understood.

Purpose of the Study:

  • To investigate whether large language models (LLMs) display rational reasoning.
  • To compare the reasoning patterns of LLMs with human cognitive biases using established psychological tasks.

Main Methods:

  • Evaluated seven different large language models (LLMs).
  • Utilized tasks commonly found in cognitive psychology literature to assess reasoning.
  • Analyzed the nature of errors and inconsistencies in LLM responses.

Main Results:

  • LLMs demonstrate irrationality in reasoning tasks, mirroring human performance in some aspects.
  • LLM irrationality manifests differently from human cognitive biases, with errors often unique to the models.
  • LLMs exhibit significant response inconsistency, adding another layer of irrationality.

Conclusions:

  • LLMs do not replicate human-like irrationality in reasoning tasks.
  • LLM reasoning is characterized by unique error patterns and notable response inconsistency.
  • The study proposes a methodological framework for assessing and comparing LLM capabilities, specifically rational reasoning.