mlsanityData to 7 Oct 2026

A perceptual bias of AI Logical Argumentation Ability in Writing

arXiv 2511.22151 ↗First posted 27 Nov 2025cs.AIcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

Can machines think? This is a central question in artificial intelligence research. However, there is a substantial divergence of views on the answer to this question. Why do people have such significant differences of opinion, even when they are observing the same real world performance of artificial intelligence? The ability of logical reasoning like humans is often used as a criterion to assess whether a machine can think. This study explores whether human biases influence evaluations of the reasoning abilities of AI. An experiment was conducted where participants assessed two texts on the same topic, one AI generated and one human written,to test for perceptual biases in evaluating logical reasoning. Based on the experimental findings, a questionnaire was designed to quantify the attitudes toward AI.The results reveal a bias in perception. The evaluations of the logical reasoning ability of AI generated texts are significantly influenced by the preconceived views on the logical reasoning abilities of AI. Furthermore, frequent AI users were less likely to believe that AI usage undermines independent thinking.This study highlights the need to address perceptual biases to improve public understanding of AI's capabilities and foster better human AI interactions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 8 of its 37 references that are in the map. First posted on arXiv 27 Nov 2025.

Builds on

8 of 37 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks5 Jul
  2. 2023Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models24 May
  3. 2023Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate22 May
  4. 2023Evaluating the Logical Reasoning Ability of ChatGPT and GPT-47 Apr
  5. 2022Towards Reasoning in Large Language Models: A Survey20 Dec

Other references · 3

  1. 2024GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Modelscited by 533
  2. 2024The Curious Case of Nonverbal Abstract Reasoning with Multi-Modal Large Language Modelscited by 17
  3. 2024Public Perception of AI: Sentiment and Opportunitycited by 2

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Close in wording, no citation either way

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  4. 2024Heuristics and Biases in AI Decision-Making: Implications for Responsible AGI
  5. 2023Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases
  6. 2023Studying and improving reasoning in humans and machines
  7. 2024The Cognitive Capabilities of Generative AI: A Comparative Analysis with Human Benchmarks
  8. 2026Psychological Competence as a Missing Dimension in AI Evaluation