Illusions in Humans and AI: How Visual Perception Aligns and Diverges

• URL: http://arxiv.org/abs/2508.12422v1
• Date: Sun, 17 Aug 2025 16:12:54 GMT
• Title: Illusions in Humans and AI: How Visual Perception Aligns and Diverges
• Authors: Jianyi Yang, Junyi Ye, Ankan Dash, Guiling Wang,
• Abstract summary: By comparing biological and artificial perception through the lens of illusions, we highlight critical differences in how each system constructs visual reality.<n>Visual illusions expose how human perception is based on contextual assumptions rather than raw sensory data.<n>This article explores how AI responds to classic visual illusions that involve color, size, shape, and motion.
• Score: 14.661957041103404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: By comparing biological and artificial perception through the lens of illusions, we highlight critical differences in how each system constructs visual reality. Understanding these divergences can inform the development of more robust, interpretable, and human-aligned artificial intelligence (AI) vision systems. In particular, visual illusions expose how human perception is based on contextual assumptions rather than raw sensory data. As artificial vision systems increasingly perform human-like tasks, it is important to ask: does AI experience illusions, too? Does it have unique illusions? This article explores how AI responds to classic visual illusions that involve color, size, shape, and motion. We find that some illusion-like effects can emerge in these models, either through targeted training or as by-products of pattern recognition. In contrast, we also identify illusions unique to AI, such as pixel-level sensitivity and hallucinations, that lack human counterparts. By systematically comparing human and AI responses to visual illusions, we uncover alignment gaps and AI-specific perceptual vulnerabilities invisible to human perception. These findings provide insights for future research on vision systems that preserve human-beneficial perceptual biases while avoiding distortions that undermine trust and safety.

Related papers

• Adopting a human developmental visual diet yields robust, shape-based AI vision [0.0]
  Despite years of research, a striking misalignment between artificial intelligence (AI) systems and human vision persists.<n>We take inspiration from how human vision develops from early infancy into adulthood.<n>We show that guiding AI systems through this human-inspired curriculum produces models that closely align with human behaviour.
  arXiv  Detail & Related papers  (2025-07-03T20:52:08Z)
• Do Large Vision-Language Models Distinguish between the Actual and Apparent Features of Illusions? [12.157632635072435]
  Humans are susceptible to optical illusions, which serve as valuable tools for investigating sensory and cognitive processes.<n>Research has begun exploring whether machines, such as large vision language models (LVLMs), exhibit similar susceptibilities to visual illusions.
  arXiv  Detail & Related papers  (2025-06-06T05:47:50Z)
• Do you see what I see? An Ambiguous Optical Illusion Dataset exposing limitations of Explainable AI [4.58733012283457]
  We introduce a novel dataset of optical illusions featuring intermingled animal pairs designed to evoke perceptual ambiguity.<n>We identify generalizable visual concepts, particularly gaze direction and eye cues, as subtle yet impactful features that significantly influence model accuracy.<n>Our findings underscore the importance of concepts in visual learning and provide a foundation for studying bias and alignment between human and machine vision.
  arXiv  Detail & Related papers  (2025-05-27T12:22:59Z)
• Emergent Active Perception and Dexterity of Simulated Humanoids from Visual Reinforcement Learning [69.71072181304066]
  We introduce Perceptive Dexterous Control (PDC), a framework for vision-driven whole-body control with simulated humanoids.<n>PDC operates solely on egocentric vision for task specification, enabling object search, target placement, and skill selection through visual cues.<n>We show that training from scratch with reinforcement learning can produce emergent behaviors such as active search.
  arXiv  Detail & Related papers  (2025-05-18T07:33:31Z)
• IllusionBench+: A Large-scale and Comprehensive Benchmark for Visual Illusion Understanding in Vision-Language Models [56.34742191010987]
  Current Visual Language Models (VLMs) show impressive image understanding but struggle with visual illusions.<n>We introduce IllusionBench, a comprehensive visual illusion dataset that encompasses classic cognitive illusions and real-world scene illusions.<n>We design trap illusions that resemble classical patterns but differ in reality, highlighting issues in SOTA models.
  arXiv  Detail & Related papers  (2025-01-01T14:10:25Z)
• The Art of Deception: Color Visual Illusions and Diffusion Models [55.830105086695]
  Recent studies have shown that artificial neural networks (ANNs) can also be deceived by visual illusions.<n>We show how visual illusions are encoded in diffusion models.<n>We also show how to generate new unseen visual illusions in realistic images using text-to-image diffusion models.
  arXiv  Detail & Related papers  (2024-12-13T13:07:08Z)
• When Does Perceptual Alignment Benefit Vision Representations? [76.32336818860965]
  We investigate how aligning vision model representations to human perceptual judgments impacts their usability. We find that aligning models to perceptual judgments yields representations that improve upon the original backbones across many downstream tasks. Our results suggest that injecting an inductive bias about human perceptual knowledge into vision models can contribute to better representations.
  arXiv  Detail & Related papers  (2024-10-14T17:59:58Z)
• Grounding Visual Illusions in Language: Do Vision-Language Models Perceive Illusions Like Humans? [28.654771227396807]
  Vision-Language Models (VLMs) are trained on vast amounts of data captured by humans emulating our understanding of the world. Do VLMs have the similar kind of illusions as humans do, or do they faithfully learn to represent reality? We build a dataset containing five types of visual illusions and formulate four tasks to examine visual illusions in state-of-the-art VLMs.
  arXiv  Detail & Related papers  (2023-10-31T18:01:11Z)
• Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images [66.20578637253831]
  There is a growing concern that the advancement of artificial intelligence (AI) technology may produce fake photos. This study aims to comprehensively evaluate agents for distinguishing state-of-the-art AI-generated visual content.
  arXiv  Detail & Related papers  (2023-04-25T17:51:59Z)
• The Who in XAI: How AI Background Shapes Perceptions of AI Explanations [61.49776160925216]
  We conduct a mixed-methods study of how two different groups--people with and without AI background--perceive different types of AI explanations. We find that (1) both groups showed unwarranted faith in numbers for different reasons and (2) each group found value in different explanations beyond their intended design.
  arXiv  Detail & Related papers  (2021-07-28T17:32:04Z)
• Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense [142.53911271465344]
  We argue that the next generation of AI must embrace "dark" humanlike common sense for solving novel tasks. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense.
  arXiv  Detail & Related papers  (2020-04-20T04:07:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.