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Related Concept Videos

Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Concepts and Prototypes01:24

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The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
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Natural and Artificial Concepts01:24

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Visual System01:26

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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Higher Mental Functions of the Brain: Language01:10

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
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Color Vision01:24

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Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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Updated: Jan 16, 2026

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Enhancing Concept-Based Explanation with Vision-Language Models.

Imran Hossain1, Ghada Zamzmi1, Peter Mouton2

  • 1Computer Science and Engineering University of South Florida Tampa, Florida, USA.

Proceedings. IEEE International Symposium on Computer-Based Medical Systems
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method using Vision-Language Models (VLMs) to simplify concept selection for explaining AI models. The approach generates understandable image concepts, matching natural concepts without extra costs.

Keywords:
Concept-based XAIDeep Neural NetworksExplainabilityGenerative Artificial Intelligence

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Area of Science:

  • Artificial Intelligence
  • Machine Learning Explainability
  • Computer Vision

Background:

  • Concept-based approaches are crucial for AI model interpretability but often require expert knowledge for concept identification.
  • Existing methods for concept selection can be complex and inaccessible to non-experts, hindering broader model understanding.
  • Post-hoc explanation methods aim to clarify model decision-making processes after training.

Purpose of the Study:

  • To develop a novel, simplified method for identifying and generating human-readable concepts to explain AI model behavior.
  • To leverage state-of-the-art Vision-Language Models (VLMs) for automated concept selection and transformation.
  • To quantify the importance of generated concepts and evaluate their effectiveness in explaining model predictions.

Main Methods:

  • Utilized a Vision-Language Model (VLM) to select relevant textual concepts describing dataset classes.
  • Employed a text-to-image model to translate selected textual concepts into visually understandable image concepts.
  • Applied directional derivatives and concept activation vectors to measure the influence of generated concepts on model output.
  • Evaluated the method on a neonatal pain classification task.

Main Results:

  • The VLM successfully generated coherent, meaningful, and easily understandable image concepts for non-experts.
  • The generated concepts demonstrated performance comparable to manually selected natural image concepts.
  • The method effectively explained the targeted network's behavior in a post-hoc manner.
  • No additional annotation costs were required for generating the explanatory concepts.

Conclusions:

  • The proposed VLM-based method significantly simplifies concept selection for AI model explanation.
  • This approach makes model interpretability more accessible to non-experts by generating intuitive visual concepts.
  • The method offers a cost-effective and efficient alternative to traditional concept-based explanation techniques.