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Related Concept Videos

Visual System01:26

Visual System

1.8K
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.
Once through the pupil, the light passes through the lens, a...
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Visual Agnosia01:12

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Visual agnosia is a condition characterized by the inability to recognize visually presented objects despite having normal vision. For instance, a person with visual agnosia can describe the shape and color of an object but cannot identify or name it. This impairment does not affect their visual field, acuity, color vision, brightness discrimination, language, or memory. An example of this condition in a social setting is someone at a dinner party asking for "that silver thing with a round...
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Photoreceptors and Visual Pathways01:22

Photoreceptors and Visual Pathways

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At the molecular level, visual signals trigger transformations in photopigment molecules, resulting in changes in the photoreceptor cell's membrane potential. The photon's energy level is denoted by its wavelength, with each specific wavelength of visible light associated with a distinct color. The spectral range of visible light, classified as electromagnetic radiation, spans from 380 to 720 nm. Electromagnetic radiation wavelengths exceeding 720 nm fall under the infrared category,...
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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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Communication01:03

Communication

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Communication between two animals occurs when one animal transmits an information signal that causes a change in the animal that receives the information. Organisms communicate with one another in a host of different ways. Signals can be auditory, chemical, visual, tactile, or a combination of these. Communication is a critical behavioral adaptation that promotes survival, growth, and reproduction.
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What is a Sensory System?01:31

What is a Sensory System?

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Sensory systems detect stimuli—such as light and sound waves—and transduce them into neural signals that can be interpreted by the nervous system. In addition to external stimuli detected by the senses, some sensory systems detect internal stimuli—such as the proprioceptors in muscles and tendons that send feedback about limb position.
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Related Experiment Video

Updated: Feb 8, 2026

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
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Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

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Disambiguating Visual Verbs.

Spandana Gella, Frank Keller, Mirella Lapata

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |July 11, 2018
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces visual verb sense disambiguation, a new task for matching verb senses to images. Multimodal embeddings excel with unannotated images, while textual embeddings perform well with available annotations.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Natural Language Processing

    Background:

    • Textual word sense disambiguation is crucial for NLP.
    • Multimodal AI requires understanding verb actions in images.

    Purpose of the Study:

    • Introduce visual verb sense disambiguation.
    • Develop models and a dataset (VerSe) for this task.
    • Evaluate performance with and without image annotations.

    Main Methods:

    • Created the VerSe dataset by augmenting COCO and TUHOI.
    • Explored supervised and unsupervised models.
    • Utilized textual, visual, and multimodal embeddings.
    • Investigated a two-stage approach: verb detection then sense disambiguation.

    Main Results:

    • Textual embeddings are effective with gold-standard annotations.
    • Multimodal embeddings show strong performance on unannotated images.
    • The VerSe dataset facilitates research in visual verb sense disambiguation.

    Conclusions:

    • Visual verb sense disambiguation is a viable and important task.
    • Model performance varies based on data availability (annotated vs. unannotated).
    • The VerSe dataset provides a valuable resource for future research.