Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Visual Agnosia01:12

Visual Agnosia

348
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...
348
Prosopagnosia01:24

Prosopagnosia

296
Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
296
Visual System01:26

Visual System

713
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...
713
Anatomy of the Eyeball01:20

Anatomy of the Eyeball

7.6K
The eye is a spherical, hollow structure composed of three tissue layers. The outer layer — the fibrous tunic, comprises the sclera — a white structure — and the cornea, which is transparent. The sclera encompasses some of the ocular surface, most of which is not visible. However, the 'white of the eye' is distinctively visible in humans compared to other species. The cornea, a clear covering at the front of the eye, enables light penetration. The eye's middle...
7.6K
Vision01:24

Vision

55.4K
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.
55.4K
Parallel Processing01:20

Parallel Processing

257
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
257

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Studies with impossible languages falsify LMs as models of human language.

The Behavioral and brain sciences·2026
Same author

Correlations without causation do not support claims of human-LLM reasoning alignment.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

No deep insights into the alignment between human and deep learning reasoning processes: Thoughts on de Varda et al. (2025).

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

Sense-checking the approach to quantitative sensory testing to detect chemotherapy-induced peripheral neuropathy.

PloS one·2025
Same author

The successes and failures of artificial neural networks (ANNs) highlight the importance of innate linguistic priors for human language acquisition.

Psychological review·2025
Same author

Grounding computational cognitive models.

Psychological review·2025
Same journal

Another 10 years of PLOS Computational Biology: A data-driven reflection on trends in genomics research.

PLoS computational biology·2026
Same journal

Mobility data resolution needed to inform predictive models of spatial epidemic spread from mobile phone data.

PLoS computational biology·2026
Same journal

DeepMethylation: A deep learning framework for tissue-specific DNA methylation prediction and functional variant annotation.

PLoS computational biology·2026
Same journal

Redefining and estimating the early-phase reproduction ratio for epidemic outbreaks in spatially structured populations.

PLoS computational biology·2026
Same journal

Optimized phenotype definitions boost GWAS power.

PLoS computational biology·2026
Same journal

Detection, communication, and individual identification with deep audio embeddings: A case study with North Atlantic right whales.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Sep 23, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K

Feature blindness: A challenge for understanding and modelling visual object recognition.

Gaurav Malhotra1, Marin Dujmović1, Jeffrey S Bowers1

  • 1School of Psychological Sciences, University of Bristol, Bristol, United Kingdom.

Plos Computational Biology
|May 13, 2022
PubMed
Summary
This summary is machine-generated.

Humans exhibit strong shape-bias due to cognitive constraints, unlike Convolutional Neural Networks (CNNs), which learn based on environmental statistics. This difference highlights how prior biases shape human object recognition.

More Related Videos

A Method for Investigating Change Blindness in Pigeons Columba Livia
06:14

A Method for Investigating Change Blindness in Pigeons Columba Livia

Published on: September 7, 2018

6.5K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

578

Related Experiment Videos

Last Updated: Sep 23, 2025

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

11.9K
A Method for Investigating Change Blindness in Pigeons Columba Livia
06:14

A Method for Investigating Change Blindness in Pigeons Columba Livia

Published on: September 7, 2018

6.5K
Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss
07:12

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

578

Area of Science:

  • Cognitive Science
  • Computer Vision
  • Neuroscience

Background:

  • Humans heavily rely on object shape for recognition.
  • Convolutional Neural Networks (CNNs) can exhibit shape-bias, leading to proposals of them as mechanistic models of human visual processing.
  • The source of shape-bias in humans versus CNNs remains debated: cognitive constraints versus environmental statistics.

Purpose of the Study:

  • To investigate the underlying reasons for shape-bias in humans and CNNs.
  • To compare how humans and CNNs learn and select features in a novel environment.
  • To determine the role of prior cognitive biases versus environmental statistics in object recognition.

Main Methods:

  • Humans and CNNs were tested on their ability to recognize objects in a novel learning environment.
  • Behavioral observations focused on feature selection, diagnostic utility, and learning with/without global shape features.
  • CNNs were tested with and without pre-training for shape-bias and with frozen convolutional backbones.

Main Results:

  • Humans prioritized shape over more diagnostic non-shape features and struggled when global shape was absent, indicating strong prior biases.
  • CNNs favored non-shape features and increased their reliance on them as they became more predictive, even when pre-trained for shape-bias.
  • Human feature selection was strongly influenced by pre-existing biases, contrasting with CNNs' statistical learning.

Conclusions:

  • Shape-bias in humans stems from cognitive constraints and prior biases, influencing feature selection in novel environments.
  • CNNs' shape-bias is primarily driven by the statistical properties of the learning environment, not inherent cognitive biases.
  • The findings suggest significant differences in the mechanisms underlying human and artificial visual object recognition.