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

Gestalt Principles of Perception01:21

Gestalt Principles of Perception

389
Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
389
Association Areas of the Cortex01:21

Association Areas of the Cortex

6.0K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
6.0K
Vision01:24

Vision

55.1K
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.
55.1K
Visual System01:26

Visual System

657
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...
657
Perceptual Constancy01:12

Perceptual Constancy

500
Perceptual constancy is the ability to recognize that objects remain consistent and unchanged even when their appearance varies due to changes in sensory input. There are four main types of perceptual constancy: size constancy, shape constancy, color constancy, and brightness constancy.
Size constancy is the recognition that an object remains the same size, even when its image on the retina changes. For instance, a bus is perceived to be large enough to carry people, even if it looks tiny from...
500
Visual Agnosia01:12

Visual Agnosia

275
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...
275

You might also read

Related Articles

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

Sort by
Same author

Contrastive learning explains the emergence and function of visual category-selective regions.

Science advances·2024
Same author

Immersive scene representation in human visual cortex with ultra-wide-angle neuroimaging.

Nature communications·2024
Same author

The neural code for "face cells" is not face-specific.

Science advances·2023
Same author

Cortical topographic motifs emerge in a self-organized map of object space.

Science advances·2023
Same author

Immersive scene representation in human visual cortex with ultra-wide angle neuroimaging.

bioRxiv : the preprint server for biology·2023
Same author

The neuroconnectionist research programme.

Nature reviews. Neuroscience·2023
Same journal

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

PLoS computational biology·2026
Same journal

Exploring the structural lexicon of the Proteome via Metric Geometry.

PLoS computational biology·2026
Same journal

Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.

PLoS computational biology·2026
Same journal

CAdir: Joint clustering of cells and genes for single-cell transcriptomics with visualization-driven cluster quality assessment.

PLoS computational biology·2026
Same journal

Systematic design of auxotrophic strains and media conditions to probe metabolic functions in E. coli.

PLoS computational biology·2026
Same journal

Neuronal excitability and parameter variability in the Hodgkin-Huxley model.

PLoS computational biology·2026
See all related articles

Related Experiment Video

Updated: Aug 27, 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

General object-based features account for letter perception.

Daniel Janini1, Chris Hamblin1, Arturo Deza1,2

  • 1Department of Psychology, Harvard University, Cambridge, Massachusetts, United States of America.

Plos Computational Biology
|September 26, 2022
PubMed
Summary
This summary is machine-generated.

Human letter perception likely relies on general visual features, not specialized ones. Our study found that features learned for object recognition better explain how we perceive letters.

More Related Videos

Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K
Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
05:38

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

2.5K

Related Experiment Videos

Last Updated: Aug 27, 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
Generating Strictly Controlled Stimuli for Figure Recognition Experiments
05:39

Generating Strictly Controlled Stimuli for Figure Recognition Experiments

Published on: March 18, 2019

5.3K
Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology
05:38

Interaction between Phonological and Semantic Processes in Visual Word Recognition using Electrophysiology

Published on: June 29, 2021

2.5K

Area of Science:

  • Cognitive Psychology
  • Neuroscience
  • Computer Vision

Background:

  • Humans develop expertise in letter perception with experience.
  • The origin of this expertise is debated: specialized letter features vs. general visual features.

Purpose of the Study:

  • To investigate whether letter perception relies on specialized letter features or general visual features.
  • To determine the neural network features that best correlate with human perceptual similarity of letters.

Main Methods:

  • Behavioral experiments measuring letter perceptual similarity in visual search and categorization tasks.
  • Training deep convolutional neural networks on object categorization (general features) and letter categorization (specialized features).
  • Comparing network feature correlations with human behavioral data.

Main Results:

  • General object-based features from networks showed a stronger correlation with human letter perceptual similarity.
  • Attempts to create specialized letter features by fine-tuning object-trained networks did not improve behavioral correlation.
  • Human letter perception similarity is not explained by specialized letter representations.

Conclusions:

  • Domain-general visual features are likely sufficient to explain letter perception.
  • Specialized letter representations are not necessary to account for perceptual similarity.
  • Letter perception may leverage visual processing developed for broader object recognition.