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Related Concept Videos

Vision

Vision

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.

Encoding

Encoding

Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...

Visual System

Visual System

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...

Reconstruction of Signal using Interpolation

Reconstruction of Signal using Interpolation

Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...

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Related Experiment Video

Updated: Oct 6, 2025

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

Published on: August 1, 2018

An image reconstruction framework for characterizing initial visual encoding.

Ling-Qi Zhang¹, Nicolas P Cottaris¹, David H Brainard¹

¹Department of Psychology, University of Pennsylvania, Philadelphia, United States.

|January 17, 2022

Summary

This summary is machine-generated.

We created a visual encoding model using Bayesian reconstruction to analyze how the eye processes images. This model quanties information loss and simulates visual perception, aiding studies on the visual front end.

Keywords:

bayesian model color vision computational biology early vision human ideal observer image statistics neuroscience peripheral vision systems biology

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Last Updated: Oct 6, 2025

Investigating Object Representations in the Macaque Dorsal Visual Stream Using Single-unit Recordings

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Published on: August 1, 2018

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Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Area of Science:

Computational neuroscience
Vision science
Image processing

Background:

The visual system's initial encoding is complex, involving retinal cone mosaic excitations.
Previous models often focus on ideal observer analysis and psychophysical discrimination.
Understanding the visual front end requires accounting for environmental statistical regularities.

Purpose of the Study:

To develop an image-computable observer model for initial visual encoding.
To provide a unifying framework for analyzing the visual front end.
To quantify information loss and simulate visual perception.

Main Methods:

Bayesian image reconstruction from retinal cone mosaic excitations.
Utilizing error in reconstructions as a metric for analysis.
Simulating color deficiencies and interferometric experiments.

Main Results:

Analyzed photoreceptor type variations as an optimal design problem.
Visualized and quantified information loss due to optics and cone sampling across eccentricity.
Reconstructed images served as a proxy for subject percepts in simulations.
Identified interactions between spatial frequency and chromatic direction in contrast sensitivity.

Conclusions:

The developed model offers a powerful tool for studying the visual front end.
It enables quantification of information loss and simulation of visual perception.
The model is applicable to diverse experiments and applications in vision science.