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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.

Visual System

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Related Experiment Video

Updated: Apr 21, 2026

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

Why vision is not both hierarchical and feedforward.

Michael H Herzog¹, Aaron M Clarke¹

¹Laboratory of Psychophysics, Brain, Mind Institute, École Polytechnique Fédérale de Lausanne Lausanne, Switzerland.

Frontiers in Computational Neuroscience

|November 7, 2014

Summary

Object recognition is not solely based on basic features. Figural aspects and element grouping significantly influence low-level visual processing, challenging traditional filtering models.

Keywords:

Gestalt Verniers crowding feedback object recognition

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Area of Science:

Cognitive Neuroscience
Computational Vision
Psychology

Background:

Classical object recognition models rely on hierarchical, feedforward processing of basic visual features.
These models assume low-level processing is independent and information lost at early stages is irretrievable.
Despite success, these models fail to fully explain general object recognition.

Purpose of the Study:

To investigate the influence of global figural aspects on low-level visual processing.
To demonstrate how performance on individual elements depends on the entire visual scene configuration.
To challenge the classical filtering approach in vision models.

Main Methods:

Experimental investigation of visual perception.
Analysis of how element grouping affects performance.
Comparison of findings with Gestalt principles and classical filtering models.

Main Results:

Figural aspects significantly influence low-level visual processing, reversing the classical assumption.
Performance on single visual elements is contingent upon all other elements in the scene.
Element grouping across the visual field is crucial for determining individual element performance.

Conclusions:

Object recognition is not strictly bottom-up; global configurations shape low-level processing.
The classical filtering approach is insufficient for explaining complex object recognition.
Future vision models must incorporate holistic and grouping principles for accurate object perception.