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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.

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Related Experiment Video

Updated: Oct 15, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Comparing Object Recognition in Humans and Deep Convolutional Neural Networks-An Eye Tracking Study.

Leonard Elia van Dyck^1,2, Roland Kwitt³, Sebastian Jochen Denzler¹

¹Department of Psychology, University of Salzburg, Salzburg, Austria.

Frontiers in Neuroscience

|October 25, 2021

Summary

This summary is machine-generated.

Deep convolutional neural networks (DCNNs) and human vision show differences in spatial processing. A biologically plausible DCNN (vNet) better matches human viewing behavior than standard ResNet, influenced by image content.

Keywords:

brain deep neural network eye tracking object recognition saliency map seeing vision

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Area of Science:

Computer Vision
Neuroscience
Cognitive Science

Background:

Deep convolutional neural networks (DCNNs) and the human ventral visual pathway exhibit similarities in object recognition.
Previous comparisons focused on behavior and activation but overlooked spatial processing differences.

Purpose of the Study:

To compare spatial information processing between human observers and DCNNs.
To investigate how architectural differences in DCNNs affect agreement with human visual behavior.
To identify image-specific factors influencing the alignment of spatial priorities.

Main Methods:

Comparative analysis of 45 human observers and three feedforward DCNNs (vNet and ResNet).
Utilized eye tracking and saliency maps for visualizing and comparing information processing.
Investigated the impact of image properties like category, animacy, arousal, and valence.

Main Results:

Visualization methods (eye tracking, saliency maps) revealed distinct resolutions requiring careful consideration for comparison.
A DCNN with biologically plausible receptive fields (vNet) showed greater agreement with human viewing behavior than ResNet.
Image-specific factors (category, animacy, arousal, valence) directly correlated with agreement in spatial object recognition priorities.

Conclusions:

Fundamental differences in spatial processing exist between DCNNs and human vision.
DCNN architectures with biologically inspired features (like vNet) offer improved alignment with human visual priorities.
Understanding image content is crucial for bridging the gap between artificial and biological vision systems.