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Depth Perception and Spatial Vision01:15

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Updated: Sep 10, 2025

Visualizing Visual Adaptation
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Characterizing internal models of the visual environment.

Micha Engeser1,2, Susan Ajith1, Ilker Duymaz1

  • 1Department of Mathematics and Computer Science, Physics, Geography, Justus Liebig University Giessen, Giessen, HE, Germany.

Proceedings. Biological Sciences
|August 19, 2025
PubMed
Summary
This summary is machine-generated.

Understanding natural vision requires exploring internal models of the world. New participant-driven methods, like line drawings, can reveal individual expectations for scene perception.

Keywords:
drawingsindividual differencesinternal modelspredictive processingscene representationvisual perception

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Psychology

Background:

  • Natural vision is remarkably efficient despite environmental complexity.
  • Predictive processing frameworks explain perceptual efficiency via the match between visual input and internal world models.
  • Internal models are crucial for scene perception due to the environment's statistical structure.

Purpose of the Study:

  • To investigate the contents of internal models in natural scene perception.
  • To address the limitations of experimenter-driven stimulus manipulations in current research.
  • To advocate for participant-driven approaches to characterize internal representations.

Main Methods:

  • Critique of current experimental approaches inferring internal models from arbitrary stimulus manipulations.
  • Highlighting the need for participant-driven methods focusing on typical scene descriptions.
  • Discussing emerging methods like line drawings for unconstrained characterization of internal representations.

Main Results:

  • Current literature over-relies on experimenter-driven methods.
  • Participant-driven approaches offer a more direct way to understand internal models.
  • Emerging methods allow for unconstrained, individual-level characterization of representations.

Conclusions:

  • Progress in understanding scene perception requires a shift towards participant-driven research.
  • Characterizing individual expectations about the world is a crucial next step.
  • New methods enable a deeper understanding of internal models in natural vision.