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Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Generative visual common sense: Testing analysis-by-synthesis on Mondrian-style image.

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  • 1Department of Psychology and Behavioral Sciences, Zhejiang University.

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Human vision is causal, interpreting images based on their generation process. A Bayesian model trained on Mondrian images demonstrates this, supporting diverse visual tasks with minimal retraining.

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Area of Science:

  • Cognitive Science
  • Computational Neuroscience
  • Computer Vision

Background:

  • Mondrian-style images offer insights into human visual perception.
  • Human vision may involve interpreting an image's causal history.
  • This interpretation can be probabilistic and task-general.

Purpose of the Study:

  • To demonstrate the generative nature of human vision using Mondrian images.
  • To develop and test a Bayesian model for visual interpretation.
  • To show that a generative model can support various visual tasks.

Main Methods:

  • Utilized Mondrian-style images as a case study.
  • Developed a Bayesian model based on an image-generation task.
  • Trained the model on human-synthesized Mondrian images.

Main Results:

  • The model predicted human performance in perceptual complexity ranking.
  • The model captured transmission stability in iterative image passing.
  • The model successfully passed a visual Turing test.

Conclusions:

  • Human vision is inherently causal, inferring generation processes from images.
  • Generative vision acts as common sense, enabling broad task generalization.
  • Minimal retraining suggests a fundamental, adaptable visual processing mechanism.