The Müller-Lyer illusion is a well-known optical phenomenon with several competing explanations. In the current study we reviewed the illusion in a convolutional neural network from a perspective of image-source relationships in the process of visual functions development. To recreate the effect of the illusion we proposed a novel method that lets us simulate the development of visual functions in a controlled spatial environment from the state of ‘blank slate’ to effective spatial problem solving. This process is designed to reflect the development of human visual system and enable us to determine how depth perception can contribute to the appearance of the phenomenon. We were able to successfully reproduce the effect of the classic Müller-Lyer in 30 independent convolutional models and also get similar results with the variants of the illusion that are thought to be unrelated to spatial perception. For the pairs of classic stimuli we conducted additional statistical analysis using both frequentist and Bayesian methods. The methodological and empirical insights of this study may be helpful for subsequent investigation of visual cognition and reconsideration of the image-source relationships in optical illusions.