Influences of orientation on the Ponzo, contrast, and Craik-O'Brien-Cornsweet illusions

Vertical anisotropy in lightness perception not caused by lighting assumption

Our recent study found an illusory effect whereby an image of an upward-facing gray panel appears darker than its 180-degree rotated image. We attributed this inversion effect to the observer's implicit assumption that light from above is more intense than light from below. This paper aims to explore the possibility that low-level visual anisotropy also contributes to the effect. In Experiment 1, we investigated whether the effect could be observed even when the position, the contrast polarity, and the existence of the edge were manipulated. In Experiments 2 and 3, the effect was further examined using stimuli that contained no depth cues. Experiment 4 confirmed the effect using stimuli of even simpler configuration. The results of all the experiments demonstrated that brighter edges on the upper side of the target make it appear lighter, indicating that low-level anisotropy contributes to the inversion effect, even without depth orientation information. However, darker edges on the upper side of the target produced ambiguous results. We speculate that the perceived lightness of the target might be affected by two kinds of vertical anisotropy, one of which is dependent on contrast polarity while the other is independent of it. Moreover, the results also replicated the previous finding that the lighting assumption contributes to perceived lightness. Overall, the present study demonstrates that both low-level vertical anisotropy and mid-level lighting assumption influence lightness.

Deconfounded and mixed-symmetry versions of the Ponzo illusion figure

One of the original Ponzo illusion figures, which consists of two converging lines between which two parallel lines of similar length have been inserted orthogonal to the figure's axis of mirror symmetry, was itself mirror-reflected so that the overall shape of the figure became "< >" or "> <", and one line at a time was inserted into each half. The usual illusion - the overestimation of the length of a line that is nearer to a vertex than a farther-away comparison line - occurred. Experiments 1 and 2 used different distances of target and comparison lines to the vertices, but identical distances of these lines from the converging lines, and so, as a tandem, deconfounded the two variables. Experiments 3 and 4 changed the symmetries of the modified Ponzo figure by reducing opposing half-angles of the converging lines or by tilting target and comparison lines concordantly or discordantly. The first measure, which created unequal distances of the endpoints of the target and comparison lines from the converging lines, hardly affected the amount of illusion. The second measure often attenuated the illusion - equally so for concordant and discordant tilts - suggesting that global and local symmetries of the stimuli, and their accordance, were less important than the vertical versus oblique orientation of target and comparison lines. Descriptively, the main cause of the Ponzo illusion seems to be the size of the gap between target and converging lines. The neural substrate of the effect may be interactions between orientation-sensitive and end-inhibited neurons.

A Parametric Framework to Generate Visual Illusions Using Python

Visual illusions are fascinating phenomena that have been used and studied by artists and scientists for centuries, leading to important discoveries about the neurocognitive underpinnings of perception, consciousness, and neuropsychiatric disorders such as schizophrenia or autism. Surprisingly, despite their historical and theoretical importance as psychological stimuli, there is no dedicated software, nor consistent approach, to generate illusions in a systematic fashion. Instead, scientists have to craft them by hand in an idiosyncratic fashion, or use pre-made images not tailored for the specific needs of their studies. This, in turn, hinders the reproducibility of illusion-based research, narrowing possibilities for scientific breakthroughs and their applications. With the aim of addressing this gap, Pyllusion is a Python-based open-source software (freely available at https://github.com/RealityBending/Pyllusion), that offers a framework to manipulate and generate illusions in a systematic way, compatible with different output formats such as image files (.png, .jpg, .tiff, etc.) or experimental software (such as PsychoPy).

The conceptual understanding of depth rather than the low-level processing of spatial frequencies drives the corridor illusion

Our objective was to determine how different spatial frequencies affect the perceptual size rescaling of stimuli in the corridor illusion. Two experiments were performed using the method of constant stimuli. In experiment 1, the task required participants to compare the size of comparison and standard rings displayed over the same background image. ANOVA on the points of subject equality (PSEs) revealed that the perceived size of the top and bottom standard rings changed as a function of the availability of the high, medium, and low spatial frequency information. In experiment 2, the task required participants to compare the size of a comparison ring presented outside of the background image with a standard ring presented inside it. ANOVA on the PSEs revealed that the apparent size of the top and not the bottom standard ring changed depending on the availability of medium spatial frequency information. Eye-tracking revealed that the spatial frequency range of the background image in the periphery affected participants' eye positioning, which may explain why the effects of different spatial frequencies fluctuated across experiments. Nonetheless, when we consider these findings together, we propose that the conceptual understanding of depth plays a more important role in explaining the corridor illusion than the low-level processing of depth information extracted from different spatial frequencies along separate channels.