Common factors are ubiquitous. For example, there is a common factor, g, for intelligence. In vision, there is much weaker evidence for such common factors. For example, visual illusion magnitudes correlate only weakly with each other. Here, we investigated whether illusions are hyper-specific as in perceptual learning. First, we tested 19 variants of the Ebbinghaus illusion that differed in color, shape, or texture. Correlations between the illusion magnitudes of the different variants were mostly significant. Second, we reanalyzed a dataset from a previous experiment where 10 illusions were tested under four conditions of luminance and found significant correlations between the different luminance conditions of each illusion. However, there were only very weak correlations between the 10 different illusions. Third, five visual illusions were tested with four orientations. Again, there were significant correlations between the four orientations of each illusion, but not across different illusions. The weak inter-illusion correlations suggest that there is no unique common mechanism for the tested illusions. We suggest that most illusions make up their own factor.

Vision scientists have attempted to classify visual illusions according to certain aspects, such as brightness or spatial features. For example, Piaget proposed that visual illusion magnitudes either decrease or increase with age. Subsequently, it was suggested that illusions are segregated according to their context: real-world contexts enhance and abstract contexts inhibit illusion magnitudes with age. We tested the effects of context on the Müller-Lyer and Ponzo illusions with a standard condition (no additional context), a line-drawing perspective condition, and a real-world perspective condition. A mixed-effects model analysis, based on data from 76 observers with ages ranging from 6 to 66 years, did not reveal any significant interaction between context and age. Although we found strong intra-illusion correlations for both illusions, we found only weak inter-illusion correlations, suggesting that the structure underlying these two spatial illusions includes several specific factors.

We recently found only weak correlations between the susceptibility to various visual illusions. However, we observed strong correlations among different variants of an illusion, suggesting that the visual space of illusions includes several illusion-specific factors. Here, we specifically examined how factors for the vertical-horizontal, Müller-Lyer, and Ponzo illusions relate to each other. We measured the susceptibility to each illusion separately and to combinations of two illusions, which we refer to as a merged illusion; for example, we tested the Müller-Lyer illusion and the vertical-horizontal illusion, as well as a merged version of both illusions. We used an adjustment procedure in two experiments with 306 and 98 participants, respectively. Using path analyses, correlations, and exploratory factor analyses, we found that the susceptibility to a merged illusion is well predicted from the susceptibilities to the individual illusions. We suggest that there are illusion-specific factors that, by independent combinations, represent the whole visual structure underlying illusions.

Perceptual learning is usually assumed to occur within sensory areas or when sensory evidence is mapped onto decisions. Subsequent procedural and motor processes, involved in most perceptual learning experiments, are thought to play no role in the learning process. Here, we show that this is not the case. Observers trained with a standard three-line bisection task and indicated the offset direction of the central line by pressing either a left or right push button. Before and after training, observers adjusted the central line of the same bisection stimulus using a computer mouse. As expected, performance improved through training. Surprisingly, learning did not transfer to the untrained mouse adjustment condition. The same was true for the opposite, i.e., training with mouse adjustments did not transfer to the push button condition. We found partial transfer when observers adjusted the central line with two different adjustment procedures. We suggest that perceptual learning is specific to procedural motor aspects beyond visual processing. Our results support theories were visual stimuli are coded together with their corresponding actions.

The world's population is aging at an increasing rate. Even in the absence of neurodegenerative disorders, healthy aging affects perception and cognition. In the context of cognition, common factors are well established. Much less is known about common factors for vision. Here, we tested 92 healthy older and 104 healthy younger participants in 19 visual tests (including visual search and contrast sensitivity) and three cognitive tests (including verbal fluency and digit span). Unsurprisingly, younger participants performed better than older participants in almost all tests. Surprisingly, however, the performance of older participants was mostly uncorrelated between visual tests, and we found no evidence for a common factor.

There are situations in which what is perceived in central vision is different to what is perceived in the periphery, even though the stimulus display is uniform. Here, we studied two cases, known as the Extinction illusion and the Honeycomb illusion, involving small disks and lines, respectively, presented over a large extent of the visual field. Disks and lines are visible in the periphery on their own, but they become invisible when they are presented as part of a pattern (grid). Observers (N ¼ 56) adjusted a circular probe to report the size of the region in which they had seen the lines or the disks. Different images had black or white lines/disks, and we included control stimuli in which these features were spatially separated from the regular grid of squares. We confirmed that the illusion was experienced by the majority of observers and is dependent on the interaction between the elements (i.e., the lines/disks have to be near the squares). We found a dissociation between the two illusions in the dependence on contrast polarity suggesting different mechanisms. We analysed the variability between individuals with respect to schizotypical and autistic-spectrum traits (short version of the Oxford-Liverpool Inventory of Feelings and Experiences [O-LIFE] questionnaire and the Autistic Quotient, respectively) but found no significant relationships. We discuss how illusions relative to what observers are aware of in the periphery may offer a unique tool to study visual awareness.

Vision scientists have tried to classify illusions for more than a century. For example, some studies suggested that there is a unique common factor for all visual illusions. Other studies proposed that there are several subclasses of illusions, such as illusions of linear extent or distortions. We previously observed strong within-illusion correlations but only weak between-illusion correlations, arguing in favor of an even higher multifactorial space with-more or less-each illusion making up its own factor. These mixed results are surprising. Here, we examined to what extent individual differences in the perception of visual illusions are stable across eyes, time, and measurement methods. First, we did not find any significant differences in the magnitudes of the seven illusions tested with monocular or binocular viewing conditions. In addition, illusion magnitudes were not significantly predicted by visual acuity. Second, we observed stable individual differences over time. Last, we compared two illusion measurements, namely an adjustment procedure and a method of constant stimuli, which both led to similar individual differences. Hence, it is unlikely that the individual differences in the perception of visual illusions arise from instability across eyes, time, and measurement methods.

Perceptual learning is usually feature-specific. Recently, we showed that perceptual learning is even specific for the motor response type. In a three-line bisection task, participants indicated whether the central line was offset either to the left or right by pressing a left or a right button, respectively. We found no transfer when the same participants adjusted the offset by using a computer mouse. Here, we first show that perceptual learning with mouse adjustments transfers to the untrained hand, but only for the trained adjustment condition. There was no transfer to the button press conditions, neither for the trained nor the untrained hand. Second, we show that a double training procedure enables transfer from the mouse adjustment to the button press condition. Hence, the specificity of perceptual learning to the motor response type can be overcome by double training as it is the case for visual features. Our results suggest that during perceptual learning, perceptuo-decisional signals are encoded together with the corresponding actions.