However, illusions were also shown to be more heterogenous. Similarly, Roff (1953) computed a factor analysis on 70 perceptual measures, which resulted in a single factor associated with visual illusions (see also Aftanas & Royce, 1969). ![]() For example, Thurstone (1944) observed a factor underlying geometric illusions. Likewise, several factors were suggested to underlie individual differences in hue scaling ( Emery, Volbrecht, Peterzell, & Webster, 2017a Emery, Volbrecht, Peterzell, & Webster, 2017b), oculomotor tasks ( Bargary, Bosten, Goodbourn, Lawrance-Owen, Hogg, & Mollon, 2017), and binocular rivalry (e.g., Brascamp, Becker, & Hambrick, 2018).Ĭommon factors were proposed for visual illusions. A principal component analysis revealed a first component explaining only 34% of the variability in the data (but see Bosten, Goodbourn, Bargary, Verhallen, Lawrance-Owen, Hogg, & Mollon, 2017). For example, Cappe, Clarke, Mohr, & Herzog (2014) only observed weak correlations between the performance in six basic visual paradigms, such as visual acuity and contrast detection, suggesting that an individual with good performance in one task does not necessarily show good performance in other tasks. However, the space underlying vision seems to be multifactorial, i.e., there seems to be no unique common factor for vision (for reviews, see Mollon, Bosten, Peterzell, & Webster, 2017 Tulver, 2019). In analogy, a strong common factor for vision may be expected, i.e., it may be that an individual who performs better in one visual task compared to other individuals also performs better in other visual tasks, suggesting that there is a single monolithic structure underlying vision. Age-related changes of different cognitive functions, such as perceptual speed and reasoning skills, were indeed reported to significantly correlate. For example, there seems to be a strong common factor for cognition in healthy aging, that is, cognitive abilities are reliably affected with age (e.g., Baltes & Lindenberger, 1997 Kiely & Anstey, 2017 Lindenberger & Ghisletta, 2009). Hence, it is unlikely that the individual differences in the perception of visual illusions arise from instability across eyes, time, and measurement methods.Ĭommon factors are ubiquitous in everyday life. Last, we compared two illusion measurements, namely an adjustment procedure and a method of constant stimuli, which both led to similar individual differences. Second, we observed stable individual differences over time. In addition, illusion magnitudes were not significantly predicted by visual acuity. ![]() First, we did not find any significant differences in the magnitudes of the seven illusions tested with monocular or binocular viewing conditions. Here, we examined to what extent individual differences in the perception of visual illusions are stable across eyes, time, and measurement methods. 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. ![]() Other studies proposed that there are several subclasses of illusions, such as illusions of linear extent or distortions. For example, some studies suggested that there is a unique common factor for all visual illusions. Vision scientists have tried to classify illusions for more than a century.
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