Individual Differences in Visual Illusions: Graphical and Analytic Approaches For Finding Structure in Real-World Cases

Resumen

Are people who are susceptible to one illusion susceptible to others? Previous research has shown small correlations, but might small values reflect attenuation from measurement error? Data from 149 participants on 2 variants of 5 illusions were collected using an adjustment paradigm. The resulting data are of notable high quality inasmuch as there is relatively little within-subject variability and relatively much between-subject variability ($\gamma^2 \approx 1.14$, reliability $\approx .93$). Because the data are of such high quality, correlations may be estimated to high precision. In line with previous research, these cross-illusion correlations are low in value, about .22$\pm .07$. A Bayesian hierarchical analysis reveals that there is almost no attenuation from measurement error in these values. Though correlations are low, latent variable analysis reveals that the pattern among these correlations yields a single, common latent factor. This factor loads on every illusion and accounts for about 25\% of the variance in each; thus it is a {\em susceptibility to illusions} factor. We provide a novel set of statistical and graphical analyses focused on understanding the uncertainty in effects, correlations, and latent variables.

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Cómo citar (APA 7)

Mehrvarz, M., Popat, H., & Rouder, J. N. (2024). Individual Differences in Visual Illusions: Graphical and Analytic Approaches For Finding Structure in Real-World Cases. https://doi.org/10.31234/osf.io/6z92y