Random-Effects Psychophysics For Studying Individual Differences in Perception

In modern psychological science, many researchers use psychophysical tasks to study individual differences in perception, attention, aging and personality. Psychophysics, however, traditionally center designs with a great many trials and few individuals; designs that are inappropriate for individual difference studies. Here, I develop a random-effect psychophysics where variation across trials and individuals are jointly modeled in the Bayesian hierarchical framework. I show that the model is ideal for measuring thresholds in small-trial designs. Because the model jointly accounts for variation across trials and individuals, it provides assessment of correlation across tasks without the pernicious problem of attenuation from trial noise. Resulting correlations are accompanied by meansure of uncertainties that reflect both the number of trials per individuals and number of individuals. Because the frmaework is Bayesian, it is flexible, and I leverage this flexibility in two ways: First, I develop a custom-tailored psychphysical model for assessing whether stimulation is subliminal or superliminal. The threshold divides at chance-performance form above-chance performance, and the approach serves as a principled approach for assessing truly subliminal priming. Second, I place factor models on thresholds themselves demonstrating how the structure of individual differences in a battery of tasks may be assessed.