Masked priming under the Bayesian microscope: Exploring the integration of local elements into global shape through Bayesian model comparison

In the present study, we will introduce a novel masked priming design comprising both objective (i.e., performance based) and subjective (i.e., visibility reports, gathered through the perceptual awareness scale; PAS) awareness measures to study whether local elements are grouped into global (square and diamond) shapes in the absence of awareness. Prime visibility will be manipulated using three different prime-mask stimulus onset asynchronies (SOAs) and an unmasked condition. We will investigate whether the introduction of online measures of awareness (e.g., PAS) affects priming effects, as well as explore the relation between objective awareness performance and subjective awareness reports using a common sensitivity measure (d’). The introduction of Bayesian analyses and generative regression models will complement the evidence provided by linear mixed models and will allow us to evaluate the results in favor -or against- the null hypotheses across the different experiments.