Unconscious cognition without post hoc selection artifacts: From selective analysis to functional dissociations

One of the most popular approaches to unconscious cognition is the technique of "post hoc selection": Priming effects and visibility ratings are measured in multitasks on the same trial, and only trials with the lowest visibility ratings are selected for analysis of (presumably unconscious) priming effects. In the past, the technique has been criticized for creating statistical artifacts and capitalizing on chance. Here, we argue that post hoc selection constitutes a sampling fallacy, confusing sensitivity and response bias, wrongly ascribing unconscious processing to stimulus conditions that may be far from indiscriminable. In response to a high-profile "best practice" paper by Stockart et al. (2025) that condones the technique, we use standard signal detection theory to show that post hoc selection only isolates trials with neutral response bias, irrespective of actual sensitivity, and thus fails to isolate trials where the critical stimulus is "unconscious". Our own data demonstrate that zero-visibility ratings are consistent with uncomfortably high levels of sensitivity. As an alternative to post hoc selection, we advocate the study of functional dissociations, where direct (D) and indirect (I) measures are conceptualized as spanning a two-dimensional D-I space wherein simple, sensitivity, and double dissociations appear as distinct curve patterns. While Stockart et al.'s recommendations cover only a single line of that space where D is close to zero, functional dissociations can utilize the entire space. This circumvents requirements like null visibility and exhaustive reliability, allows for dissociations among different measures of awareness, and supports the planful measurement of functional relationships between direct and indirect measures.