Functional dissociations versus post-hoc selection: Moving beyond the Stockart et al. (2025) compromise

TL;DR

This paper critiques the common practice of post-hoc trial selection in unconscious cognition studies, highlighting its flaws and proposing the use of functional dissociations as a more robust alternative for understanding unconscious processing.

Contribution

It introduces the concept of functional dissociations in D-I space as a superior method over post-hoc selection, enabling more comprehensive and reliable analysis of unconscious cognition.

Findings

Post-hoc selection isolates neutral response bias but can misrepresent sensitivity.

Sampling fallacy and regression artifacts arise from post-hoc trial exclusion.

Functional dissociations utilize the entire D-I space, avoiding null visibility issues.

Abstract

Stockart et al. (2025) recommend guidelines for best practices in the field of unconscious cognition. However, they condone the repeatedly criticized technique of excluding trials with high visibility ratings or of participants with high sensitivity for the critical stimulus. Based on standard signal detection theory for discrimination judgments, we show that post-hoc trial selection only isolates points of neutral response bias but remains consistent with uncomfortably high levels of sensitivity. We argue that post-hoc selection constitutes a sampling fallacy that capitalizes on chance, generates regression artifacts, and wrongly ascribes unconscious processing to stimulus conditions that may be far from indiscriminable. As an alternative, we advocate the study of functional dissociations, where direct (D) and indirect (I) measures are conceptualized as spanning up a two-dimensional D-I-space and where single, 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, circumventing requirements like null visibility and exhaustive reliability, and allowing for the planful measurement of theoretically meaningful functional relationships between experimentally controlled variables.