Why Most Studies of Individual Differences With Inhibition Tasks Are Bound To Fail

Establishing correlations among common inhibition tasks such as Stroop or flanker tasks has been proven quite difficult despite many attempts. It remains unknown whether this difficulty occurs because inhibition is a disparate set of phenomena or whether the analytical techiques to uncover a unified inhibition phenomenon fail in real-world contexts. In this paper, we explore the field-wide inability to assess whether inhibition is unified or disparate. We do so by showing that ordinary methods of correlating performance including those with latent variable models are doomed to fail because of trial noise (or, as it is sometimes called, measurement error). We then develop hierarchical models that account for variation across trials, variation across individuals, and covariation across individuals and tasks. These hierarchical models also fail to uncover correlations in typical designs for the same reasons. While we can charaterize the degree of trial noise, we cannot recover correlations in typical designs that enroll hundreds of people. We discuss possible improvements to study designs to help uncovering correlations, though we are not sure how feasible they are.