Errors-in-variables regression analysis to investigate unconscious mental processes

A large number of researchers agree that people can detect regularities in their environment and adapt behavior accordingly in the absence of awareness. The presumed unconscious effect of stimuli, contingencies, or rules on learning has been shown in a variety of paradigms (e.g., repetition priming, contextual cueing, unconscious conditioning, artificial grammar learning). Evidence that learning was indeed unconscious sometimes requires accepting the null hypothesis that participants were unaware of the regularities (indirect-without-direct-effect data pattern). As null-hypothesis significance testing is a poor method for proving the absence of an effect, one can regress the learning measure on the awareness measure, so that a significant intercept would be understood as successful learning without awareness (Greenwald, Klinger, & Schuh, 1995). However, the relationship between predictor and criterion variable is frequently biased by their respective low reliabilities. In particular, ignoring measurement error in the predictor variable will disattenuate the regression slope towards zero, which in turn could raise a true zero intercept above zero. As a solution, Klauer, Draine, and Greenwald (1998) suggested a correction method for predictor variables with rational zero points (such as d’) in the framework of errors-in-variables regression. In a series of simulations, we show that their method still overestimates true zero intercepts. As an alternative, we suggest that researchers (a) use a generative Bayesian regression approach that takes the uncertainty of predictor and criterion variable into account and (b) calculate Bayes factors to test the crucial intercept.