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

Simone Malejka
University of Cologne ~ Department of Psychology
Miguel A. Vadillo
Universidad Autónoma de Madrid, Spain
Zoltán Dienes
University of Sussex, United Kingdom
David R. Shanks
University College London, United Kingdom

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.



unconscious learning
implicit cognition
regression analysis
measurement error
Bayes factor
Linear regression? Last updated 2 years ago

Interesting work! I'm wondering why you would stick to linear regression at all. A linearity assumption itself may not be valid, and hence values closer to the 0 predictor value should be more informative about the intercept than values further away from it. Could you not instead use a nonparametric alternative like Gaussian Processes to focus on e...

Prof. Maarten Speekenbrink 1 comment
Cite this as:

Malejka, S., Vadillo, M., Dienes, Z., & Shanks, D. (2021, July). Errors-in-variables regression analysis to investigate unconscious mental processes. Paper presented at Virtual MathPsych/ICCM 2021. Via