Event-Based Conformity Versus Regression to the Mean: A Comment on Kim and Hommel (2015)

Kim and Hommel (2015) provided an intriguing alternative explanation for conformity effects. Building on the theory of event coding (TEC; Hommel, 2009; Hommel, Müsseler, Aschersleben, & Prinz, 2001), they assumed that one’s own and others’ actions are represented in comparable ways, so that people may fail to distinguish between those two action categories. As a consequence, “people’s actions that have no social meaning should induce conformity effects” (p. 484). Building on a paradigm previously used to investigate social conformity (Klucharev, Hytönen, Rijpkema, Smidts, & Fernández, 2009; Shestakova et al., 2013), Kim and Hommel tested their hypothesis in three experiments involving facial-beauty ratings. In a first block, participants rated the beauty of 220 faces on a scale from 1 to 8 using a computer keyboard. After each rating, participants were presented with an intervening event. Participants saw either a static slide showing a number between 1 and 8 or a short movie in which another person pressed the respective number key (1–8) on a computer keyboard. These numbers were equal to, 2 to 3 points lower than, or 2 to 3 points higher than the rating given by the participant (equal, lower, and higher conditions, respectively). In a second block, all faces were again rated on the same scale from 1 to 8. The hypothesis was that numbers lower than the initial rating should lead to a negativity shift, whereas numbers higher than the initial rating should lead to a positivity shift. The authors present support for this across all three experiments. Critically, the paradigm confounded extremity of initial ratings and assignment of experimental condition (equal vs. lower vs. higher). Since the rating scale was restricted from 1 to 8, experimental condition could not be assigned independently of initial ratings. For example, it was not possible to assign an initial rating of 7 to the higher condition, but only to the equal or lower condition. Similarly, low ratings could not be assigned to the lower condition, but only to the equal or higher condition. As a result, the higher condition occurred for low (and moderate) but not for high values, and the lower condition occurred for high (and moderate) but not for low values. Figure 1 shows the distribution of experimental conditions across initial ratings and experiments in Kim and Hommel’s study. This confound is problematic because regression to the mean (Campbell & Kenny, 1999; Galton, 1886) dictates that extreme values on a variable X (e.g., first measurement) will be closer to the mean on any variable Y (e.g., second measurement) when X and Y are less than perfectly correlated. While error is on average randomly distributed across all measurements of X, high values of X will on average have a more positive error, whereas low values of X will have a more negative error. In other words, the measured values of X correlate with the measurement error of X. Because the measurement error of Y is independent of the measurement error of X, any extreme value in X will be less extreme in Y. See Shanks (2017) for an extensive discussion on this topic. Applied to Kim and Hommel’s data, this means that we would expect initially high values to be lower in the second measurement and initially low values to be higher in the second measurement. Regression to the mean combined with the confound described above can create the original pattern of results without assuming any psychological processes. 719082 PSSXXX10.1177/0956797617719082Ihmels, AcheEvent Coding Versus Regression to the Mean research-article2018