Abstract

When evaluating items in a sequence, the current judgment is influenced by the previous item and decision. These sequential biases take the form of assimilation (shifting toward the previous item/decision) or contrast (shifting away). Previous research investigating facial attractiveness evaluations provides mixed results while using analytical techniques that fail to address the dependencies in the data or acknowledge that the images represent only a subset of the population. Here, we utilized cross-classified linear mixed-effects modeling across 5 experiments. We found compelling evidence of multicollinearity in our models, which may explain apparent contradictions in the literature. Our results demonstrated that the previous image’s rating positively influenced current ratings, and this was also the case for the previous image’s baseline value, although only when that image remained onscreen during the current trial. Further, we found no influence of the next face on current judgments when this was visible. In our final experiment, the response bias due to the previous trial remained present even when accounts involving motor effort were addressed. Taken together, these findings provide a clear framework in which to incorporate current and past results regarding the biases apparent in sequential judgments, along with an appropriate method for investigating these biases.