When confronted with a scene of emotional faces, our brains automatically average the individual facial expressions together to create the gist of the collective emotion. Here, we tested whether this ensemble averaging could also occur for facial attractiveness, and in turn shape 2 related face perception phenomena: adaptation and the cheerleader effect. In our first 2 experiments, we showed that adaptation aftereffects could indeed be shaped by ensemble statistics; viewing an increasingly unattractive group of faces conversely increased attractiveness judgments for a subsequently presented face. Not only did group adaptation aftereffects occur, but their effects were equivalent to those observed from the morphed average face of the group, suggesting that the visual system had averaged the group together. In our last 2 experiments, we showed that viewing a target face in an increasingly unattractive group led to the target being perceived as increasingly more attractive: a "cheerleader" effect. Moreover, our results suggest that this cheerleader effect likely comprises of both a social positive effect and a contrastive process, requiring variance between the surrounding and target faces; that is, the visual system appeared incapable of boosting a target's attractiveness when all of the faces in the scene were identical. Furthermore, the mean group attractiveness ratings strongly predicted both the cheerleader effect and adaptation aftereffects, with the latter 2 also interrelated. This suggests that ensemble statistics is the common underlying process linking each of these phenomena. To be perceived as beautiful, being surrounded by unattractive friends may help. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The human vision system can extract a stable representation of the always-changing visual world. However, the mechanism underlying such perceptual continuity remains unclear. A possible candidate is the serial dependence: visual perception of an object is positively biased toward the visual input from the recent past. Does the visual system use one pattern of serial dependence for general purposes? Or different patterns of serial dependence for different visual tasks? Because different social facial traits (e.g., trustworthiness and dominance) are dissociable, it is reasonable to assume that the perception of different facial characteristics would require different patterns of serial dependences. In this study, we examine the existence and the similarities of the serial dependence(s) in the evaluation of seven facial characteristics (i.e., attractiveness, trustworthiness, confidence, dominance, intelligence, age, and aggressiveness). The convergent evidence from conventional Derivative of Gaussian fitting and Markov Chain modeling demonstrated that (1) serial dependence exists in judgments of all seven social facial characteristic, (2) the serial dependences of them are highly similar, and (3) the serial dependence follows the efficient coding. Thus it is highly possible that there exists a general serial dependence mechanism for (at least high-level) vision processing. Moreover, we used the Markov Chain modeling to better describe the transitional pattern of serial dependence, which is a kind of Markov process. These findings may shed light on future works regarding serial dependence, as well as face perception.

Ensemble coding and attention are two mechanisms utilized by our visual system to overcome the limitation of visual processing when confronted with the overwhelming visual information. Recent evidence in ensemble coding of size suggests that the attended items contributed more to the averaging. On the other hand, some new evidence also indicates that reduced attention jeopardies the perceptual averaging of stimuli. What is the relationship between attention and ensemble coding? To answer this question, in the current study, we tested whether an exogenous attentional cue would influence the reported mean emotion of a crowd. We showed participants a group of four faces with different emotions. Participants’ attention was guided to the happiest or saddest face (attention conditions), or not to any specific face (baseline condition). The results supported the notion that the attention alters the ensemble perception of the facial expression by elevating the weight of that face in the ensemble representation. This opens the question for the neural mechanisms of ensemble coding and its connection to visual attention.