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Previous findings on people perception show that perceivers are attuned to the social categories of group members, which subsequently influences social judgments. An outstanding question is whether perceivers are also attuned to visual cue variability (e.g., gender typicality). In two studies (n = 165), perceivers viewed 12-person ensembles (500 ms) of varying White men-to-women ratios. Importantly, faces of one gender/sex were morphed to appear either more masculine or more feminine. Consistent with prior work, results indicated that judgments varied by the actual gender/sex ratio. In addition, perceivers' judgments varied as a function of manipulated gender cues. Ensembles composed of masculine, compared to feminine White men, were judged to have more men, higher perceived masculinity, and to be more threatening. Complementary results were found for ensembles composed of feminine, compared to masculine White women. These findings highlight the impact of both social categories and visual phenotypic cue variability on people perception. 

Abstract Cesario claims that all bias research tells us is that people “end up using the information they have come to learn as being probabilistically accurate in their daily lives” (sect. 5, para. 4). We expose Cesario's flawed assumptions about the relationship between accuracy and bias. Through statistical simulations and empirical work, we show that even probabilistically accurate responses are regularly accompanied by bias. 

Numerous attempts to improve diversity by way of changing the hearts of decision makers have fallen short of the desired outcome. One underappreciated factor that contributes to bias resides not in decision makers’ hearts, but instead in their minds. People possess images, or mental representations, for specific roles and professions. Which mental image or representation springs spontaneously to mind depends on the current status quo within a field. Whether or not an individual or groups’ appearance matches visual stereotypes results in perceptually mediated preferences and prejudices, both of which harbor pernicious assumptions about who belongs in a professional setting and why. Leveraging these scientific insights can enact change. Shifting visible exemplars can change people’s mental representations and their heart’s evaluative reactions to others. 

Developing computational methods for bodily expressed emotion understanding can benet from knowledge and approaches of multiple fields, including computer vision, robotics, psychology/psychiatry, graphics, data mining, machine learning, and movement analysis. The panel, consisting of active researchers in some closely related fields, attempts to open a discussion on the future of this new and exciting research area. This paper documents the opinions expressed by the individual panelists.