The weight of beauty in psychological research

present valuable insights into approaching inquiries on weight-based bias at work. For example, they highlight the difference between weight-based bias and other biases based on demographic characteristics such as gender and ethnicity, pointing out that belief in controllability over one ’ s own weight leads to greater culpability. However, it is also necessary to pay adequate attention to a demographic trait that forms a highly analogous type of bias. We extend Lemmon and colleagues ’ argument by endorsing the need to attend to pro-beauty or attractiveness bias

that weight researchers can leverage is its acknowledgment of implicit mechanisms.Research on attractiveness bias has generally relied on the halo effect to explain the mechanism leading to discrimination against less attractive individuals.The halo effect is a cognitive tendency that occurs when an impression created based on one feature of a person influences the evaluation of the person in another area.Batres and Shiramizu (2022) emphasize how this bias becomes apparent in terms of physical appeal.Around the world, individuals with apparently pleasing features are often regarded as having positive qualities in other, often unrelated, domains such as intelligence or compliance.Similarly, people not fitting the social standard of desirable weight can be judged negatively in other unrelated domains, such as job skills, which would yield disadvantages in their professional careers.
The attractiveness literature's greater emphasis on the halo effect suggests that people's negative attitudes toward obese people tend to be implicit and automatic rather than explicit and controlled.Although the focal article focuses on explicit mechanisms of antifat bias such as (potentially) conscious reasoning about weight controllability, some research evidence also implies the necessity for weight researchers to carefully attend to implicit mechanisms.For example, implicit association tests (IAT; Greenwald et al., 1998), which measure the strength of associations through a series of timed tasks, have provided consistent evidence that people generally associate "fat" with "bad" and "thin" with "good," regardless of their different level of explicit biases (Schupp & Renner, 2011).It has been also documented that the mere sight of an obese person triggers negative nonverbal behaviors such as facial expressions.A series of field studies found consistent evidence that shoppers who appeared to be obese (wearing obesity prosthetics) received significantly less eye contact and friendliness than did the same shoppers who appeared to be thin (not wearing obesity prosthetics; King et al., 2006).
However, in the same experiments, apparent weight did not lead to differences in overt behavior of the store employees such as providing help upon request or greeting.Such contradicting evidence of antifat justice is in line with the justification suppression model (JSM) described in the focal article.People suppress their genuine prejudice against obese people in the realm where they can consciously control their behavior.Although altering beliefs in weight controllability can enhance motivation to suppress conscious discriminatory behavior, however, unconscious and implicit forms of discrimination are more pervasive in contemporary society, as seen in the King and colleagues' field experiments.Because such subtle discrimination still has severe consequences (Pearson et al., 2009), weight researchers need to take broader approaches to effectively address such implicit mechanisms as well.Likewise, merging insights from research on attractiveness bias will enable weight researchers to have a more comprehensive understanding of the phenomenon of antifat bias while equipping strategies to address antifat bias more effectively.

Distinctions between weight-and attractiveness-based biases
Whereas pro-beauty and antifat biases often overlap and intersect, they differ primarily in their focus.Weight-based bias centers on judgments related to an individual's body size and weight, whereas attractiveness bias focuses on judgments related to facial features, body symmetry, and overall appearance.These biases also show somewhat different intersectionality.Although women are more severely penalized for not meeting both weight and attractiveness standards than men, race influences different patterns in weight and attractiveness biases.
Attractiveness is a stronger predictor of income among Black people than among White people (Monk et al., 2021), but it has been also documented that Black women are less vulnerable to the stigma of obesity than White women (Hebl & Heatherton, 1998), suggesting that weight may have less impact on mental health among Black women.

Articulating boundaries from attractiveness-based bias literature
Although focal article argues that antifat bias stems from unique stereotypes specifically attached to obese individuals such as being lazy and selfish, little is understood about how much these stereotypes contribute to negative consequences after accounting for physical attractiveness.Whereas it is plausible that these unique stereotypes would have a distinct set of negative consequences from the consequences of lacking physical attractiveness, clear evidence has been unavailable.To assess the distinct effect of each bias, future research should consider the confounded impact and establish the boundary of each bias.Acknowledging the great overlap as well as the distinctiveness of weight and attractiveness biases will also enable researchers to build on and contribute to cumulative knowledge.

Suggestions for future weight-based bias researchers
Drawing upon the associations and distinctions between weight and attractiveness biases, we discussed the necessity to attend to implicit mechanisms and disentangle the often-confounded impacts of those biases.We will extend these points below by providing more concrete examples of how organizational researchers can pursue these inquiries.

Addressing implicit mechanisms
A growing body of research on implicit mechanisms shows that although implicit bias is less malleable than explicit bias, there are strategies found to be effective in addressing it.For example, exposure to counterstereotypic examples has been found effective in reducing implicit bias (e.g., Hinman et al., 2015;Lai et al., 2016).Organizational researchers can apply this strategy to mitigate weight bias at work.It is worth investigating the effectiveness of highlighting the accomplishments of successful employees across diverse body sizes, weights, and forms in media, advertising, and public relations efforts.By providing vivid examples that violate the mental association between success and a specific body shape, the subconscious link between fat people and negative valence could be attenuated.Merging insights from broader research on implicit mechanisms will empower weight researchers to develop effective strategies to address deep-rooted unconscious biases, fostering a more empathetic and inclusive organizational culture.
Furthermore, understanding that discrimination occurs more often because of unconscious behavioral tendencies and embodied cultural values rather than the perpetrators' willful acts can inform interventions to address the issue.The acknowledgment of the limited "controllability" of the bias holders over their behaviors can reduce the stigmatization of bias holders and enhance engagement from a wider range of individuals in constructive discussions while promoting initiatives to make structural changes.By taking implicit as well as explicit mechanisms into account, researchers can also avoid losing sight of systemic inequity for individual misconduct.

Disentangling confounding effects
By separating antifat bias from pro-beauty bias, weight researchers can contribute a more precise understanding of their distinct roles in driving discrimination at work.To achieve this goal, we recommend that future researchers conduct meta-analyses with existing data that include measures of both attractiveness and weight.Some research on attractiveness bias uses BMI as one of the subscales to measure attractiveness (e.g., Gupta et al., 2015), whereas studies on weight bias often collect information on how overweight people are evaluated in an overall attractiveness scale (e.g., Hebl & Heatherton, 1998).Future researchers can collect raw data from these prior studies to test to what extent antifat bias at work is mediated by "plainness penalty" (i.e., disadvantages associated with being unattractive) and to what extent weight explains the impact of attractiveness on work-related outcomes.Setting clear boundaries for weight research will lay the foundation for specialization of the field, contributing to the and richness of scholarship and fruitful interdisciplinary collaboration.