Angry White Faces: A Contradiction of Racial Stereotypes and Emotion-Resembling Appearance

Adams, Reginald B.; Albohn, Daniel N.; Hedgecoth, Nicole; Garrido, Carlos O.; Adams, Katharine Donnelly

doi:10.1007/s42761-021-00091-5

Cited by 10 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although identifying the overall relationship between racial prototypicality and trustworthiness was not a central goal of this investigation, this change is noteworthy. One possibility is that this change across studies reflects stimulus race differences in emotional expression resemblance (Adams et al, 2022) or that darker skin tone was actually evaluated as a trustworthy cue among college students (Birdsong et al, 2019). Although we address this in greater detail in the General Discussion, this highlights the importance of stimulus populations in shaping the outputs of computational models (Todorov & Oh, 2021).…”

Section: Discussionmentioning

confidence: 99%

“…We found opposing average-level relationships between trustworthiness and White-to-Black prototypicality in Studies 1 and 3, which employed different stimuli. One possibility is that racial differences in facial features (e.g., emotional expression resemblance; skin tone) are differentially related to trustworthiness across contexts (Adams et al, 2022; Birdsong et al, 2019). Alternatively, the diverse stimuli in Study 3 may have made race salient, triggering bias regulation.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Racial Prejudice Affects Representations of Facial Trustworthiness

Hutchings,

Freiburger,

Sim

et al. 2024

Psychol Sci

View full text Add to dashboard Cite

What makes faces seem trustworthy? We investigated how racial prejudice predicts the extent to which perceivers employ racially prototypical cues to infer trustworthiness from faces. We constructed participant-level computational models of trustworthiness and White-to-Black prototypicality from U.S. college students’ judgments of White (Study 1, N = 206) and Black–White morphed (Study 3, N = 386) synthetic faces. Although the average relationships between models differed across stimuli, both studies revealed that as participants’ anti-Black prejudice increased and/or intergroup contact decreased, so too did participants’ tendency to conflate White prototypical features with trustworthiness and Black prototypical features with untrustworthiness. Study 2 ( N = 324) and Study 4 ( N = 397) corroborated that untrustworthy faces constructed from participants with pro-White preferences appeared more Black prototypical to naive U.S. adults, relative to untrustworthy faces modeled from other participants. This work highlights the important role of racial biases in shaping impressions of facial trustworthiness.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Racial Prejudice Affects Representations of Facial Trustworthiness

Hutchings,

Freiburger,

Sim

et al. 2024

Psychol Sci

View full text Add to dashboard Cite

show abstract

“…These findings match self-reported emotional experiences and expressions with strong cultural consistency across 37 different countries worldwide [ 153 ]. Furthermore, there are even differences in the extent to which neutral facial appearance resembles different emotions, with male faces physically resembling anger expressions more (low, hooded brows, thin lips, and angular features) and female faces resembling fear expressions more [ 154 ].…”

Section: Emotion : Computational Principles and Foundationsmentioning

confidence: 99%

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion

Wang

Zhao

et al. 2023

Proc. IEEE

Self Cite

View full text Add to dashboard Cite

The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of “emotion,” coupled with the inherently subjective nature of emotions and their intricate nuances. In this article, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a “Holy Grail” research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field.

show abstract

“…The stimulus-level features of race, gender, and age were estimated using the categorizations provided by DeepFace (see Study 2 Method for details). Stimulus-level face metrics (e.g., eye size, face height) and incidental emotion resemblance (i.e., the likelihood that the neutral face resembled an angry, happy, sad, disgusted, fearful, or surprised expression) were estimated using a modified and updated version of a neutral face-specific stacked-ensemble deep learning model developed and detailed in previous studies (for details see, Adams et al, 2022;Albohn & Adams, 2021).…”

Section: Training Featuresmentioning

confidence: 99%

Determinants of Shared and Idiosyncratic Contributions to Judgments of Faces

Albohn,

Martinez,

Todorov

2023

Preprint

View full text Add to dashboard Cite

Recent work has shown that the idiosyncrasies of the observer can contribute more to the variance of social judgments of faces than the features of the faces. However, it is unclear what conditions determine the relative contributions of idiosyncratic and shared variance. Here, we examine three conditions: type of judgment, response scales, and diversity of face stimuli. First, we show that for simpler, directly observable judgments (e.g., masculinity) shared exceeds idiosyncratic variance, whereas for more complex, less directly observable judgments (e.g., trustworthiness) idiosyncratic exceeds shared variance. Second, dichotomous forced-choice responses (i.e., “yes”/”no”) resulted in greater shared variance compared to multi-point Likert- type responses. Third, we show that judgments of more diverse face images increase the amount of shared variance. Finally, using machine learning methods, we examine how stimulus (e.g., emotion resemblance, skin luminosity) and observer variables (e.g., race, age) contribute to shared and idiosyncratic variance of judgments. Overall, our results indicate that an observer's age is the most consistent and best predictor of idiosyncratic variance contributions to face judgments measured in the current research.

show abstract

Angry White Faces: A Contradiction of Racial Stereotypes and Emotion-Resembling Appearance

Cited by 10 publications

References 34 publications

Racial Prejudice Affects Representations of Facial Trustworthiness

Racial Prejudice Affects Representations of Facial Trustworthiness

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion

Determinants of Shared and Idiosyncratic Contributions to Judgments of Faces

Contact Info

Product

Resources

About