How Pain-Related Facial Expressions Are Evaluated in Relation to Gender, Race, and Emotion

Abstract

Inequities in pain assessment are well-documented; however, the psychological mechanisms underlying such biases are poorly understood. We investigated potential perceptual biases in the judgments of faces displaying pain-related movements. Across five online studies, 956 adult participants viewed images of computer-generated faces (“targets”) that varied in features related to race (Black and White) and gender (women and men). Target identity was manipulated across participants, and each target had equivalent facial movements that displayed varying intensities of movement in facial action-units related to pain (Studies 1–4) or pain and emotion (Study 5). On each trial, participants provided categorical judgments as to whether a target was in pain (Studies 1–4) or which expression the target displayed (Study 5) and then rated the perceived intensity of the expression. Meta-analyses of Studies 1–4 revealed that movement intensity was positively associated with both categorizing a trial as painful and perceived pain intensity. Target race and gender did not consistently affect pain-related judgments, contrary to well-documented clinical inequities. In Study 5, in which pain was equally likely relative to other emotions, pain was the least frequently selected emotion (5%). Our results suggest that perceivers can utilize facial movements to evaluate pain in other individuals, but perceiving pain may depend on contextual factors. Furthermore, assessments of computer-generated, pain-related facial movements online do not replicate sociocultural biases observed in the clinic. These findings provide a foundation for future studies comparing CGI and real images of pain and emphasize the need for further work on the relationship between pain and emotion.

Validation of the Montreal Pain and Affective Face Clips (MPAFC): The role of sex and participants’ pain status

“…Nevertheless, it has been found that dynamic emotional faces expressing happiness or disgust seem to be more representative of the emotion than static facial expressions (Trautmann et al, 2009). Recent evidence (Dildine et al, 2023) from computer-generated faces of pain, shows that the movement intensity of the faces was positively associated with higher rates of pain by the participants observing these images. Thus, it could be possible that, although we selected the highest peak of the expression of pain in each face from the MPAFC database, eliminating the sequence of movements might have reduced the perceived intensity of the pain and provided a more accurate approach to examining the attentional processing of emotional facial expressions (Biele & Grabowska, 2006;Fernandes-Magalhaes et al, 2022).…”

“…Teaching to detect genuine expressions of pain is important and is often included in clinical assessments of specific populations (e.g., infants, nonverbal patients, or cognitively impaired individuals). Another potential implication for the field is that future studies using models (like the MPAFC database) or computer-generated images, should pay attention to the specific groups of muscles more directly involved in the expression of pain (i.e., contraction of the eyebrows, contraction of the muscles surrounding the eyes, nose wrinkle/lip raise, and opening of the mouth) (Kunz et al, 2019) and are not involved in the expression of emotions like fear (Dildine et al, 2023). Thus, future studies using actors or computer-generated images might improve the validity of the stimuli paying attention to these unique muscle features of facial expressions of pain.…”

Personalization of Affective Models Using Classical Machine Learning: A Feasibility Study

“…In healthcare, affective computing is crucial for understanding various neurological disorders, including sleep disorders [2], schizophrenia [3], sleep quality assessment [4], autism spectrum disorder [5,6], and Parkinson's disease [7][8][9]. Emotions are also significant in identifying physiological states like fatigue, drowsiness, depression, and pain [10][11][12]. Emotions may be conveyed through a combination of facial expressions, vocalizations, gestures, and body movements [13][14][15].…”