The need for diversity in research on facial expressions of pain

Dildine, Troy C.; Atlas, Lauren Y.

doi:10.1097/j.pain.0000000000001593

Cited by 16 publications

(12 citation statements)

References 27 publications

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“…However, it is yet unknown whether culture and associated factors such as language also affect the facial encoding of pain. Indeed, most studies on facial responses to pain have been conducted in culturally, ethnically, and linguistically homogenous samples from northern Europe and in North America, and studies are lacking that have compared facial responses to pain between individuals from more heterogeneous backgrounds [ 25 ]. The present comparative study is a first pilot attempt to investigate whether facial encoding and especially decoding of pain differs between patients and observers from a Nordic (Denmark), a western central (Germany), and two European Mediterranean countries (Italy and Spain) as well as from one western Asian country (Israel).…”

Section: Discussionmentioning

confidence: 99%

Observing Pain in Individuals with Cognitive Impairment: A Pilot Comparison Attempt across Countries and across Different Types of Cognitive Impairment

et al. 2021

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Facial expression is a key aspect in observational scales developed to improve pain assessment in individuals with cognitive impairments. Although these scales are used internationally in individuals with different types of cognitive impairments, it is not known whether observing facial expressions of pain might differ between regions or between different types of cognitive impairments. In a pilot study, facial responses to standardized experimental pressure pain were assessed among individuals with different types of cognitive impairments (dementia, mild cognitive impairment, Huntington’s disease, and intellectual disability) from different countries (Denmark, Germany, Italy, Israel, and Spain) and were analyzed using facial descriptors from the PAIC scale (Pain Assessment in Impaired Cognition). We found high inter-rater reliability between observers from different countries. Moreover, facial responses to pain did not differ between individuals with dementia from different countries (Denmark, Germany, and Spain). However, the type of cognitive impairment had a significant impact; with individuals with intellectual disability (all being from Israel) showing the strongest facial responses. Our pilot data suggest that the country of origin does not strongly affect how pain is facially expressed or how facial responses are being scored. However, the type of cognitive impairment showed a clear effect in our pilot study, with elevated facial responses in individuals with intellectual disability.

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Section: Discussionmentioning

confidence: 99%

Observing Pain in Individuals with Cognitive Impairment: A Pilot Comparison Attempt across Countries and across Different Types of Cognitive Impairment

et al. 2021

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“…Because of this diversity, researchers may better examine how pain-related outcomes vary based on these sociodemographic variables, promoting better understanding of disparities in pain care. 19 Indeed, stimuli from the DPD have been used to demonstrate that racial bias in pain perception facilitates disparities in treatment in white perceivers, 56 and further, that racial bias in pain perception is exacerbated by bottom-up and top-down cues to racial prototypicality. 20…”

Section: Discussionmentioning

confidence: 99%

“…Existing painful expression sets are relatively homogenous across race and gender. This lack of diversity poses an obstacle to understanding and eliminating disparities in pain care 19 and stems from small sample sizes: Each set above comprises 50 or fewer subjects. The largest (BP4D-Spontaneous) contains only 20 white, 11 Asian, 6 black, and 4 Hispanic/Latinx subjects (23 female and 18 male 83 ), making well-powered comparisons across race and gender difficult.…”

Section: Introductionmentioning

confidence: 99%

The Delaware Pain Database: a set of painful expressions and corresponding norming data

Mende-Siedlecki

Qu-Lee

Lin

et al. 2020

PR9

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“…There exists a considerable amount of literature [2][3][4][5][6] dealing with the recognition and identification of affective states putatively communicated by facial expressions. Novel in the approach we present here are the following: (1) to use one AI tool to extract faces from video frames; (2) to use another AI tool (an autoencoder) to determine their dimensionreduced feature vectors; (3) to quantify their differences (if any); and (4) to implement the machinery of multivariate statistical analysis, combined with clustering algorithms, to detect noise contributions and identify patterns.…”

Section: Using Ai As a Novel Approach To Analyzing Facial Expressions...mentioning

confidence: 99%

Determination of “Neutral”–“Pain”, “Neutral”–“Pleasure”, and “Pleasure”–“Pain” Affective State Distances by Using AI Image Analysis of Facial Expressions

et al. 2022

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(1) Background: In addition to verbalizations, facial expressions advertise one’s affective state. There is an ongoing debate concerning the communicative value of the facial expressions of pain and of pleasure, and to what extent humans can distinguish between these. We introduce a novel method of analysis by replacing human ratings with outputs from image analysis software. (2) Methods: We use image analysis software to extract feature vectors of the facial expressions neutral, pain, and pleasure displayed by 20 actresses. We dimension-reduced these feature vectors, used singular value decomposition to eliminate noise, and then used hierarchical agglomerative clustering to detect patterns. (3) Results: The vector norms for pain–pleasure were rarely less than the distances pain–neutral and pleasure–neutral. The pain–pleasure distances were Weibull-distributed and noise contributed 10% to the signal. The noise-free distances clustered in four clusters and two isolates. (4) Conclusions: AI methods of image recognition are superior to human abilities in distinguishing between facial expressions of pain and pleasure. Statistical methods and hierarchical clustering offer possible explanations as to why humans fail. The reliability of commercial software, which attempts to identify facial expressions of affective states, can be improved by using the results of our analyses.

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The need for diversity in research on facial expressions of pain

Cited by 16 publications

References 27 publications

Observing Pain in Individuals with Cognitive Impairment: A Pilot Comparison Attempt across Countries and across Different Types of Cognitive Impairment

Observing Pain in Individuals with Cognitive Impairment: A Pilot Comparison Attempt across Countries and across Different Types of Cognitive Impairment

The Delaware Pain Database: a set of painful expressions and corresponding norming data

Determination of “Neutral”–“Pain”, “Neutral”–“Pleasure”, and “Pleasure”–“Pain” Affective State Distances by Using AI Image Analysis of Facial Expressions

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