The Confidence Database

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects

Sociocultural and cognitive influences of pain expression and assessment

Background: Health inequities in pain are rampant in the United States. Historically marginalized populations (HMPs) experience increased levels of pain, pain more often, and receive less pain treatment. Health inequities are largely based on verbal reports, suggesting providers are not standardized in their assessments of verbal pain. As pain is a complex phenomenon, it is important to understand the decision-making process underlying verbal self-report and to identify potential ways nonverbal information might impact both a person’s real and expected pain outcomes and how perceivers assess pain in another individual. Aim: Across four studies, we aimed to assess how individuals experience and report pain and how individuals perceive pain in others. We aimed to identify 1) how provider’s facial characteristics might impact a persons expected pain outcomes and 2) whether individuals varied in their confidence in their verbal self-reports of pain. We also aimed to 3) identify if individuals bias their pain assessment outcomes by sociocultural effects when presented computer-generated images of pain-related expressions and 4) when presented videos of real acute pain. Methods: We used several experimental methodologies and tools to assess pain experience and pain assessment. We used an experimental paradigm of acute heat pain to assess verbal self-reports of real acute pain and participant’s confidence in their pain reports. We used online methods, by presenting facial images and asking for perceiver self-reports, to assess how individuals’ perceptions of potential providers impact expected pain outcomes and how individuals perceive pain in computer-generated images. Online methods allowed us to reach a more general sample, across the United States and to safely collect data during the COVID- 19 pandemic. Finally, we used a mixed-methods approach, by running online sessions with telehealth visits to interact with each of our online participants. We investigated how individuals assess videos of acute heat pain and this mixed-methods approach allowed us to: collect more information from each participant, reinforce the instructions and verify task understanding, and to continue safe data collection during the COVID-19 pandemic. Results: Individuals exhibited influences in their expected pain and medication outcomes in an online task in which participants were presented several potential providers. Participants expected less pain and medication use when viewing similar providers. However, the effect of perceived similarity was stronger for White compared to other racialized participants. Individuals also reported high levels of confidence in their verbal self-reports of pain during an in-person pain calibration task with aversive heat stimulation. Although reports of confidence were high, we did observe significant variability in explicit reports of confidence that associated with pain rating reaction time. We also observed biases in our participants when they assessed pain in others. Although we did not observe sociocultural biases in pain assessment to computer-generated images, we did observe biases in pain perception compared to other emotions. Pain was rarely perceived (7%) when it was equally presented compared to other basic emotions. We also observed biases by race and gender in emotionrelated decisions. Finally, when we presented participants with videos of acute heat pain, as opposed to stills of computer-generated images, perceivers exhibited typical biases by target race. Perceivers were less accurate, saw pain less often, and reported less intense pain in Black targets compared to White targets. Racial biases pain categorizations were increased by perceived similarity and racial biases in pain intensity judgments were increased with greater endorsement in explicit racism via the Modern Racism Scale. Finally, we observed atypical gender biases, such that perceivers reported pain more often and more intense pain in women targets compared to men. Conclusions: Individuals are biased in their expected pain outcomes based on the facial traits of potential providers, suggesting a need to diversify the medical workforce and the potential importance for patient input in selecting their providers. Individuals also typically report pain ratings with high levels of confidence; however, variability does exist and suggests confidence is another measure of verbal report that can be probed by researchers and providers alike. Finally, our results suggest that individuals can have difficulty identifying pain in pain-related expressions without priming or context. However, using more ecologically valid stimuli (i.e., the videos of real acute pain that we created), allowed us to experimentally extend pain assessment biases by race to experimental settings and indicate racial bias as a potential target for pain assessment bias intervention

How pain-related facial expressions are evaluated in relation to gender, race, and emotion

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

Video-based acute pain assessment is subject to sociocultural biases

In the United States, historically marginalized populations experience worse and more frequent pain and receive less pain treatment. Recent efforts have investigated inequities in pain care; however, without diverse databases of nonverbal pain behaviors, prior research has been unable to identify the role of biases in assessments of nonverbal reactions to real physical pain. We asked whether sociocultural biases impact pain assessment in sixty ‘perceivers’ by measuring estimated pain in response to videos of healthy ‘targets’ who experienced noxious heat. Videos were matched for pain intensity across four sociodemographic subgroups: White men, White women, Black men, and Black women targets. Perceivers attributed pain more often (β = .41, p < .001) and higher intensities (β = 8.08, p < .001) to White targets than Black targets. The effect on pain perception was impacted by perceived similarity (β = -2.08, p = .001) and pain intensity was associated with explicit racial bias (β = -.02, p = .006). In contrast to documented gender disparities, perceivers attributed pain more often (β = .24, p < .001) and higher intensities (β = 3.23, p = .003) for women compared to men. Finally, perceivers were more accurate when evaluating White compared to Black targets (p < .001), and this was driven by differences in accuracy when viewing male targets (p = .002). Our study provides an experimental model to assess biases of real nonverbal pain assessment and suggests nonverbal biases are consistent with historically documented racial biases but counter gender biases in pain care

Test-retest reliability of an adaptive thermal pain calibration procedure in healthy volunteers

Quantitative sensory testing (QST) allows researchers to evaluate associations between noxious stimuli and acute pain in clinical populations and healthy participants. Despite its widespread use, our understanding of QST’s reliability is limited, as reliability studies have used small samples and restricted time windows. We examined the reliability of pain ratings in response to noxious thermal stimulation in 171 healthy volunteers (n = 99 female, n = 72 male) who completed QST on multiple visits ranging from 1 day to 952 days between visits. On each visit, participants underwent an adaptive pain calibration in which they experienced 24 heat trials and rated pain intensity after stimulus offset on a 0-10 Visual Analog Scale. We used linear regression to determine pain threshold, pain tolerance, and the correlation between temperature and pain for each session and examined the reliability of these measures. Threshold and tolerance were moderately reliable (Intra-class correlation [ICC]=0.66 and 0.67, respectively; p<.001), whereas temperature-pain correlations had low reliability (ICC=0.23). In addition, pain tolerance was significantly more reliable in female participants than male participants, and we observed similar trends for other pain sensitive measures. Our findings indicate that threshold and tolerance are largely consistent across visits, whereas sensitivity to changes in temperature vary over time and may be influenced by contextual factors

The Confidence Database.

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects

The Confidence Database

Understanding how people rate their confidence is critical for characterizing a wide range of perceptual, memory, motor, and cognitive processes. However, as in many other fields, progress has been slowed by the difficulty of collecting new data and the unavailability of existing data. To address this issue, we created a large database of confidence studies spanning a broad set of paradigms, participant populations, and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analyzed in multiple software packages. Each dataset is further accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (available at osf.io/s46pr) contained 145 datasets with data from over 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. We show the usefulness of this large collection of datasets in four different analyses that provide precise estimation for several foundational confidence-related effects and lead to new findings that depend on the availability of large quantity of data. This Confidence Database will continue to enable new discoveries and can serve as a blueprint for similar databases in related fields