Managing Pain

Estimating others' pain is a challenging inferential process, associated with a high degree of uncertainty. The present study exploited models of probabilistic decision-making to investigate how uncertainty influences the assessment of another’s pain. We engaged sixty-three dyads (43 strangers and 20 romantic couples) in a task where individual choices affected the pain delivered to either oneself (the agent) or the other member of the dyad. At each trial, agents were presented with cues predicting a given pain intensity with an associated probability of occurrences. Agents chose either a sure (mild decrease of pain) or risky (50% chance of avoiding pain altogether) management option, following which they were asked to bid on their choice. A heat stimulation was then issued to the target (self or other), whose intensity was rated. We found that, the higher the expected pain, the more risk-averse agents became, in line with findings in value-based decision-making. Furthermore, agents gambled less on another individual’s pain (especially strangers) and placed higher bids on pain relief than they did for themselves. Most critically, the uncertainty associated with expected pain dampened ratings made for strangers’ pain. This contrasted with the effect on an agent’s own pain, for which risk had a marginal hyperalgesic effect. Overall, our results suggested that risk selectively affects decision-making on a stranger’s suffering, both at the level of assessment and treatment selection, by 1) leading to underestimation, 2) privileging sure options, and 3) altruistically allocating more money to insure the treatment’s success.

Healthcare providers often underestimate patients’ pain, sometimes even when aware of their reports. This could be the effect of experience reducing sensitivity to others pain, or distrust toward patients’ self-evaluations. Across multiple experiments (375 participants), we tested whether senior medical students differed from younger colleagues and lay controls in the way they assess people’s pain and take into consideration their feedback. We found that medical training affected the sensitivity to pain faces, an effect shown by the lower ratings and highlighted by a decrease in neural response of the insula and cingulate cortex. Instead, distrust toward the expressions’ authenticity affected the processing of feedbacks, by decreasing activity in the ventral striatum whenever patients’ self-reports matched participants’ evaluations, and by promoting strong reliance on the opinion of other doctors. Overall, our study underscores the multiple processes which might influence the evaluation of others’ pain at the early stages of medical career.

Medical students and professional healthcare providers often underestimate patients’ pain, an effect associated with decreased neural response of the anterior insula to pain information. However, the functional significance of these neural modulations is still debated. We recruited university medical students and emergency caregivers to test the role of healthcare experience on the behavioral/neural reactivity to other’s pain, emotions, and beliefs. We confirmed that healthcare experience decreased the sensitivity to others’ suffering, as measured by subjective ratings and insular response. This effect was independent from stimulus modality (pictures, texts), but specific for pain, as it did not generalize to emotions or beliefs. Critically, multivariate pattern analysis revealed that healthcare experience impacted specifically a component of the neural representation of others’ pain shared with that of first-hand nociception. This suggests a reduced likelihood of appraising others’ sufferance as one’s own, and might offer a mechanistic explanation for pain underestimation in clinical settings.

TThe ongoing Covid-19 pandemic has demanded a degree of sacrifice from individuals for the sake of the greater good. Individuals have taken costly actions, both volitional and imposed, to reduce harm to strangers. While many studies have examined health decision-making by experts, the study of individual, non-expert decision-making on a stranger’s health has been left to the wayside, as ordinary citizens are usually not tasked with such decisions. The recent pandemic has brought this dilemma to the fore however, as life-saving decisions fell to ordinary citizens in the form of social restrictions, decreased work activity and, ultimately, economic loss. We administered two surveys where we applied models of probabilistic decision-making to investigate health-related choices for oneself, a loved one and a stranger. We found converging evidence that participants were risk-seeking overall, privileging risky treatments that could heal someone over treatments that reduce disease severity with certainty. Nonetheless, risk-seeking decreased with expected disutility of disease, regardless of target. However, distinctions across targets emerged when decisions were conditioned on treatment cost, with participants preferring cheaper options for strangers. Overall our data suggest that 1) individuals apply an expected utility model to quantify disease; 2) risk preferences for others parallel those for the self; and 3) decisions for strangers’ health differ from those of self and loved ones only in terms of their associated cost. These findings provide a descriptive model of individual risky decision-making for self and others, in the case of a novel disease; and inform on the limits of what can be asked of an individual in service to a stranger.

Pain inadequate treatment is frequent in modern society, with major medical, ethical, and financial implications. In many healthcare environments, pain is quantified prevalently through subjective measures, such as self-reports from patients or health care providers' personal experience. Recently, automatic diagnostic tools have been developed to detect and quantify pain more “objectively” from facial expressions. However, it is still unclear if these approaches can distinguish pain from other aversive (but painless) states. In the present study, we analyzed the facial responses from a database of video-recorded facial reactions evoked by comparably-upleasant painful and disgusting stimuli. We modeled this information as function of subjective unpleasantness, as well as the specific state evoked by the stimuli (pain vs. disgust). Results show that a machine learning algorithm could predict subjective pain unpleasantness from facial information, but mistakenly detected unpleasant disgust, especially in those models relying in great extent on the brow lowerer. Importantly, pain and disgust could be disentangled using an ad hoc algorithm that rely on combined information from the eyes and the mouth. Overall, the facial expression of pain contains both specific and unpleasantness-related information shared with disgust. Automatic diagnostic tools should be guided to account for this confounding effect.

Pain undertreatment, or oligoanalgesia, is frequent in the emergency department (ED), with major medical, ethical, and financial implications. Across different hospitals, healthcare providers have been reported to differ considerably in the ways in which they recognise and manage pain, with some prescribing analgesics far less frequently than others. However, factors that could explain this variability remain poorly understood. Here, we used neuroscience approaches for neural signal modelling to investigate whether individual decisions in the ED could be explained in terms of brain patterns related to empathy, risk-taking, and error monitoring. For 15 months, we monitored the pain management behaviour of 70 ED nurses at triage, and subsequently invited 33 to a neuroimaging study involving three well-established tasks probing relevant cognitive and affective dimensions. Univariate and multivariate regressions were used to predict pain management decisions from neural activity during these tasks. We found that the brain signal recorded when empathising with others predicted the frequency with which nurses documented pain in their patients. In addition, neural activity sensitive to errors and negative outcomes predicted the frequency with which nurses denied analgesia by registering potential side-effects. These results highlight the multiple processes underlying pain management, and suggest that the neural representations of others' states and one’s errors play a key role in individual treatment decisions. Neuroscience models of social cognition and decision-making are a powerful tool to explain clinical behaviour and might be used to guide future educational programs to improve pain management in ED.