Biased pain reports through vicarious information: A computational approach to investigate the role of uncertainty

Expectations about an impeding pain stimulus strongly shape its perception, yet the degree that uncertainty might affect perception is far less understood. To explore the influence of uncertainty on pain ratings, we performed a close replication of the study of Yoshida, Seymour, Koltzenburg, and Dolan (2013), who manipulated vicarious information about upcoming heat pain and found evidence for uncertainty-induced hyperalgesia. In our study, we presented eight fictitious ratings of previous participants prior the delivery of electrocutaneous pain. The vicarious information was either biased to over- or underreport pain levels based on the participant's psychometric function. We induced uncertainty by manipulating the variation of the vicarious information. As in Yoshida et al. (2013), four computational models were formulated, such that each model represented a different way of how the pain ratings might have been generated by the physical stimulus and the vicarious information. The four competing models were tested against the data of each participant separately. Using a formal model selection criterion, the best model was selected and interpreted. Contrary to the original study, the preferred model for the majority of participants suggested that pain ratings were biased towards the average vicarious information, ignoring the degree of uncertainty. Possible reasons for these diverging results are discussed.