Sensitivity to pain expectations: A Bayesian model of individual differences.

The thoughts and feelings people have about pain (referred to as 'pain expectations') are known to alter the perception of pain. However little is known about the cognitive processes that underpin pain expectations, or what drives the differing effect that pain expectations have between individuals. This paper details the testing of a model of pain perception which formalises the response to pain in terms of a Bayesian prior-to-posterior updating process. Using data acquired from a short and deception-free predictive cue task, it was found that this Bayesian model predicted ratings of pain better than other, simpler models. At the group level, the results confirmed two core predictions of predictive coding; that expectation alters perception, and that increased uncertainty in the expectation reduces its impact on perception. The addition of parameters relating to trait differences in pain expectation improved the fit of the model, suggesting that such traits play a significant role in perception above and beyond the influence of expectations triggered by predictive cues. When the model parameters were allowed to vary by participant, the model's fit improved further. This final model produced a characterisation of each individual's sensitivity to pain expectations. This model is relevant for the understanding of the cognitive basis of pain expectations and could potentially act as a useful tool for guiding patient stratification and clinical experimentation.

Pubmed ID: 30243037