Validating a measure of the precision of ascending signals in pain perception

Pain perception can be described as a process of Bayesian inference, which generates sensory estimates based on prior expectations and afferent information. The inference is affected by within-individual variations in the precision (inverse variance) of the distribution of centrally predicted ascending noxious signals. While the top-down effect of priors (expectations and beliefs) on pain perception has received much attention within the Bayesian framework, there remains a lack of validated quantitative measures that capture within-individual variations in the likelihood function. Using a 2 × 2 fully factorial within-individual design, we measured and compared the precision of the likelihood function in four tasks administered to 57 healthy adults: the cued pain task (CPT) and the Focused Analgesia Selection Test (FAST), in two noxious modalities, thermal and electrical. A hierarchical Bayesian model was applied to the CPT, and the FAST was employed as a validation criterion, given that it is known to correlate with clinical pain reports and the placebo response. Individuals with a more precise representation of ascending sensory signals in the CPT produced less variable pain reports in the FAST. We validated the result by replicating this correlation across thermal and electrical pain. These results support the validity of our approach to the measurement of the precision of ascending noxious signals. Their correlation with FAST scores supports their criterion validity, and their correlation across noxious sub-modalities supports the concurrent validity of this measurement. Quantifying the precision of noxious inputs could inform work on placebo sensitivity and strengthen the assay sensitivity of randomized clinical trials involving pain.