Certainty in ascending sensory signals – The unexplored driver of analgesic placebo response

P. Kuperman, D. Talmi, Np Katz, R. Treister

Research output: Contribution to journalArticlepeer-review


Previous frameworks have failed to adequately explain the observed correlation between within-subject variability in pain reporting and analgesic placebo response. These relationships have been observed in both clinical and experimental setups. Within-subject variability of clinical pain scores is traditionally assessed based on daily pain diaries collected during the pre-intervention stage. Experimental variability can be assessed by the Focused Analgesia Selection Test (FAST), which calculates the relationship between noxious stimuli administrated at various intensities and pain reports. The variability, either clinical or experimental, has been shown to predict the placebo response. In explaining the placebo response, Bayesian Brain Hypothesis (BBH) posits that pain perception (posterior), is composed of certainty (precision) of expectations (priors due to belief or conditioning) and incoming sensory information (likelihood), with the bulk of research focused on the precision of priors. Virtually all placebo analgesia research has focused on the priors and their certainty, rather than on the certainty of the likelihood, mainly because it cannot be assessed directly. We propose that the within-subject variability, as encapsulated by the FAST, is a proxy for certainty in (or, precision of) ascending sensory signals, and our results suggest that it could not only be assessed, but also manipulated. If true, our hypothesis will facilitate new lines of research and could potentially promote precision analgesic medicine by use of variability of pain scores as a diagnostic method to identify pain patients who will benefit from specific treatments.

Original languageEnglish
Article number110113
JournalMedical Hypotheses
StatePublished - Oct 2020

Bibliographical note

Publisher Copyright:
© 2020 Elsevier Ltd

ASJC Scopus subject areas

  • General Medicine


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