Forming and Updating Pain Expectations: Influence of Sequence Volatility and Test-Retest Reliability
(2024) IASP 2024 World Congress on Pain — Location: Amsterdam (Netherlands) (5.August.2025)
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- Background and aims (1000 char) Pain expectations are formed by learning (from past experience and environmental cues) the association between specific circumstances and the occurrence of pain. These expectations are known to influence pain perception. Whether this learning is statistically optimal remains debated. If it is, the learning process of the participant should reflect their perception of the precision of each observation (stochasticity) and of the instability of the environment (volatility). Further, it is unclear whether inter-individual differences observed in experimental pain learning studies are due to temporary states or intrinsic traits. If they reflect traits, they could account for the wide variability in pain experiences across individuals and might be a feature of chronic pain conditions. Using a novel pain learning task (with periods of high and low volatility) and computational modeling, we aimed to probe the learning strategy and temporal stability of the learning parameters of participants. Methods (1000 char) Fifty healthy volunteers participated in 2 sessions, 1 week apart. At each session, participants completed a reversal learning task in which they learnt probabilistic associations between 2 arbitrary visual cues and 2 stimulus intensities (selected to be reliably perceived as either non-burning or burning). The task included 160 trials and 10 reversals. We constructed several alternative computational models, corresponding to specific hypotheses on how expectations are translated into predictions, the effect of expectations on stimulus recognition, and the algorithm used by humans to update their expectations. These models were fitted on the cue-stimulus contingencies, participant responses to the prediction, recognition, and rating questions, as well as reaction times. Once model fitting is complete, the best model will be selected through model comparison (loo). We will use ICC to assess the test-retest reliability of individual learning parameters derived from the winning model. Results (1000 char) Data collection is complete but modeling is still ongoing. Manipulation checks showed the following expected patterns (all p<10⁴): participants were able to predict the next stimulus above chance level based on the visual cue; they accurately identified the stimulus types above chance level; non-painful stimuli were consistently rated as less intense than the painful ones; as per predictive coding, recognition accuracy improved when the stimulus matched the participant's prediction and intensity ratings were biased towards these predictions; and, finally, incorrect predictions or recognitions were associated with longer reaction times to the corresponding questions, indexing harder decision making. Preliminary results, derived from a reduced set of models, suggest that humans adapt their pain learning rate based on their perception of the sequence volatility (p=0.0005) and indicate poor-to-excellent test-retest reliability of pain learning parameters (ICC CIs ranging from <0.4 to 1). Conclusions (1000 char) Manipulation checks and preliminary results appear to suggest that human agents process expectations in a statistically optimal manner. This includes both the updating of expectations, which appeared to follow a Bayesian rule accounting for changes in the sequence volatility, and their integration with sensory evidence during stimulus recognition, which seemed consistent with predictive coding principles. Another important finding is that only some of the learning parameters may be reliable over time. If confirmed, this would indicate that these parameters may reflect transient states rather than intrinsic traits. Ethics (1000 char) This study was approved by the local ethics review board and complies with the Declaration of Helsinki. All participants provided written informed consent prior to the beginning of the experiment. Relevance for patient care (1000 char) Expectations seem to strongly modulate pain perception, as exemplified by the well-known placebo and nocebo effects. Better understanding how pain expectations are formed and updated and how they influence pain perception could help us further our understanding of endogenous pain modulation pathways and, eventually, design interventions harnessing these mechanisms for pain relief. By allowing trial-by-trial modeling of expectation trajectories, the task and computational framework developed in this project could prove useful to precisely probe, using a model based approach, the neurophysiological correlates of pain perception and its biasing by expectations.
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Courtin, A., & et al. (2024). Forming and Updating Pain Expectations: Influence of Sequence Volatility and Test-Retest Reliability. IASP 2024 World Congress on Pain, Amsterdam (Netherlands). https://hdl.handle.net/2078.5/239645