Modulation of the sensory and affective dimensions of pain by expectations and uncertainty: a Bayesian modeling approach

Abstract

The perception of pain is sensitive to various mental processes such as expectation about the nociceptive stimulus or individual differences in the affective and cognitive evaluations of pain. A promising candidate to study the neurocomputational principles of pain perception is the theory of predictive processing which offers a general framework to understand perception and cognition. Rethinking the brain as a Bayesian inference organ, this theory has been recently applied to the experience of pain and its modulation with convincing results. We adapted an existing pain cueing paradigm to collect pain intensity and unpleasantness ratings on fifty-four healthy subjects with ongoing meditation experience, while manipulating expectations and uncertainty about impending electrical stimulations. Using state-of-the-art statistical modeling, we modeled the generation of trial-wise pain ratings as a Bayesian inference process integrating probability distributions over sensory information, cue-based expectations and a trait-like, individual prior about pain experience. Notably, we extended previous hierarchical Bayesian models of pain by successfully accounting for both the sensory and the affective components of pain. As predicted, psychological measures of pain catastrophizing and cognitive defusion showed opposite correlation patterns with their computational counterparts. Additionally, the lifetime meditation practice of our participants was strongly and inversely correlated with the weight of short-term expectations in the perceptual process, as well as with a trait-like prior influencing the affective dimension of pain. We conclude that this approach offers a promising avenue for future studies on meditation-induced plasticity of pain perception.

Expectations underlie the effects of unpredictable pain: a behavioral and electroencephalogram study

“…Future research may focus on individual variability in pain memory, a promising research avenue given that individual variability in expectations seems to improve pain predictions. 20,38…”

“…58 Indeed, plenty of empirical evidence has shown biases of pain perception toward the expected pain intensities. 11,17,20,25,32,38,44,56,57 Zaman et al 58 argued that participants in experimental conditions of unpredictability do not remain naive but build pain expectations based on past experiences, a statement in line with Bayesian inference 35 and predictive coding perspectives. 6,20,23,38,43 Unpredictability and expectations have also been shown to affect neural responses to thermonociceptive pain, but again with contradictory evidence.…”

“…11,17,20,25,32,38,44,56,57 Zaman et al 58 argued that participants in experimental conditions of unpredictability do not remain naive but build pain expectations based on past experiences, a statement in line with Bayesian inference 35 and predictive coding perspectives. 6,20,23,38,43 Unpredictability and expectations have also been shown to affect neural responses to thermonociceptive pain, but again with contradictory evidence. Although some studies have shown effective modulations of laser-evoked potentials by unpredictability or expectation, 8,17,28,46,52 other studies have not.…”

Expectations underlie the effects of unpredictable pain: a behavioral and EEG study

“…In the latter case, the short term expectations may account for the recent pain experience on a current task while the long term expectations may involve the personal history with pain prior to this task. Such models accounting for individual variability in pain expectations have shown enhanced performances in predicting actual pain ratings [21,39]. Therefore, future studies directly comparing different operationalizations of dynamic short and long term expectations on pain perception are a promising research avenue.…”

“…Indeed, plenty of empirical evidence has shown biases in pain perception towards the expected pain intensities [11,17,21,26,34,39,44,56,57]. Zaman et al [58] argued that participants in experimental conditions of unpredictability do not remain naïve but build pain expectations based on past experiences, a statement in line with Bayesian inference [37] and predictive coding perspectives [6,24,21,39,43].…”