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.

The auditory mismatch negativity (MMN) is a well characterized event-related potential component which has gained recent attention in theoretical models describing the impact of various styles of mindfulness meditation on attentional processes and perceptual inference. Previous findings highlighted a differential modulation of the MMN amplitude by meditation states and degrees of expertise. In the present study, we attempted to replicate results from the recent literature with a data sample that allowed for increased statistical power compared to previous experiments. Relying on traditional frequentist analysis, we found no effects of meditation states and expertise on the auditory MMN amplitude, non-replicating our previous work (Fucci et al., 2018). Using a Bayesian approach, we found strong evidence against an interaction effect on the MMN amplitude between expertise groups and meditation states and only moderate evidence in favour of a weak effect of expertise during focused attention practice. On the other hand, we replicated previous evidence of increased alpha oscillatory power during meditation practices compared to a control state. We discuss our null findings in relation to factors that could undermine the replicability of previous research on this subject, namely low statistical power, use of flexible analysis methods and a possible publication bias leading to a misrepresentation of the available evidence.