Human pain perception is not solely driven by sensory input but is dynamically modulated by what we expect to feel and how confident we are in those expectations. Yet, the temporal mechanisms through which evolving expectations shape pain remain poorly understood. Here, we combined a probabilistic cueing paradigm with computational modeling and EEG to dissociate two core components of expectation: strength (a recency-weighted estimate of predicted pain) and precision (the inverse variability of recent predictions). Trial-wise strength estimates closely tracked subjective expectations and outperformed static cue labels, validating the model’s psychological relevance. Expectation strength and precision exerted dissociable effects on pain processing: strength enhanced, whereas precision suppressed, pain-evoked responses. Critically, anticipatory α-band activity mediated these effects via distinct topographical patterns—expectation strength reduced fronto-central α power (reflecting heightened vigilance), while precision increased contralateral sensorimotor α-synchronization (supporting sensory gating). Source-level mediation analyses identified a right-lateralized dorsolateral prefrontal–sensorimotor cortices (DLPFC-SM1) integrating both components, with strength-specific engagement of the medial prefrontal cortex (mPFC). These effects were supported by Bayesian inference and pooled mega-analyses, underscoring their robustness. Together, these findings highlight cortical α-oscillations as dual-control mechanisms for predictive integration, with DLPFC–SM1 as a shared expectation hub and mPFC as a strength-specific node. By moving beyond static cue-based models, this framework captures the adaptive dynamics of expectation and provides a neurocomputational foundation for targeted interventions in chronic pain.
Li et al. (Mon,) studied this question.