Sensitivity to pain expectations: A Bayesian model of individual differences.

Area of Science:

• Cognitive Neuroscience
• Pain Perception Research
• Computational Psychiatry

Background:

• Pain expectations, or thoughts and feelings about pain, are known to modify pain perception.
• The underlying cognitive mechanisms and individual variability in pain expectation effects remain poorly understood.

Purpose of the Study:

• To test a Bayesian model of pain perception using a prior-to-posterior updating process.
• To investigate the cognitive processes driving pain expectations and their impact on pain perception.
• To explore individual differences in pain expectation sensitivity.

Main Methods:

• Utilized data from a deception-free predictive cue task to evaluate a Bayesian computational model.
• Compared the predictive accuracy of the Bayesian model against simpler models of pain perception.
• Incorporated parameters for trait differences in pain expectation and allowed model parameters to vary individually.

Main Results:

• The Bayesian model significantly outperformed simpler models in predicting pain ratings.
• Confirmed that expectation alters perception and that uncertainty reduces expectation's impact, aligning with predictive coding principles.
• Individual-level modeling revealed significant contributions of trait differences and improved model fit, characterizing individual sensitivity to pain expectations.

Conclusions:

• A Bayesian framework effectively models pain perception, integrating predictive cues and individual traits.
• The findings highlight the role of cognitive processes and individual differences in modulating pain perception.
• This model offers a potential tool for understanding pain mechanisms and guiding clinical stratification.