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- W4366989433 abstract "In Bayesian and predictive coding frameworks, the magnitude and precision of pain expectancies are key determinants of perceptual experience However, relatively few studies have directly tested whether this holds for pain, and results so far have been inconclusive. Here, we investigated expectancy effects on pain experiences and affective responses. In two studies, healthy participants (n=30 in each study) received painful electrical stimuli preceded by explicit pain predictions. In study 1, the magnitude of pain predictions and administered pain intensities were varied. In study 2, the magnitude and precision of pain predictions were varied, while administered pain intensity was kept constant. Experienced pain intensity was the primary outcome in both studies. Study 1 found that pain experiences assimilated towards both under- and overpredictions of pain for moderate and very high pain stimuli, but this was not observed for non-painful stimuli. In addition, effects were not necessarily larger with predictions of greater magnitude. Study 2 found assimilation of pain experiences upon under- and overpredictions of pain, regardless of precision level, but no effects on EMG eyeblink startle responses. Moreover, results of both studies showed that under- and overpredictions caused disappointment and relief, respectively. In conclusion, the influence of pain predictions on pain may be more complex than assumed in current theoretical frameworks, with no systematically stronger assimilation of pain experiences to larger and more precise predictions. Since overpredictions are associated with relief, but underpredictions with disappointment, these findings underline the importance of providing realistic predictions when preparing for upcoming painful procedures." @default.
- W4366989433 created "2023-04-27" @default.
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- W4366989433 date "2023-04-24" @default.
- W4366989433 modified "2023-10-16" @default.
- W4366989433 title "How the magnitude and precision of pain predictions shape pain experiences" @default.
- W4366989433 doi "https://doi.org/10.31234/osf.io/bsp9a" @default.
- W4366989433 hasPublicationYear "2023" @default.
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