Bayesian Model of Subjective Pain Experience

Pain expectations play a critical role in shaping the experience of pain and influence pain relief outcomes in patients with persistent pain. These expectations arise from prior experiences, emotions, and contextual factors, forming a complex mind-body interaction that has been challenging to quantify. Traditional methods, such as clinical interviews and self-report questionnaires, provide valuable insights but are limited by introspective variability and social desirability biases.

We have developed an experimental procedure that objectively measures the impact of expectations on pain perception. Participants receive electric pain stimulations to the back of the hand, individualized to their pain threshold and tolerance. Prior to each stimulation, probabilistic cues indicate the likely intensity, systematically biasing pain ratings. For example, a stimulus perceived as a ‘5’ on a 1–10 scale is rated lower when preceded by a cue suggesting a 50% chance of ‘4/5’ than when preceded by a ‘5/6’ cue.

These pain reports are analyzed using a Bayesian computational model, which quantifies expectation effects at both state and trait levels. This model has been validated across multiple datasets, demonstrating superior predictive power compared to alternative models. It has also been tested in independent studies, including research on meditation practitioners, and has shown stability across time and different types of noxious stimulation. Refinements to the model have addressed previous implementation issues, improving parameter estimation through hierarchical priors.

This work provides a robust, quantitative framework for studying the cognitive and affective influences on pain perception, with potential applications in both clinical and research settings.

Pain Exp

Dan Acosta-Kane developed the models used in the Hoskin et al. (2019) Cognition paper. He has now revised them to make them more accessible.


 

The code is available to download from here.


 

To Run the code:


 

devtools::install_github("dan-ak/bayesian-pain")

library(BayesianPain)


 

data_loc <- system.file("extdata", "exp_1_data.csv", package="BayesianPain")

exp_data <- BayesianPain::center_scale(data.frame(read.csv( data_loc , header = TRUE)))

stan_data <- BayesianPain::create_stan_data(exp_data)

models <- BayesianPain::get_models(c(1,3,4,5,6))

output <- BayesianPain::fit_models(models, stan_data, iter=700, chains = 4)


 

output['DICs']

BayesianPain::get_parameters(output['fits'])

Our Publications in this Area:

 

Hird, E.J., Charalambous, C., El-Deredy, W. et al. Dynamic learning of the meaning of information changes pain perception. Sci Rep 15, 35786 (2025). https://doi.org/10.1038/s41598-025-14299-z

 

Singh, A., Treister, R., Charalambous, C., Mancini, F., & Talmi, D. (2025). EXPRESS: Validating a measure of the precision of ascending signals in pain perception. Quarterly Journal of Experimental Psychology. https://doi.org/10.1177/17470218251343863

 

Poublan‐Couzardot, A., & Talmi, D. (2024). Pain perception as hierarchical Bayesian inference: A test case for the theory of constructed emotion. Annals of the New York Academy of Sciences, 1536(1), 42–59. https://doi.org/10.1111/nyas.15141

 

Hoskin, R., Pernet, C., & Talmi, D. (2023). Interactions between the Representations of Pain and Reward Suggest Dynamic Shifts in Reference Point. SSRN. https://doi.org/10.2139/ssrn.4518575

 

Hoskin, R., & Talmi, D. (2023). Adaptive coding of pain prediction error in the anterior insula. European Journal of Pain, 27(6), 766–778. https://doi.org/10.1002/ejp.2093

 

Delgado-Sanchez, A., Brown, C., Sivan, M., Talmi, D., Charalambous, C., & Jones, A. (2023). Are we any closer to understanding how chronic pain develops? A systematic search and critical narrative review of existing chronic pain vulnerability models. Journal of Pain Research, Volume 16, 3145–3166. https://doi.org/10.2147/jpr.s411628

 

Delgado‐Sanchez, A., Charalambous, C., Trujillo‐Barreto, N. J., Safi, H., Jones, A., Sivan, M., Talmi, D., & Brown, C. (2023). Test–retest reliability of Bayesian estimations of the effects of stimulation, prior information and individual traits on pain perception. European Journal of Pain, 28(3), 434–453. https://doi.org/10.1002/ejp.2193

 

Hird, E. J., Charalambous, C., El-Deredy, W., Jones, A. K. P., & Talmi, D. (2019). Boundary effects of expectation in human pain perception. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-45811-x

 

Hoskin, R., Berzuini, C., Acosta-Kane, D., El-Deredy, W., Guo, H., & Talmi, D. (2018). Sensitivity to pain expectations: A Bayesian model of individual differences. Cognition, 182, 127–139. https://doi.org/10.1016/j.cognition.2018.08.022

 

Hird, E., Jones, A., Talmi, D., & El-Deredy, W. (2017). A comparison between the neural correlates of laser and electric pain stimulation and their modulation by expectation. Journal of Neuroscience Methods, 293, 117–127. https://doi.org/10.1016/j.jneumeth.2017.09.011

 

Talmi, D., Dayan, P., Kiebel, S. J., Frith, C. D., & Dolan, R. J. (2009). How Humans Integrate the Prospects of Pain and Reward during Choice. Journal of Neuroscience, 29(46), 14617–14626. https://doi.org/10.1523/jneurosci.2026-09.2009