# Dynamic learning of the meaning of information changes pain perception

## TL;DR

This study shows that people's perception of pain changes based on how their beliefs about a situation differ from reality, and that this process is best explained by a dynamic learning model.

## Contribution

The study introduces a novel Bayesian reinforcement learning model to explain how pain perception is dynamically updated based on cue values.

## Key findings

- Pain intensity perception is best explained by a Bayesian reinforcement learning model where cue values are updated dynamically.
- Participants' beliefs about information cues change over time through trial-by-trial updates, even when instructed otherwise.
- Deception in pain studies should not assume fixed participant beliefs.

## Abstract

Beliefs influence the intensity of perceived pain. We previously applied regression models to two independent pain-cueing datasets (https://osf.io/5r6z7/) and found that pain intensity reports were a function of the error between participants’ beliefs and the actual stimulation they received, such that greater error decreased the influence of prior beliefs. Although this result appeared to present a challenge to established models of perception, our former analyses did not formally compare the result against established computational models. Here (https://osf.io/fj27k/) we compared a model that corresponded to our original interpretation against Bayesian reinforcement learning models. We found that pain intensity perception was best explained by a model in which the expected value of each information cue was updated via a Bayesian reinforcement learning algorithm. In addition to their importance for understanding the fundamental mechanism that underlies pain perception, these new results indicate that deception pain studies should not assume that participants’ belief about the information they are given is fixed. Rather, our results provide evidence that the meaning of cues change dynamically over the course of the session through trial-by-trial updates, even when participants are instructed otherwise.

The online version contains supplementary material available at 10.1038/s41598-025-14299-z.