Predictive Dynamics of Human Pain Perception

While the static magnitude of thermal pain perception has been shown to follow a power-law function of the temperature, its dynamical features have been largely overlooked. Due to the slow temporal experience of pain, multiple studies now show that the time evolution of its magnitude can be captured with continuous online ratings. Here we use such ratings to model quantitatively the temporal dynamics of thermal pain perception. We show that a differential equation captures the details of the temporal evolution in pain ratings in individual subjects for different stimulus pattern complexities, and also demonstrates strong predictive power to infer pain ratings, including readouts based only on brain functional images. Author Summary: We propose a model of thermal pain perception that accounts for its dynamical behavior, and can be used to predict subjective responses to thermal stimulation on individual subjects with high accuracy, close to 90% averaged over subjects (over 65% for the null hypothesis). The model is based on behavioral considerations that include the need to signal current or approaching tissue damage, and the need to discount past danger. Moreover, we show that in a ‘mind reading’ setting, the combined use of sparse regression to infer pain perception from functional MRI recordings (fMRI), and from the model applied to the stimulus temperature also inferred from fMRI, leads to equally significant predictive accuracy, close to 75% averaged over subjects. Our results demonstrate that a subjective percept such as pain displays a highly deterministic behavior.