Past experience influences judgment of pain: Prediction of sequential dependencies

Recent experience can influence judgments in a wide range of tasks, from reporting physical properties of stimuli to grading papers to evaluating movies. In this work, we analyze data from a task involving a series of judgments of pain (discomfort) made by participants who were asked to place their hands in a bowl of water of varying temperature. Although trials in this task were separated by a minute in order to avoid sequential dependencies, we nonetheless find that responses are reliably influenced by the recent trial history. We explore a space of statistical models to predict sequential dependencies, and show that a nonlinear autoregression using neural networks is able to predict over 6% of the response variability unrelated to the stimulus itself. We discuss the possibility of using decontamination procedures to remove this variability and thereby obtain more meaningful ratings from individuals.