Post by Elisa Guma

What's the science?

Our past experiences often shape our expectations of the future, which in turn can influence how we perceive future events. Previous work has shown that our expectation of how painful a stimulus will be can influence our perception of that stimulus and our response to it. Moreover, expectations about pain (e.g., following placebo treatments) can be surprisingly resistant to change, acting much like self-fulfilling prophecies. The brain and behavioural mechanisms underlying these phenomena are largely unknown. This week in Nature: Human Behavior Jepma and colleagues used behavioural assessments and functional magnetic resonance imaging (fMRI) to investigate how expectations about pain affect pain experience, and why expectations of high or low pain sometimes persist despite evidence to the contrary.

How did they do it?

The authors designed two experiments in which they independently manipulated predictive pain cues (to investigate pain expectation), and the intensity of a pain stimulus (to investigate pain perception). In both studies, participants first went through a learning phase where they learned to associate abstract visual cues with either low or high temperatures (displayed on thermometers). In the subsequent test phase participants were presented with both sets of cues followed by a painful heat stimulus to the inner forearm in the first study, and to the lower leg in the second study. Unbeknownst to the participants, the cues were no longer predictive of heat intensity. Participants were instructed to rate how much pain they expected following each cue, and how much pain they experienced following each heat stimulus. In the second study, fMRI activity was also recorded. The authors tested responses in a measure called the Neurologic Pain Signature (NPS); a measure of activity across brain areas validated to be sensitive and specific to pain in tests performed to date. They used these brain areas to guide their investigation of brain signatures of pain perception and expectation in this study. Finally, the authors used computational models to quantify their findings, using both a reinforcement learning model and a Bayesian model.