For the study, participants were asked to choose between real painful stimuli in the form of electric shocks, and imagined painful dental appointments occurring at different times in the future.
The researchers found that most people chose to hasten the experience of pain — and would even accept more severe pain to avoid having to wait for it. However, there was a smaller percentage of people who preferred to “put it off” into the future.
The researchers note that the anticipation of pain is a major source of misery. People who suffer from long-standing painful conditions report that the dread of worsening future pain can be more disabling than the pain itself, they said.
The general phenomenon is typically referred to as “negative time preference,” according to Giles Story, Ph.D., who led the research team in the quest to better understand the fundamental processes by which people anticipate pain.
The researchers propose that the dread of pain increases as the predicted time of pain approaches. In their study, they demonstrated that if people focus only on the approaching pain, they will choose to defer pain into the future, if possible, to reduce their immediate dread.
However, if people also take into account the dread they may experience waiting for a painful event, the unpleasantness of a prolonged period of dread may exceed the unpleasantness of the pain itself, according to the researchers.
They note that their study shows that, in such cases, the prospect of pain becomes more unpleasant the more the pain is delayed, leading people to choose to expedite unavoidable pain.
Further research is required to uncover the mechanisms of dread, according to the researchers.
They note that a greater understanding of these mechanisms could be helpful for clinicians and health policy makers in finding a way in which potentially painful investigations and treatments are practiced.
The study was conducted at the Institute for Global Health Innovation at Imperial College London, and the Wellcome Trust Centre for Neuroimaging at the University College London.
Source: PLOS Computational Biology