The desire to predict discoveries--to have some idea, in advance, of what will be discovered, by whom, when, and where--pervades nearly all aspects of modern science, from individual scientists to publishers, from funding agencies to hiring committees. In this Essay, we survey the emerging and interdisciplinary field of the "science of science" and what it teaches us about the predictability of scientific discovery. We then discuss future opportunities for improving predictions derived from the science of science and its potential impact, positive and negative, on the scientific community.
A major challenge for using data to make predictions is distinguishing what is meaningful from noise. As this special section explores, prediction is now a developing science. Social scientists and the machine learning community are acquiring new analytical tools to distinguish meaningful patterns from noise. Several authors in this special section describe the importance of realistic goals that seek to balance machine learning approaches with the human element.
Phenomena such as placebo analgesia or pain relief through distraction highlight the powerful influence cognitive processes and learning mechanisms have on the way we perceive pain. Although contemporary models of pain acknowledge that pain is not a direct readout of nociceptive input, the neuronal processes underlying cognitive modulation are not yet fully understood. Modern concepts of perception--which include computational modeling to quantify the influence of cognitive processes--suggest that perception is critically determined by expectations and their modification through learning. Research on pain has just begun to embrace this view. Insights into these processes promise to open up new avenues to pain prevention and treatment by harnessing the power of the mind.