The computational principles by which the brain creates a painful experience from nociception are still unknown. Classic theories suggest that cortical regions either reflect stimulus intensity or additive effects of intensity and expectations, respectively. By contrast, predictive coding theories provide a unified framework explaining how perception is shaped by the integration of beliefs about the world with mismatches resulting from the comparison of these beliefs against sensory input. Using functional magnetic resonance imaging during a probabilistic heat pain paradigm, we investigated which computations underlie pain perception. Skin conductance, pupil dilation, and anterior insula responses to cued pain stimuli strictly followed the response patterns hypothesized by the predictive coding model, whereas posterior insula encoded stimulus intensity. This novel functional dissociation of pain processing within the insula together with previously observed alterations in chronic pain offer a novel interpretation of aberrant pain processing as disturbed weighting of predictions and prediction errors.DOI: http://dx.doi.org/10.7554/eLife.24770.001

It has recently been suggested that learning signals in the amygdala might be best characterized by attentional theories of associative learning [such as Pearce-Hall (PH)] and more recent hybrid variants that combine Rescorla-Wagner and PH learning models. In these models, unsigned prediction errors (PEs) determine the associability of a cue, which is used in turn to control learning of outcome expectations dynamically and reflects a function of the reliability of prior outcome predictions. Here, we employed an aversive Pavlovian reversal-learning task to investigate computational signals derived from such a hybrid model. Unlike previous accounts, our paradigm allowed for the separate assessment of associability at the time of cue presentation and PEs at the time of outcome. We combined this approach with high-resolution functional magnetic resonance imaging to understand how different subregions of the human amygdala contribute to associative learning. Signal changes in the corticomedial amygdala and in the midbrain represented unsigned PEs at the time of outcome showing increased responses irrespective of whether a shock was unexpectedly administered or omitted. In contrast, activity in basolateral amygdala regions correlated negatively with associability at the time of cue presentation. Thus, whereas the corticomedial amygdala and the midbrain reflected immediate surprise, the basolateral amygdala represented predictiveness and displayed increased responses when outcome predictions became more reliable. These results extend previous findings on PH-like mechanisms in the amygdala and provide unique insights into human amygdala circuits during associative learning.