Prediction errors (PEs) are a keystone for computational neuroscience. Their association with midbrain neural firing has been confirmed across species and has inspired the construction of artificial intelligence that can outperform humans. However, there is still much to learn. Here, we leverage the wealth of human PE data acquired in the functional neuroimaging setting in service of a deeper understanding, using meta-analysis. Across 263 PE studies that have focused on reward, punishment, action, cognition, and perception, we found consistent region-PE associations that were posited theoretically or evinced in preclinical studies, but not yet established in humans, including midbrain PE signals during perceptual and Pavlovian tasks. Further, we found evidence for PEs over successor representations in orbitofrontal cortex, and for default mode network PE signals. By combining functional imaging meta-analysis with theory and basic research, we provide new insights into learning in machines, humans, and other animals.