Numerical predictions are of central interest for both coherence-based approaches to judgment and decisions — the Heuristic and Biases (HB) program in particular — and to correspondence-based approaches — Social Judgment Theory (SJT). In this paper I examine the way these two approaches study numerical predictions by reviewing papers that use Cue Probability Learning (CPL), the central experimental paradigm for studying numerical predictions in the SJT tradition, while attempting to look for heuristics and biases. The theme underlying this review is that both bias-prone heuristics and adaptive heuristics govern subjects’ predictions in CPL. When they have little experience to guide them, subjects fall prey to relying on bias-prone natural heuristics, such as representativeness and anchoring and adjustment, which are the only prediction strategies available to them. But, as they acquire experience with the prediction task, these heuristics are abandoned and replaced by ecologically valid heuristics.
