Abstract
We can predict and control events in the world via associative learning. Such learning is rational if we come to believe that an associative relationship exists between a pair of events only when it truly does. The statistical metric ΔP, the difference between the probability of an outcome event in the presence of the predictor and its probability in the absence of the predictor tells us when and to what extent events are indeed related. Contrary to what is often claimed, humans' associative judgements compare very favourably with the ΔP metric, even in situations where multiple predictive cues are in competition for association with the outcome. How do humans achieve this judgemental accuracy? I argue that it is not via the application of an explicit mental version of the ΔP rule. Instead, accurate judgements are an emergent property of an associationist learning process of the sort that has become common in adaptive network models of cognition. Such an associationist mechanism is the “means” to a normative or statistical “end”.
