While a hindrance to statistical and computational models of inference, missing knowledge can be exploited by organisms in their natural environments. The recognition heuristic utilizes missing knowledge to make accurate inferences about the real world. A consequence of applying this heuristic is a counterintuitive less-is-more effect where less knowledge is better than more for inferential accuracy. Theoretical arguments and experimental evidence supporting the less-is-more effect are given.
