Recognition-based inference: When is less more in the real world?

Common wisdom tells us that more information can only help and never hurt. Goldstein and Gigerenzer (2002) highlighted an instance violating this intuition. Specifically, in an analysis of their recognition heuristic, they found a counterintuitive less-is-more effect in inference: An individual recognizing fewer objects than another individual can, nevertheless, make more accurate inferences. Goldstein and Gigerenzer emphasized that a sufficient condition for this effect is that the recognition validity be higher than the knowledge validity, assuming that the validities are uncorrelated with the number of recognized objects, n. But how is the occurrence of the less-is-more effect affected when this independence assumption is violated? I show that validity dependencies (i.e., correlations of the validities with n) abound in empirical data sets, and I demonstrate by computer simulations that these dependencies often have a strong limiting effect on the less-is-more effect. Moreover, I discuss what cognitive (e.g., memory) and ecological (e.g., distribution of the criterion variable, environmental frequencies) factors can give rise to a dependency of the recognition validity on the number of recognized objects.