Information needs representation. If a representation is recurrent and stable during human evolution, one can expect that mental algorithms are designed to operate on this representation. In this chapter, I apply this argument to the understanding of human inferences under uncertainty. The thesis is that mental algorithms were designed for natural frequencies, which were the recurrent format of information until very recently. I deal with a specific class of inferences that correspond to a simple form of Bayesian inferences, where one of several possible states is inferred from one or a few cues. Here mental computations are simpler when information is encountered in the same form as in the environment in which our ancestors evolved, rather than in the modern form of probabilities or percentages. The evidence from a broad variety of everyday situations and laboratory experiments shows that natural frequencies can make human minds smarter.
Keywords: experts, Bayesian inference, representation of information, clinical inference, law, intelligence, reasoning, teaching
Short Title: Gigerenzer, G. (1997) PsyBeit 1-2:107Prof. Gerd Gigerenzer
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