Individuation, counting, and statistical inference: The role of frequency and whole-object representations in judgment under uncertainty.

Evolutionary approaches to judgment under uncertainty have led to new data showing that untutored subjects reliably produce judgments that conform to many principles of probability theory when (a) they are asked to compute a frequency instead of the probability of a single event and (b) the relevant information is expressed as frequencies. But are the frequency-computation systems implicated in these experiments better at operating over some kinds of input than others? Principles of object perception and principles of adaptive design led us to propose the individuation hypothesis: that these systems are designed to produce well-calibrated statistical inferences when they operate over representations of "whole" objects, events, and locations. In a series of experiments on Bayesian reasoning, we show that human performance can be systematically improved or degraded by varying whether a correct solution requires one to compute hit and false-alarm rates over "natural" units, such as whole objects, as opposed to inseparable aspects, views, and other parsings that violate evolved principles of object construal.