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 subject reliably produce judgments that conform to may 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 frequencycomputation 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 wellcalibrated 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. The ability to make well-calibrated probability judgments depends, at a very basic level, on the ability to count. The ability to count depends on the ability to individuate the world: to see it as composed of discrete entities. Research on how people individuate the world is, therefore, relevant to understanding the statistical inference mechanisms that govern how people make judgments under uncertainty. Computational machinery whose architecture is designed to parse the world and make inferences about it is under intensive study in many branches of psychology: perception, psychophysics, cognitive development, cognitive neurosci