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 c onform to many principles of probability theory when (a) they are aske d 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 bett er at operating over some kinds of input than others? Principles of ob ject perception and principles of adaptive design led us to propose th e individuation hypothesis: that these systems are designed to produce well-calibrated statistical inferences when they operate over represe ntations of ''whole'' objects, events, and locations. In a series of e xperiments on Bayesian reasoning, we show that human performance can b e systematically improved or degraded by varying whether a correct sol ution requires one to compute hit and false-alarm rates over ''natural '' units, such as whole objects, as opposed to inseparable aspects, vi ews, and other parsings that violate evolved principles of object cons trual.