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In an ongoing debate between two visions of statistical reasoning competency, ecological rationality proponents claim that pictorial representations help tap into the frequency coding mechanisms of the mind, whereas nested sets proponents argue that pictorial representations simply help one to appreciate general subset relationships. Advancing this knowledge into applied areas is hampered by this present disagreement. A series of experiments used Bayesian reasoning problems with different pictorial representations (Venn circles, iconic symbols and Venn circles with dots) to better understand influences on performance across these representation types. Results with various static and interactive presentations of pictures all indicate a consistent advantage for iconic representations. These results are more consistent with an ecological rationality view of how these pictorial representations achieve facilitation in statistical task performance and provide more specific guidance for applied uses.

A series of experiments examined the relationship of urbanism to helping. Six types of helping behaviors were studied in a cross-sample of 36 small, medium, and large cities across the United States. The relationship of helping to a series of statistics reflecting the demographic, social, environmental, and economic characteristics of these communities was then examined. The strongest and most consistent predictor of overall helping was population density. There were significant correlations between economic indicators and helping in three situations. Helping in some situations also tended to be negatively related to violent crime rates and to environmental problems.Thomas Wolfe (1940) wrote that city people "have no manners, no courtesy, no consideration for the rights of others, and no humanity." Several studies offer evidence that this urban stereotype is widely shared in the United States. Krupat and Guild (1980), for example, reported that a sample of university students perceived cities as anonymous, impersonal, and unsafe and the "typical urbanite" as untrusting and uninvolved with others. Schneider and Mockus (1974) reported that 79% of a sample of university students believed that help from a stranger was more likely to be received in a small town than in a large city.Many urban theorists have offered similarly unflattering descriptions of the "urban personality." Theorists ranging from Wirth (1938) and Simmel (1950) to Milgram (1970) have described the urban dweller as alienated, unresponsive, and unhelpful. Each hypothesized that the size of a community is negatively related to the likelihood of receiving help from a stranger.This hypothesis has generated a large number of studies in the helping literature. In a review of that research, Steblay (1987) found qualified support for the hypothesis of greater rural helpfulness. In a total of 65 studies, 46 reported greater helpfulness in smaller areas, 9 found greater helpfulness in larger areas, and the remaining 10 reported no significant differences. A subsequent meta-analysis indicated a modest negative relationship between population size and helping. The effect was stronger for studies that defined population size as a context variable (i.e., whether helping occurred in a city or rural area) than for studies that defined population as a subject variable (i.e., the population of the city where the subject was raised).

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-_________________________________________________

The idea that naturally sampled frequencies facilitate performance in statistical reasoning tasks because they are a cognitively privileged representational format has been challenged by findings that similarly structured numbers presented as chances similarly facilitate performance, on the basis of the claim that these are technically single-event probabilities. A crucial opinion, however, is that of the research participants, who possibly interpret chances as de facto frequencies. A series of experiments here indicate that not only is performance improved by clearly presented natural frequencies, rather than chances phrasing, but also that participants who interpreted chances as frequencies, rather than as probabilities, were consistently better at statistical reasoning. This result was found across different variations of information presentation and across different populations.