The Ape Lottery: Chimpanzees Fail To Consider Spatial Information When Drawing Statistical Inferences

Humans and nonhuman great apes share a sense for intuitive statistics, making intuitive probability judgments based on proportional information. This ability is of tremendous importance, in particular for predicting the outcome of events using prior information and for inferring general regularities from limited numbers of observations. Already in infancy, humans functionally integrate intuitive statistics with other cognitive domains, rendering this type of reasoning a powerful tool to make rational decisions in a variety of contexts. Recent research suggests that chimpanzees are capable of one type of such cross-domain integration: The integration of statistical and social information. Here, we investigated whether apes can also integrate physical information into their statistical inferences. We tested 14 sanctuary-living chimpanzees in a new task setup consisting of two “gumball machine”-apparatuses that were filled with different combinations of preferred and non-preferred food items. In four test conditions, subjects decided which of two apparatuses they wanted to operate to receive a random sample, while we varied both the proportional composition of the food items as well as their spatial configuration above and below a barrier. To receive the more favorable sample, apes needed to integrate proportional and spatial information. Chimpanzees succeeded in conditions in which we provided them either with proportional information or spatial information, but they failed to correctly integrate both types of information when they were in conflict. Whether these limitations in chimpanzees' performance reflect true limits of cognitive competence or merely performance limitations due to accessory task demands is still an open question.

Can Consumers Learn Price Dispersion? Evidence for Dispersion Spillover Across Categories

Price knowledge is a key antecedent of many consumer judgments and decisions. This paper examines consumers’ ability to form accurate beliefs about the minimum, the maximum, and the overall variability of prices for multiple product categories. Eight experiments provide evidence for a novel phenomenon we call dispersion spillover: Consumers tend to overestimate price dispersion in a category after encountering another category in which prices are more dispersed (versus equally or less dispersed). Our experiments show that this dispersion spillover is consequential: It influences the likelihood that consumers will search for (and find) better prices and offers, and how much consumers bid in auctions. Finally, we disentangle two cognitive processes that might underlie dispersion spillover. Our results suggest that judgments of dispersion are not only based on specific prices stored in memory, and that dispersion spillover does not simply reflect the inappropriate activation of prices from other categories. Instead, it appears that consumers also form “intuitive statistics” of dispersion: Summary representations that encode the dispersion of prices in the environment, but that are insufficiently category-specific.

Criterion-Based Metrics for Range Restriction Corrections

For decades, it has been suggested organizations utilize mechanical decisions. Unfortunately, organizations continue to rely on holistic judgments. Perhaps part of the reasons organizations continue to rely on judgment when mak- ing decisions is because the reported statistics associated with mechanical judgments (e.g., R2) are not intuitive to stakeholders. For example, it is unclear in terms of turnover whether an R2 change from 0.23 under the old (holistic) system to an R2 of 0.27 under the (mechanical) system is enough of an improvement to justify the cost of changing selection methods. In this paper, we argue that researchers instead report changes in criteria of interest (e.g., absenteeism rate before versus after utilizing a mechanical selection sys- tem). This can be accomplished by utilizing a multiple imputation algorithm that simulates optimal selection decisions. Additionally, we provide both an R-package, as well as a point-and-click Shiny app that allows researchers to easily estimate intuitive statistics (e.g., improvement in turnover, proportion of applicants for which an optimal and holistic system agree).

Long-tailed macaques ( Macaca fascicularis ) can use simple heuristics but fail at drawing statistical inferences from populations to samples

Human infants, apes and capuchin monkeys engage in intuitive statistics: they generate predictions from populations of objects to samples based on proportional information. This suggests that statistical reasoning might depend on some core knowledge that humans share with other primate species. To aid the reconstruction of the evolution of this capacity, we investigated whether intuitive statistical reasoning is also present in a species of Old World monkey. In a series of four experiments, 11 long-tailed macaques were offered different pairs of populations containing varying proportions of preferred versus neutral food items. One population always contained a higher proportion of preferred items than the other. An experimenter simultaneously drew one item out of each population, hid them in her fists and presented them to the monkeys to choose. Although some individuals performed well across most experiments, our results imply that long-tailed macaques as a group did not make statistical inferences from populations of food items to samples but rather relied on heuristics. These findings suggest that there may have been convergent evolution of this ability in New World monkeys and apes (including humans).

Do long-tailed macaques engage in intuitive statistics?

Human infants, apes and capuchins have been found to engage in intuitive statistics, generating predictions from populations to samples based on proportional information. This suggests that statistical reasoning might depend on some core knowledge that is shared with other species. Here, we investigated whether such intuitive statistical reasoning is also present in a species of Old World monkeys, to aid in the reconstruction of the evolution of this capacity. In a series of 7 test conditions, 11 long-tailed macaques were offered different pairs of populations containing varying proportions of preferred vs. neutral food items. One population always contained a higher proportion of preferred items than the other. An experimenter simultaneously drew one item out of each population, hid them in her fists and presented them to the monkey to choose. Results revealed that at least one individual seemed to make systematic population-to-sample inferences and consistently chose the sample from the population with the more favorable distribution of preferred vs. neutral food items. While it is not clear whether she used relative or absolute quantities of food, she seemed to understand the difference between a correct choice and a favorable draw and thus some basic principles of intuitive statistics.