Principles of Economic Rationality in Mice

Two-dimensional reward evaluation in mice

When choosing among multi-attribute options, integrating the full information may be computationally costly and time-consuming. So-called non-compensatory decision rules only rely on partial information, for example when a difference on a single attribute overrides all others. Such rules may be ecologically more advantageous, despite being economically suboptimal. Here, we present a study that investigates to what extent animals rely on integrative rules (using the full information) versus non-compensatory rules when choosing where to forage. Groups of mice were trained to obtain water from dispensers varying along two reward dimensions: volume and probability. The mice’s choices over the course of the experiment suggested an initial reliance on integrative rules, later displaced by a sequential rule, in which volume was evaluated before probability. Our results also demonstrate that while the evaluation of probability differences may depend on the reward volumes, the evaluation of volume differences is seemingly unaffected by the reward probabilities.

A description-experience gap in statistical intuitions: Of smart babies, risk-savvy chimps, intuitive statisticians, and stupid grown-ups

Comparison of different lines of research on statistical intuitions and probabilistic reasoning reveals several puzzling contradictions. Whereas babies seem to be intuitive statisticians, surprisingly capable of statistical learning and inference, adults' statistical inferences have been found to be inconsistent with the rules of probability theory and statistics. Whereas researchers in the 1960s concluded that people's probability updating is "conservatively" proportional to normative predictions, probability updating research in the 1970s suggested that people are incapable of following Bayes's rule. And whereas animals appear to be strikingly risk savvy, humans often seem "irrational" when dealing with probabilistic information. Drawing on research on the description-experience gap in risky choice, we integrate and systematize these findings from disparate fields of inquiry that have, to date, operated largely in parallel. Our synthesis shows that a key factor in understanding inconsistencies in statistical intuitions research is whether probabilistic inferences are based on symbolic, abstract descriptions or on the direct experience of statistical information. We delineate this view from other conceptual accounts, consider potential mechanisms by which attributes of first-hand experience can facilitate appropriate statistical inference, and identify conditions under which they improve or impair probabilistic reasoning. To capture the full scope of human statistical intuition, we conclude, research on probabilistic reasoning across the lifespan, across species, and across research traditions must bear in mind that experience and symbolic description of the world may engage systematically distinct cognitive processes.