When solving problems, like making predictions or choices, people often “sample” possibilities into mind. Here, we consider whether there is structure to the kinds of thoughts people sample by default—that is, without an explicit goal. Across three experiments we found that what comes to mind by default are samples from a probability distribution that combines what people think is likely and what they think is good. Experiment 1 found that the first quantities that come to mind for everyday behaviors and (...) events are quantities that combine what is average and ideal. Experiment 2 found, in a manipulated context, that the distribution of numbers that come to mind resemble the mathematical product of the presented statistical distribution and a (softmax-transformed) prescriptive distribution. Experiment 3 replicated these findings in a visual domain. These results provide insight into the process generating people’s conscious thoughts and invite new questions about the value of thinking about things that are both likely and good. (shrink)

How veridical is perception? Rather than representing objects as they actually exist in the world, might perception instead represent objects only in terms of the utility they offer to an observer? Previous work employed evolutionary modeling to show that under certain assumptions, natural selection favors such “strict‐interface” perceptual systems. This view has fueled considerable debate, but we think that discussions so far have failed to consider the implications of two critical aspects of perception. First, while existing models have explored single (...) utility functions, perception will often serve multiple largely independent goals. (Sometimes when looking at a stick you want to know how appropriate it would be as kindling for a campfire, and other times you want to know how appropriate it would be as a weapon for self‐defense.) Second, perception often operates in an inflexible, automatic manner—proving “impenetrable” to shifting higher‐level goals. (When your goal shifts from “burning” to “fighting,” your visual experience does not dramatically transform.) These two points have important implications for the veridicality of perception. In particular, as the need for flexible goals increases, inflexible perceptual systems must become more veridical. We support this position by providing evidence from evolutionary simulations that as the number of independent utility functions increases, the distinction between “interface” and “veridical” perceptual systems dissolves. Although natural selection evaluates perceptual systems only on their fitness, the most fit perceptual systems may nevertheless represent the world as it is. (shrink)

Humans can seamlessly infer other people's preferences, based on what they do. Broadly, two types of accounts have been proposed to explain different aspects of this ability. The first account focuses on spatial information: Agents' efficient navigation in space reveals what they like. The second account focuses on statistical information: Uncommon choices reveal stronger preferences. Together, these two lines of research suggest that we have two distinct capacities for inferring preferences. Here we propose that this is not the case, and (...) that spatial-based and statistical-based preference inferences can be explained by the assumption that agents are efficient alone. We show that people's sensitivity to spatial and statistical information when they infer preferences is best predicted by a computational model of the principle of efficiency, and that this model outperforms dual-system models, even when the latter are fit to participant judgments. Our results suggest that, as adults, a unified understanding of agency under the principle of efficiency underlies our ability to infer preferences. (shrink)

From early in childhood, humans exhibit sophisticated intuitions about how to share knowledge efficiently in simple controlled studies. Yet, untrained adults often fail to teach effectively in real‐world situations. Here, we explored what causes adults to struggle in informal pedagogical exchanges. In Experiment 1, we first showed evidence of this effect, finding that adult participants failed to communicate their knowledge to naïve learners in a simple teaching task, despite reporting high confidence that they taught effectively. Using a computational model of (...) rational teaching, we found that adults assigned to our teaching condition provided highly informative examples but failed to teach effectively because their examples were tailored to learners who were only considering a small set of possible explanations. In Experiment 2, we then found experimental evidence for this possibility, showing that knowledgeable participants systematically misunderstand the beliefs of naïve participants. Specifically, knowledgeable participants assumed naïve agents would primarily consider hypotheses close to the correct one. Finally, in Experiment 3, we aligned learners’ beliefs to knowledgeable agents’ expectations and showed learners the same examples selected by participants assigned to teach in Experiment 1. We found that these same examples were significantly more informative once learners’ hypothesis spaces were constrained to match teachers’ expectations. Our findings show that, in informal settings, adult pedagogical failures result from an inaccurate representation of what naïve learners believe is plausible and not an inability to select informative data in a rational way. (shrink)