Learning what others know, especially experts, is a crucial shortcut to understanding the world. Other people's actions and utterances are thus a powerful source of evidence. However, people do not simply copy others' choices or stated beliefs; rather, they infer what others believe and integrate these beliefs with their own. In this paper, we present a computational account of the inference and integration process that underpins learning from a combination of social and direct evidence. This account formalizes the learner's intuitive understanding of psychology-or theory of mind (ToM)-including attributes such as confidence, reliability, and knowledgeability. It then shows how ToM is the lens used to interpret another person's choices, weighing them against the learner's own direct evidence. To test this account, we develop an experimental paradigm that allows for graded manipulation of social and direct evidence, and for quantitative measurement of the learner's resulting beliefs. Four experiments test the predictions of the model, manipulating knowledgeability, confidence, and reliability of the social source. Learners' behavior is consistent with our quantitative and qualitative model predictions across all 4 experiments, demonstrating subtle interactions between evidence and the attributes of those learned from.

How can the large, systematic differences that exist between individuals' color preferences be explained? The ecological valence theory (Palmer & Schloss, Proceedings of the National Academy of Sciences 107:8877-8882, 2010) posits that an individual's preference for each particular color is determined largely by his or her preferences for all correspondingly colored objects. Therefore, individuals should differ in their color preferences to the extent that they have different preferences for the same color-associated objects or that they experience different objects. Supporting this prediction, we found that individuals' color preferences were predicted better by their own preferences for correspondingly colored objects than by other peoples' preferences for the same objects. Moreover, the fit between color preferences and affect toward the colored objects was reliably improved when people's own idiosyncratic color-object associations were included in addition to a standard set of color-object associations. These and related results provide evidence that individual differences in color preferences are reliably influenced by people's personal experiences with colored objects in their environment.

Teaching is a powerful way to transmit knowledge, but with this power comes a hazard: When teachers fail to select the best set of evidence for the learner, learners can be misled to draw inaccurate inferences. Evaluating others' failures as teachers, however, is a nontrivial problem; people may fail to be informative for different reasons, and not all failures are equally blameworthy. How do learners evaluate the quality of teachers, and what factors influence such evaluations? Here, we present a Bayesian model of teacher evaluation that considers the utility of a teacher's pedagogical sampling given their prior knowledge. In Experiment 1 (N = 1168), we test the model predictions against adults' evaluations of a teacher who demonstrated all or a subset of the functions on a novel device. Consistent with the model predictions, participants' ratings integrated information about the number of functions taught, their values, as well as how much the teacher knew. Using a modified paradigm for children, Experiments 2 (N = 48) and 3 (N = 40) found that preschool-aged children (2a, 3) and adults (2b) make nuanced judgments of teacher quality that are well predicted by the model. However, after an unsuccessful attempt to replicate the results with preschoolers (Experiment 4, N = 24), in Experiment 5 (N = 24) we further investigate the development of teacher evaluation in a sample of seven-and eight-year-olds. These older children successfully distinguished teachers based on the amount and value of what was demonstrated, and their ability to evaluate omissions relative to the teacher's knowledge state was related to their tendency to spontaneously reference the teacher's knowledge when explaining their evaluations. In sum, our work illustrates how the human ability to learn from others supports not just learning about the world but also learning about the teachers themselves. By reasoning about others' informativeness, learners can evaluate others' teaching and make better learning decisions.

In standard decision theory, rational agents are objective, keeping their beliefs independent from their desires. Such agents are the basis for current computational models of Theory of Mind (ToM), but the accuracy of these models are unknown. Do people really think that others do not let their desires color their beliefs? In two experiments we test whether people think that others engage in wishful thinking. We find that participants do think others believe that desirable events are more likely to happen, and that undesirable ones are less likely to happen. However, these beliefs are not well calibrated as people do not let their desires influence their beliefs in the task. Whether accurate or not, thinking that others wishfully think has consequences for reasoning about them. We find one such consequence—people learn more from an informant who thinks an event will happen despite wishing it was otherwise. People’s ToM therefore appears to be more nuanced than the current rational accounts in that it allows other’s desires to directly affect their subjective probability of an event.