Are simple explanations better? Research has shown that people favor simple explanations (defined as number of unex-plained causes; Lombrozo, 2007; Pacer & Lombrozo, 2017), but new findings suggest that under some conditions, com-plexity is preferred (Johnson et al., in press; Zemla et al., 2017). We explore three features that could affect preferences:causal structure, baserates, and likelihoods. Adults (N=544) read one simple and one complex explanation following oneof three causal structures. Simplicity preferences were strongest for one vs. two causes explaining two independent ef-fects, modest for one vs. two jointly sufficient causes explaining one effect, and reversed (to favor complexity) for one vs.two independently sufficient causes explaining one effect. When baserates and likelihoods were specified and matched,simplicity preferences were attenuated, while complexity preferences were sometimes reversed. These findings suggestthat simplicity preferences are moderated by several factors and point to a more unified account of explanatory reasoning.

Much of human learning throughout the lifespan is achievedthrough seeking and generating explanations. However, verylittle is known about what triggers a learner to seek anexplanation. In two studies, we investigate what makes a givenevent or phenomenon stand in need of explanation. In Study 1,we show that a learner’s judgment of “need for explanation”for a given question predicts that learner’s likelihood ofseeking an answer to this question. In Study 2, we exploreseveral potential predictors of need for explanation. We findthat the need for explanation is greater for questions expectedto have useful answers that require expert understanding, andthat “need for explanation” can be differentiated from generalcuriosity.

Explanations not only increase understanding; they are oftendeeply satisfying. In the present research, we explore how thisphenomenological sense of “explanatory satisfaction” relatesto the functional role of explanation within the process ofinquiry. In two studies, we address the following questions: 1)Does explanatory satisfaction track the epistemic, learning-directed features of explanation? and 2) How doesexplanatory satisfaction relate to both antecedent andsubsequent curiosity? In answering these questions, weuncover novel determinants of explanatory satisfaction andcontribute to the broader literature on explanation and inquiry.

How does actively seeking explanations for one’sobservations affect information search over the course oflearning? Generating explanations could plausibly leadlearners to take advantage of the information they havealready obtained, resulting in less exploration. Alternatively,explaining could lead learners to explore more, especiallyafter encountering evidence that suggests their current beliefsare incorrect. In two experiments using a modified observe orbet task, we investigate these possibilities and find support forthe latter: participants who are prompted to explain theirobservations in the course of learning tend to explore more,especially after encountering evidence that challenges acurrent belief.

Question asking is a powerful means by which humans learn. However, asking a question requires searching through a massive space of possible questions to find a single question that is relevant and informative. How do humans efficiently accomplish this task? Drawing on prior research on other decision problems, we propose that the search for new questions is constrained by those encountered in the past, so that people frequently reuse questions (or parts of questions) rather than generating new questions "from scratch." We find empirical support for this prediction, and we find that this "question reuse" has consequences for the informational value of people's questions. Taken together, this research sheds new light on the mechanisms behind human question asking abilities and, more generally, how we narrow down a large space of possibilities to find a single solution.

Across the lifespan, humans direct their learning towards information they are curious to know. However, it is unclear what elicits curiosity, and whether and how this changes across development. Is curiosity triggered by surprise and uncertainty, as prior research suggests, or by expected learning, which is often confounded with these features? In the present research, we use a Bayesian reinforcement learning model to quantify and disentangle surprise, uncertainty, and expected learning. We use the resulting model-estimated features to predict curiosity ratings from 5- to 9-year-olds and adults in an augmented multi-armed bandit task. Like adults’ curiosity, children’s curiosity was best predicted by expected learning. However, after accounting for expected learning, children (but not adults) were also more curious when uncertainty was higher and surprise lower. This research points to developmental changes in what elicits curiosity and calls for a reexamination of research that confounds these elicitors.

Curiosity motivates exploration and is beneficial for learning,but curiosity is not always experienced when facing theunknown. In the present research, we address this selectivity:what causes curiosity to be experienced under somecircumstances but not others? Using a Bayesian reinforcementlearning model, we disentangle four possible influences oncuriosity that have typically been confounded in previousresearch: surprise, local uncertainty/expected informationgain, global uncertainty, and global expected informationgain. In two experiments, we find that backward-lookinginfluences (concerning beliefs based on prior experience) andforward-looking influences (concerning expectations aboutfuture learning) independently predict reported curiosity, andthat forward-looking influences explain the most variance.These findings begin to disentangle the complexenvironmental features that drive curiosity.

People often rely on knowledgeable teachers to help them learn. Sometimes, this teaching is direct: teachers provide instructions, examples, demonstrations, or feedback. But other times, teaching is more subtle: teachers construct the physical environment in which a learner explores. In the present research, we investigate this more subtle form of teaching in an artificial grid-based learning environment. How do people construct the physical environment to teach, and how does the (pedagogical) design of the physical environment affect people's learning? Study 1 shows that people pursue multiple approaches to pedagogical environment design. Study 2 shows that learners make systematic, often accurate inferences from pedagogically designed environments, even in the absence of exploration. Together, these studies add to our understanding of the myriad ways in which experts communicate their knowledge to novices—a capacity that is part of what makes human intelligence unique.

Question asking is a key tool for learning, especially in childhood. However, formulating good questions is challenging. In any given situation, many questions are possible but only few are informative. In the present work, we investigate two ways 5- to 10-year-olds and adults simplify the challenge of formulating questions: by reusing previous questions, and by remixing components of previous questions to form new questions. Our experimental results suggest that children and adults reuse and remix questions and adaptively modulate reuse depending on how informative a question will be in a particular situation. This work shows that task-relevant experience asking questions provides fodder for future questions, simplifying the challenge of inquiry and enabling effective learning.

Both science and religion offer explanations for everydayevents, but they differ with respect to their tolerance formysteries. In the present research, we investigate laypeople’sperceptions about the extent to which religious and scientificquestions demand an explanation and the extent to which anappeal to mystery can satisfy that demand. In Study 1, wedocument a large domain difference between science andreligion: scientific questions are judged to be more in need ofexplanation and less appropriately answered by appeal tomystery than religious questions. In Study 2, we demonstratethat these differences are not driven by differing levels of beliefin the content of these domains. While the source of thesedomain differences remains unclear, we propose severalhypotheses in the General Discussion.