Publications

Previous research suggests that preschoolers struggle with understanding abstract relations andwithreasoning by analogy. Four experiments find, in contrast, that 3-and 4-year-olds (N=168) are surprisingly adept at relational and analogical reasoning within a causal context. In earlier studies preschoolers routinely favoredimagesthat share thematic or perceptual commonalities with a target image(object matches) over choices that match the target along abstract relations (relational matches). The present studies embed suchchoice taskswithin a cause-and-effect framework. Withoutcausal framing, preschoolers strongly favor object matches, replicating the results of previous studies. But withcausal framing, preschoolers succeed at analogical transfer (i.e., choose relational matches). These findings suggest that causal framing facilitates early analogical reasoning.

Mental simulation – such as imagining tilting a glass to figure out the angle at which water would spill – can be a way of coming to know the answer to an internally or externally posed query. Is this form of learning a species of inference or a form of observation? We argue that it is neither: learning through simulation is a genuinely distinct form of learning. On our account, simulation can support learning the answer to a query even when the basis for that answer is opaque to the learner. Moreover, through repeated simulation, the learner can reduce this opacity, supporting self-training and the acquisition of more accurate models of the world. Simulation is thus an essential part of the story of how creatures like us become effective learners and knowers.

People often answer why-questions with what we call experiential explanations: narratives or stories with temporal structure and concrete details. In contrast, on most theories of the epistemic function of explanation, explanations should be abstractive: structured by general relationships and lacking extraneous details. We suggest that abstractive and experiential explanations differ not only in level of abstraction, but also in structure, and that each form of explanation contributes to the epistemic goals of individual learners and of science. In particular, experiential explanations support mental simulation and survive transitions across background theories; as a result, they support learning and help us translate between competing frameworks. Experiential explanations play an irreducible role in human cognition – and perhaps in science.

How do scientific explanations for beliefs affect people’s confidence in those beliefs? For example, do people think neuroscientific explanations for religious belief support or challenge belief in God? In five experiments, we find that the effects of scientific explanations for belief depend on whether the explanations imply normal or abnormal functioning (e.g., if a neural mechanism is doing what it evolved to do). Experiments 1 and 2 find that people think brain- based explanations for religious, moral, and scientific beliefs corroborate those beliefs when the explanations invoke a normally functioning mechanism, but not an abnormally functioning mechanism. Experiment 3 demonstrates comparable effects for other kinds of scientific explanations (e.g., genetic explanations). Experiment 4 confirms that these effects derive from (im)proper functioning, not statistical (in)frequency. Experiment 5 suggests that these effects interact with people’s prior beliefs to produce motivated judgments: People are more skeptical of scientific explanations for their own beliefs if the explanations appeal to abnormal functioning, but they are less skeptical of scientific explanations of opposing beliefs if the explanations appeal to abnormal functioning. These findings suggest that people treat “normality” as a proxy for epistemic reliability and reveal that folk epistemic commitments shape attitudes towards scientific explanations.

The capacity to search for information effectively by asking informative questions is crucial for self-directed learning and develops throughout the preschool years and beyond. We tested the hypothesis that explaining observations in a given domain prepares children to ask more informative questions in that domain, and that it does so by promoting the identification of features that apply to multiple objects, thus supporting more effective questions. Across two experiments, 4- to 7-year-old children (N  = 168) were prompted to explain observed evidence or to complete a control task prior to a 20-questions game. We found that prior prompts to explain led to a decrease in the number of questions needed to complete the game, but only for older children (ages 6-7). Moreover, we found that effects of explanation manifested as a shift away from questions that targeted single objects. These findings shed light on the development of question asking in childhood and on the role of explanation in learning.

Generating explanations can be highly effective in promoting category learning; however, the underlying mechanisms are not fully understood. We propose that engaging in explanation can recruit comparison processes, and that this in turn contributes to the effectiveness of explanation in supporting category learning. Three experiments evaluated the interplay between explanation and various comparison strategies in learning artificial categories. In Experiment 1, as expected, prompting participants to explain items’ category membership led to (a) higher ratings of self-reported comparison processing and (b) increased likelihood of discovering a rule underlying category membership. Indeed, prompts to explain led to more self- reported comparison than did direct prompts to compare pairs of items. Experiment 2 showed that prompts to compare all members of a particular category (“group comparison”) were more effective in supporting rule learning than were pairwise comparison prompts. Experiment 3 found that group comparison (as assessed by self-report) partially mediated the relationship between explanation and category learning. These results suggest that one way in which explanation benefits category learning is by inviting comparisons in the service of identifying broad patterns.

This chapter introduces “learning by thinking” (LbT) as a form of learning distinct from familiar forms of learning through observation. When learning by thinking, the learner gains genuinely new insight in the absence of novel observations “outside the head.” Scientific thought experiments are canonical examples, but the phenomenon is much more widespread, and includes learning by explaining to oneself, through analogical reasoning, or through mental simulation. The chapter argues that episodes of LbT can be re-expressed as explicit arguments or inferences but are neither psychologically nor epistemically reducible to explicit arguments or inferences, and that this partially explains the novelty of the conclusions reached through LbT. It also introduces a new perspective on the epistemic value of LbT processes as practices with potentially beneficial epistemic consequences, even when the commitments they invoke and the conclusions they immediately deliver are not themselves true.

Many natural and artificial entities can be predicted and explained both mechanistically, in term of parts and proximate causal processes, as well as functionally, in terms of functions and goals. Do these distinct “stances” or “modes of construal” support fundamentally different kinds of understanding? Based on recent work in epistemology and philosophy of science, as well as empirical evidence from cognitive and developmental psychology, we argue for what we call the “weak differentiation thesis”: the claim that mechanistic and functional understanding are distinct in that they involve importantly different objects. We also consider more tentative arguments for the “strong differentiation thesis”: the claim that mechanistic and functional understanding involve different epistemic relationships between mind and world.

Generating explanations can be highly effective in promoting category learning; however, the underlying mechanisms are not fully understood. We propose that engaging in explanation can recruit comparison processes, and that this in turn contributes to the effectiveness of explanation in supporting category learning. Three experiments evaluated the interplay between explanation and various comparison strategies in learning artificial categories. In Experiment 1, as expected, prompting participants to explain items’ category membership led to (a) higher ratings of self-reported comparison processing and (b) increased likelihood of discovering a rule underlying category membership. Indeed, prompts to explain led to more self- reported comparison than did direct prompts to compare pairs of items. Experiment 2 showed that prompts to compare all members of a particular category (“group comparison”) were more effective in supporting rule learning than were pairwise comparison prompts. Experiment 3 found that group comparison (as assessed by self-report) partially mediated the relationship between explanation and category learning. These results suggest that one way in which explanation benefits category learning is by inviting comparisons in the service of identifying broad patterns.

https://doi.org/10.1016/j.cognition.2018.12.011

Scientific norms value skepticism; many religious traditions value faith. We test the hypothesis that these different attitudes towards inquiry and belief result in different inferences from epistemic behavior: Whereas the pursuit of evidence or explanations is taken as a signal of commitment to science, forgoing further evidence and explanation is taken as a signal of commitment to religion. Two studies (N = 401) support these predictions. We also find that deciding to pursue inquiry is judged more moral and trustworthy, with moderating effects of participant religiosity and scientism. These findings suggest that epistemic behavior can be a social signal, and shed light on the epistemic and social functions of scientific vs. religious belief.

Representations of social categories help us make sense of the social world, supporting predictions and explanations about groups and individuals. In an experiment with 156 participants, we explore whether children and adults are able to understand category-property associations (such as the association between “girls” and “pink”) in structural terms, locating an object of explanation within a larger structure and identifying structural constraints that act on elements of the structure. We show that children as young as 3-4 years old show signs of structural thinking, and that 5-6 year olds show additional differentiation between structural and nonstructural thinking, yet still fall short of adult performance. These findings introduce structural connections as a new type of non-accidental relationship between a property and a category, and present a viable alternative to internalist accounts of social categories, such as psychological essentialism.

Occam's razor-the idea that all else being equal, we should pick the simpler hypothesis-plays a prominent role in ordinary and scientific inference. But why are simpler hypotheses better? One attractive hypothesis known as Bayesian Occam's razor (BOR) is that more complex hypotheses tend to be more flexible-they can accommodate a wider range of possible data-and that flexibility is automatically penalized by Bayesian inference. In two experiments, we provide evidence that people's intuitive probabilistic and explanatory judgments follow the prescriptions of BOR. In particular, people's judgments are consistent with the two most distinctive characteristics of BOR: They penalize hypotheses as a function not only of their numbers of free parameters but also as a function of the size of the parameter space, and they penalize those hypotheses even when their parameters can be "tuned" to fit the data better than comparatively simpler hypotheses.

Much recent work on explanation in the interventionist tradition emphasizes the explanatory value of stable causal generalizations—i.e., causal generalizations that remain true in a wide range of background circumstances. We argue that two separate explanatory virtues are lumped together under the heading of `stability’. We call these two virtues breadth and guidancerespectively. In our view, these two virtues are importantly distinct, but this fact is neglected or at least under-appreciated in the literature on stability. We argue that an adequate theory of explanatory goodness should recognize breadth and guidance as distinct virtues, as breadth and guidance track different ideals of explanation, satisfy different cognitive and pragmatic ends, and play different theoretical roles in (for example) helping us understand the explanatory value of mechanisms. Thus keeping track of the distinction between these two forms of stability yields a more accurate and perspicuous picture of the role that stability considerations play in explanation.

An actor's mental states-whether she acted knowingly and with bad intentions-typically play an important role in evaluating the extent to which an action is wrong and in determining appropriate levels of punishment. In four experiments, we find that this role for knowledge and intent is significantly weaker when evaluating transgressions of conventional rules as opposed to moral rules. We also find that this attenuated role for knowledge and intent is partly due to the fact that conventional rules are judged to be more arbitrary than moral rules; whereas moral transgressions are associated with actions that are intrinsically wrong (e.g., hitting another person), conventional transgressions are associated with actions that are only contingently wrong (e.g., wearing pajamas to school, which is only wrong if it violates a dress code that could have been otherwise). Finally, we find that it is the perpetrator's belief about the arbitrary or non-arbitrary basis of the rule-not the reality-that drives this differential effect of knowledge and intent across types of transgressions.