Three experiments investigate how self-generated explanation influences children's causal learning. Five-year-olds (N = 114) observed data consistent with two hypotheses and were prompted to explain or to report each observation. In Study 1, when making novel generalizations, explainers were more likely to favor the hypothesis that accounted for more observations. In Study 2, explainers favored a hypothesis that was consistent with prior knowledge. Study 3 pitted a hypothesis that accounted for more observations against a hypothesis consistent with prior knowledge. Explainers were more likely to base generalizations on prior knowledge. Findings suggest that attempts to explain drive children to evaluate hypotheses using features of "good" explanations, or those supporting generalizations with broad scope, as informed by children's prior knowledge and observations.
Lombrozo, T., & Vasilyeva, N. (2017). Causal explanation. In M. Waldmann (Ed.), Oxford Handbook of Causal Reasoning (pp. 415–432) . Oxford, UK: Oxford University Press.Abstract
Explanation and causation are intimately related. Explanations often appeal to causes, and causal claims are often answers to implicit or explicit questions about why or how something occurred. In this chapter we consider what research on explanation can tell us about causal reasoning. In particular, we review an emerging body of work suggesting that explanatory considerations – such as the simplicity or scope of a causal hypothesis – can systematically influence causal inference and learning. We also discuss proposed distinctions among types of explanations and review their differential effects on causal reasoning and representation. Finally, we consider the relationship between explanations and causal mechanisms and raise important questions for future research.
When learners explain to themselves as they encounter new information, they recruit a suite of processes that influence subsequent learning. One consequence is that learners are more likely to discover exceptionless rules that underlie what they are trying to explain. Here we investigate what it is about exceptionless rules that satisfies the demands of explanation. Are exceptions unwelcome because they lower predictive accuracy, or because they challenge some other explanatory ideal, such as simplicity and breadth? To compare these alternatives, we introduce a causally rich property explanation task in which exceptions to a general rule are either arbitrary or predictable. If predictive accuracy is sufficient to satisfy the demands of explanation, the introduction of a rule plus exception that supports perfect prediction should block the discovery of a more subtle but exceptionless rule. Across two experiments, we find that effects of explanation go beyond attaining perfect prediction.
How does actively seeking explanations for one’s observations affect information search over the course of learning? Generating explanations could plausibly lead learners to take advantage of the information they have already obtained, resulting in less exploration. Alternatively, explaining could lead learners to explore more, especially after encountering evidence that suggests their current beliefs are incorrect. In two experiments using a modified observe or bet task, we investigate these possibilities and find support for the latter: participants who are prompted to explain their observations in the course of learning tend to explore more, especially after encountering evidence that challenges a current belief.
Representations of social categories help us make sense of the social world, supporting predictions and explanations about groups and individuals. Here we explore whether children and adults are able to understanding category-property associations 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 3-4 years of age show signs of structural thinking, but that this capacity does not fully develop until after 7 years of age. These findings introduce a viable alternative to internalist accounts of social categories, such as psychological essentialism.
Most crimes in America require that the defendant have mens rea, Latin for "guilty mind." However, mens rea is not legally required for strict liability crimes, such as speeding, for which someone is guilty even if ignorant or deceived about her speed. In 3 experiments involving participants responding to descriptive vignettes, we investigated whether the division of strict liability crimes in the law reflects an aspect of laypeople's intuitive moral cognition. Experiment 1 (N = 396; 236 male, 159 female, 1 other; M = 30) found evidence that it does: ignorance and deception were less mitigating for strict liability crimes than for "mens rea" crimes. Experiments 2 (N = 413; 257 male, 154 female, 2 other; M = 31) and 3 (N = 404; 183 male, 221 female, M = 35) revealed that strict liability crimes are not treated as pure moral violations, but additionally as violations of convention. We found that for strict liability crimes, ratings of moral wrongness and punishment were influenced to a greater extent by the fact that a rule had been violated, even when harm was kept constant, mirroring the legal distinction of malum prohibitum (wrong as prohibited) versus malum in se (wrong in itself). Further, we found that rules prohibiting strict liability crimes were judged more arbitrary than corresponding rules for "mens rea" crimes, and that this judgment was related to the role of mental states. Jointly, the findings suggest a surprising correspondence between the law and laypeople's intuitive judgments. (PsycINFO Database Record
Explanations play an important role in learning and inference. People often learn by seeking explanations, and they assess the viability of hypotheses by considering how well they explain the data. An emerging body of work reveals that both children and adults have strong and systematic intuitions about what constitutes a good explanation, and that these explanatory preferences have a systematic impact on explanation-based processes. In particular, people favor explanations that are simple and broad, with the consequence that engaging in explanation can shape learning and inference by leading people to seek patterns and favor hypotheses that support broad and simple explanations. Given the prevalence of explanation in everyday cognition, understanding explanation is therefore crucial to understanding learning and inference.
Children are active learners: they learn not only from the information people offer and the evidence they happen to observe, but by actively seeking information. However, children's information search strategies are typically less efficient than those of adults. In two studies, we isolate potential sources of developmental change in how children (7- and 10-year-olds) and adults search for information. To do so, we develop a hierarchical version of the 20-questions game, in which participants either ask questions (Study 1) or test individual objects (Study 2) to discover which category of objects within a nested structure (e.g., animals, birds, or owls) has a novel property. We also develop a computational model of the task, which allows us to evaluate performance in quantitative terms. As expected, we find developmental improvement in the efficiency of information search. In addition, we show that participants' performance exceeds random search, but falls short of optimal performance. We find mixed support for the idea that children's inefficiency stems from difficulty thinking beyond the level of individual objects or hypotheses. Instead, we reveal a previously undocumented source of developmental change: Children are significantly more likely than adults to continue their search for information beyond the point at which a single hypothesis remains, and thus to ask questions and select objects associated with zero information gain. This suggests that one crucial source of developmental change in information search efficiency lies in children's "stopping rules." (PsycINFO Database Record
Four experiments investigate the folk concept of ‘‘understanding,’’ in particular when and why it is deployed differently from the concept of knowledge. We argue for the positions that (1) people have higher demands with respect to explanatory depth when it comes to attributing understanding, and (2) that this is true, in part, because understanding attributions play a functional role in identifying experts who should be heeded with respect to the general field in question. These claims are supported by our findings that people differentially withhold attributions of understanding (rather than knowledge) when the object of attribution has minimal explanatory information. We also show that this tendency significantly correlates with people’s willingness to defer to others as potential experts. This work bears on a pressing issue in epistemology concerning the place and value of understanding. Our results also provide reason against positing a simple equation of knowledge(- why) and understanding(-why). We contend that, because deference plays a crucial role in many aspects of everyday reasoning, the fact that we use understanding attributions to demarcate experts reveals a potential mechanism for achieving our epistemic aims in many domains.
Explanation has been an important topic of study in philosophy of science, in epistemology, and in other areas of philosophy. In parallel, psychologists have been studying children’s and adults’ explanations, including their role in inference and in learning. This entry reviews recent work that begins to bridge the philosophy and psychology of explanation, with sections introducing recent empirical work on explanation by philosophers, formal and functional accounts of explanation, inference to the best explanation, the role of explanation in discovery, and the implications of empirical work on explanation for the “negative program” in experimental philosophy.
Natural phenomena, such as illness or adaptation, can be explained in many ways. Typically, this many-to-one mapping between explanations and the phenomena they explain is construed as a source of tension between scientific and religious explanations (e.g., creationism vs. evolution) or between different forms of scientific explanation (e.g., Lamarck’s vs. Darwin’s theory of evolution). However, recent research suggests that competing explanations exist not only across individuals within the same community, but also within individuals themselves, who maintain competing explanations. Here, we explore this phenomenon of “explanatory coexistence” and analyze its implications for conceptual change, or knowledge restructuring at the level of individual concepts. We argue that conceptual change is often better construed as a process of augmentation, in which early-developing concepts coexist with later-developing concepts because both types of concepts remain useful for predicting and explaining the natural world, albeit in different circumstances or for different purposes.
We report two experiments investigating whether people’s judgments about causal relationships are sensitive to the robustness or stability of such relationships across a wide range of background circumstances. We demonstrate that people prefer stable causal relationships even when overall causal strength is held constant, and show that this effect is unlikely to be driven by a causal generalization’s actual scope of application. This documents a previously unacknowledged factor that shapes people’s causal reasoning.
Three experiments investigate whether and why people accept explanations for symptoms that appeal to mental disorders, such as: “She experiences delusions because she has schizophrenia.” Such explanations are potentially puzzling, as mental disorder diagnoses are made on the basis of symptoms rather than causes. Do laypeople nonetheless conceptualize mental disorder classifications in causal terms? Or is this an instance of non-causal explanation? Experiment 1 shows that such explanations are found explanatory. Experiment 2 presents participants with novel disorders that are stipulated to involve or not involve an underlying cause across symptoms and people. Disorder classifications are found more explanatory when a causal basis is stipulated, or when participants infer that one is present (even after it’s denied in the text). Finally, Experiment 3 finds that merely having a principled, but non-causal, basis for defining symptom clusters is insufficient to reach the explanatory potential of categories with a stipulated common cause.
One way to learn about the world is by asking questions. We investigate how younger children (7- to 8-year-olds), older children (9- to 11-year-olds), and young adults (17- to 18-year-olds) ask questions to identify the cause of an event. We find a developmental shift in children's reliance on hypothesis-scanning questions (which test hypotheses directly) versus constraint-seeking questions (which reduce the space of hypotheses), but also that all age groups ask more constraint-seeking questions when hypothesis-scanning questions are least likely to pay off: When the solution is one among equally likely alternatives (Study 1) or when the problem is difficult (Studies 1 and 2). These findings are the first to demonstrate that even young children dynamically adapt their strategies for inquiry to increase the efficiency of information search.
In pedagogical contexts and in everyday life, we often come to believe something because it would best explain the data. What is it about the explanatory endeavor that makes it essential to everyday learning and to scientific progress? There are at least two plausible answers. On one view, there is something special about having true explanations. This view is highly intuitive: it’s clear why true explanations might improve one’s epistemic position. However, there is another possibility—it could be that the process of seeking, generating, or evaluating explanations itself puts one in a better epistemic position, even when the outcome of the process is not a true explanation. In other words, it could be that accurate explanations are beneficial, or it could be that high-quality explaining is beneficial, where there is something about the activity of looking for an explanation that improves our epistemic standing. The main goal of this paper is to tease apart these two possibilities, both theoretically and empirically, which we align with ‘‘Inference to the Best Explanation’’ (IBE) and ‘‘Explaining for the Best Inference’’ (EBI), respectively. We also provide some initial support for EBI and identify promising directions for future research.
A perpetrator’s mental state – whether she had mens rea or a “guilty mind” – typically plays an important role in evaluating wrongness and assigning punishment. In two experiments, we find that this role for mental states is weaker in evaluating conventional violations relative to moral violations. We also find that this diminished role for mental states may be associated with the fact that conventional violations are wrong by virtue of having violated a (potentially arbitrary) rule, whereas moral violations are also wrong inherently.
Like scientists, children and adults learn by asking questions and making interventions. How does this ability develop? We investigate how children (7- and 10-year-olds) and adults search for information to learn which kinds of objects share a novel causal property. In particular, we consider whether children ask questions and select interventions that are as informative as those of adults, and whether they recognize when to stop searching for information to provide a solution. We find an anticipated developmental improvement in information search efficiency. We also present a formal analysis that allows us to identify the basis for children’s inefficiency. In our 20-questions-style task, children initially ask questions and make interventions no less efficiently than adults do, but continue to search for information past the point at which they have narrowed their hypothesis space to a single option. In other words, the performance change from age seven to adulthood is due largely to a change in implementing a “stopping rule”; when considering only the minimum number of queries participants would have needed to identify the correct hypothesis, age differences disappear.
We report four experiments demonstrating that judgments of explanatory goodness are sensitive both to covariation evidence and to mechanism information. Compared to judgments of causal strength, explanatory judgments tend to be more sensitive to mechanism and less sensitive to covariation. Judgments of understanding tracked covariation least closely. We discuss implications of our findings for theories of explanation, understanding and causal attribution.
Do people evaluate the quality of explanations differently depending on their goals? In particular, are explanations of different kinds (formal, mechanistic, teleological) judged differently depending on the future judgments the evaluator anticipates making? We report two studies demonstrating that the perceived “goodness” of explanations depends on the evaluator’s current goals, with explanations receiving a relative boost when they are based on relationships that support anticipated judgments. These findings shed light on the functions of explanation and support pragmatic and pluralist approaches to explanation.
Two studies examined the specificity of effects of explanation on learning by prompting 3- to 6-year-old children to explain a mechanical toy and comparing what they learned about the toy's causal and non-causal properties with children who only observed the toy, both with and without accompanying verbalization. In Study 1, children were experimentally assigned to either explain or observe the mechanical toy. In Study 2, children were classified according to whether the content of their response to an undirected prompt involved explanation. Dependent measures included whether children understood the toy's functional-mechanical relationships, remembered perceptual features of the toy, effectively reconstructed the toy, and (for Study 2) generalized the function of the toy when constructing a new one. Results demonstrate that across age groups, explanation promotes causal learning and generalization but does not improve (and in younger children can even impair) memory for causally irrelevant perceptual details.