Tania Lombrozo

The structure and function of explanations

Generating and evaluating explanations is spontaneous, ubiquitous and fundamental to our sense of understanding. Recent evidence suggests that in the course of an individuals reasoning, engaging in explanation can have profound effects on the probability assigned to causal claims, on how properties are generalized and on learning. These effects follow from two properties of the structure of explanations: explanations accommodate novel information in the context of prior beliefs, and do so in a way that fosters generalization. The study of explanation thus promises to shed light on core cognitive issues, such as learning, induction and conceptual representation. Moreover, the influence of explanation on learning and inference presents a challenge to theories that neglect the roles of prior knowledge and explanation-based reasoning.

Simplicity and probability in causal explanation

What makes some explanations better than others? This paper explores the roles of simplicity and probability in evaluating competing causal explanations. Four experiments investigate the hypothesis that simpler explanations are judged both better and more likely to be true. In all experiments, simplicity is quantified as the number of causes invoked in an explanation, with fewer causes corresponding to a simpler explanation. Experiment 1 confirms that all else being equal, both simpler and more probable explanations are preferred. Experiments 2 and 3 examine how explanations are evaluated when simplicity and probability compete. The data suggest that simpler explanations are assigned a higher prior probability, with the consequence that disproportionate probabilistic evidence is required before a complex explanation will be favored over a simpler alternative. Moreover, committing to a simple but unlikely explanation can lead to systematic overestimation of the prevalence of the cause invoked in the simple explanation. Finally, Experiment 4 finds that the preference for simpler explanations can be overcome when probability information unambiguously supports a complex explanation over a simpler alternative. Collectively, these findings suggest that simplicity is used as a basis for evaluating explanations and for assigning prior probabilities when unambiguous probability information is absent. More broadly, evaluating explanations may operate as a mechanism for generating estimates of subjective probability.

Functional explanation and the function of explanation

Teleological explanations (TEs) account for the existence or properties of an entity in terms of a function: we have hearts because they pump blood, and telephones for communication. While many teleological explanations seem appropriate, others are clearly not warranted--for example, that rain exists for plants to grow. Five experiments explore the theoretical commitments that underlie teleological explanations. With the analysis of [Wright, L. (1976). Teleological Explanations. Berkeley, CA: University of California Press] from philosophy as a point of departure, we examine in Experiment 1 whether teleological explanations are interpreted causally, and confirm that TEs are only accepted when the function invoked in the explanation played a causal role in bringing about what is being explained. However, we also find that playing a causal role is not sufficient for all participants to accept TEs. Experiment 2 shows that this is not because participants fail to appreciate the causal structure of the scenarios used as stimuli. In Experiments 3-5 we show that the additional requirement for TE acceptance is that the process by which the function played a causal role must be general in the sense of conforming to a predictable pattern. These findings motivate a proposal, Explanation for Export, which suggests that a psychological function of explanation is to highlight information likely to subserve future prediction and intervention. We relate our proposal to normative accounts of explanation from philosophy of science, as well as to claims from psychology and artificial intelligence.

The Importance of Understanding the Nature of Science for Accepting Evolution

Many students reject evolutionary theory, whether or not they adequately understand basic evolutionary concepts. We explore the hypothesis that accepting evolution is related to understanding the nature of science. In particular, students may be more likely to accept evolution if they understand that a scientific theory is provisional but reliable, that scientists employ diverse methods for testing scientific claims, and that relating data to theory can require inference and interpretation. In a study with university undergraduates, we find that accepting evolution is significantly correlated with understanding the nature of science, even when controlling for the effects of general interest in science and past science education. These results highlight the importance of understanding the nature of science for accepting evolution. We conclude with a discussion of key characteristics of science that challenge a simple portrayal of the scientific method and that we believe should be emphasized in classrooms.

The Role of Explanation in Discovery and Generalization: Evidence From Category Learning

Research in education and cognitive development suggests that explaining plays a key role in learning and generalization: When learners provide explanations-even to themselves-they learn more effectively and generalize more readily to novel situations. This paper proposes and tests a subsumptive constraints account of this effect. Motivated by philosophical theories of explanation, this account predicts that explaining guides learners to interpret what they are learning in terms of unifying patterns or regularities, which promotes the discovery of broad generalizations. Three experiments provide evidence for the subsumptive constraints account: prompting participants to explain while learning artificial categories promotes the induction of a broad generalization underlying category membership, relative to describing items (Exp. 1), thinking aloud (Exp. 2), or free study (Exp. 3). Although explaining facilitates discovery, Experiment 1 finds that description is more beneficial for learning item details. Experiment 2 additionally suggests that explaining anomalous observations may play a special role in belief revision. The findings provide insight into explanations role in discovery and generalization.

Inferring Design Evidence of a Preference for Teleological Explanations in Patients With Alzheimer's Disease

Unlike educated adults, young children demonstrate a “promiscuous” tendency to explain objects and phenomena by reference to functions, endorsing what are called teleological explanations. This tendency becomes more selective as children acquire increasingly coherent beliefs about causal mechanisms, but it is unknown whether a widespread preference for teleology is ever truly outgrown. The study reported here investigated this question by examining explanatory judgments in patients with Alzheimers disease (AD), whose dementia affects the rich causal beliefs adults typically consult in evaluating explanations. The results indicate that unlike healthy adults, AD patients systematically and promiscuously prefer teleological explanations, suggesting that an underlying tendency to construe the world in terms of functions persists throughout life. This finding has broad relevance not only to understanding conceptual impairments in AD, but also to theories of development, learning, and conceptual change. Moreover, this finding sheds light on the intuitive appeal of creationism.

The role of moral commitments in moral judgment.

Traditional approaches to moral psychology assumed that moral judgments resulted from the application of explicit commitments, such as those embodied in consequentialist or deontological philosophies. In contrast, recent work suggests that moral judgments often result from unconscious or emotional processes, with explicit commitments generated post hoc. This paper explores the intermediate position that moral commitments mediate moral judgments, but not through their explicit and consistent application in the course of judgment. An experiment with 336 participants finds that individuals vary in the extent to which their moral commitments are consequentialist or deontological, and that this variation is systematically but imperfectly related to the moral judgments elicited by trolley car problems. Consequentialist participants find action in trolley car scenarios more permissible than do deontologists, and only consequentialists moderate their judgments when scenarios that typically elicit different intuitions are presented side by side. The findings emphasize the need for a theory of moral reasoning that can accommodate both the associations and dissociations between moral commitments and moral judgments.

Occam's Rattle: Children's Use of Simplicity and Probability to Constrain Inference.

A growing literature suggests that generating and evaluating explanations is a key mechanism for learning and inference, but little is known about how children generate and select competing explanations. This study investigates whether young children prefer explanations that are simple, where simplicity is quantified as the number of causes invoked in an explanation, and how this preference is reconciled with probability information. Both preschool-aged children and adults were asked to explain an event that could be generated by 1 or 2 causes, where the probabilities of the causes varied across conditions. In 2 experiments, it was found that children preferred explanations involving 1 cause over 2 but were also sensitive to the probability of competing explanations. Adults, in contrast, responded on the basis of probability alone. These data suggest that children employ a principle of parsimony like Occams razor as an inductive constraint and that this constraint is employed when more reliable bases for inference are unavailable.

Explanation and Prior Knowledge Interact to Guide Learning.

How do explaining and prior knowledge contribute to learning? Four experiments explored the relationship between explanation and prior knowledge in category learning. The experiments independently manipulated whether participants were prompted to explain the category membership of study observations and whether category labels were informative in allowing participants to relate prior knowledge to patterns underlying category membership. The experiments revealed a superadditive interaction between explanation and informative labels, with explainers who received informative labels most likely to discover (Experiments 1 and 2) and generalize (Experiments 3 and 4) a pattern consistent with prior knowledge. However, explainers were no more likely than controls to discover multiple patterns (Experiments 1 and 2), indicating that effects of explanation are relatively targeted. We suggest that explanation recruits prior knowledge to assess whether candidate patterns are likely to have broad scope (i.e., to generalize within and beyond study observations). This interpretation is supported by the finding that effects of explanation on prior knowledge were attenuated when learners believed prior knowledge was irrelevant to generalizing category membership (Experiment 4). This research provides evidence that explanation can serve as a mechanism for deploying prior knowledge to assess the scope of observed patterns.

Selective effects of explanation on learning during early childhood

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 toys 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 toys 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.