Publications

2014
Lombrozo, T., & Gwynne, N. Z. (2014). Explanation and inference: mechanistic and functional explanations guide property generalization. Frontiers in Human Neuroscience , 8 700. https://doi.org/10.3389/fnhum.2014.00700Abstract
The ability to generalize from the known to the unknown is central to learning and inference. Two experiments explore the relationship between how a property is explained and how that property is generalized to novel species and artifacts. The experiments contrast the consequences of explaining a property mechanistically, by appeal to parts and processes, with the consequences of explaining the property functionally, by appeal to functions and goals. The findings suggest that properties that are explained functionally are more likely to be generalized on the basis of shared functions, with a weaker relationship between mechanistic explanations and generalization on the basis of shared parts and processes. The influence of explanation type on generalization holds even though all participants are provided with the same mechanistic and functional information, and whether an explanation type is freely generated (Experiment 1), experimentally provided (Experiment 2), or experimentally induced (Experiment 2). The experiments also demonstrate that explanations and generalizations of a particular type (mechanistic or functional) can be experimentally induced by providing sample explanations of that type, with a comparable effect when the sample explanations come from the same domain or from a different domains. These results suggest that explanations serve as a guide to generalization, and contribute to a growing body of work supporting the value of distinguishing mechanistic and functional explanations.
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Walker, C. M., Lombrozo, T., Legare, C. H., & Gopnik, A. (2014). Explaining prompts children to privilege inductively rich properties. Cognition , 133 (2), 343-57. https://doi.org/10.1016/j.cognition.2014.07.008Abstract
Four experiments with preschool-aged children test the hypothesis that engaging in explanation promotes inductive reasoning on the basis of shared causal properties as opposed to salient (but superficial) perceptual properties. In Experiments 1a and 1b, 3- to 5-year-old children prompted to explain during a causal learning task were more likely to override a tendency to generalize according to perceptual similarity and instead extend an internal feature to an object that shared a causal property. Experiment 2 replicated this effect of explanation in a case of label extension (i.e., categorization). Experiment 3 demonstrated that explanation improves memory for clusters of causally relevant (non-perceptual) features, but impairs memory for superficial (perceptual) features, providing evidence that effects of explanation are selective in scope and apply to memory as well as inference. In sum, our data support the proposal that engaging in explanation influences children's reasoning by privileging inductively rich, causal properties.
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Uttich, K., Tsai, G., & Lombrozo, T. (2014). Exploring meta-ethical commitments: Moral objectivity and moral progress. In H. Sarkissian & J. C. Wright (Ed.), Advances in experimental moral psychology (pp. 188–208) . London: Bloomsbury Publishing. PDF
Lombrozo, T., Knobe, J., & Nichols, S. (Ed.). (2014). Oxford studies in experimental philosophy (Vol. 1) . Oxford, UK: Oxford University Press.
Edwards, B. J., Williams, J. J., Gentner, D., & Lombrozo, T. (2014). Effects of comparison and explanation on analogical transfer. P. Bello, M. Guarini, M. McShane, & B. Scassellati (Ed.), Proceedings of the 36th Annual Conference of the Cognitive Science Society.Abstract
Although comparison and explanation have typically been studied independently, recent work suggests connections between these processes. Three experiments investigated effects of comparison and explanation on analogical problem solving. In Experiment 1, explaining the solutions to two analogous stories increased spontaneous transfer to an analogical problem. In Experiment 2, explaining a single story promoted analogical transfer, but only after receiving a hint that may have facilitated comparison. In Experiment 3, irrelevant stories were interspersed among the two story analogs to block unprompted comparison; prompts to compare were effective, but prompts to explain were not. This pattern suggests that effects of explanation on analogical transfer may be greatest when combined with comparison.
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Plunkett, D., Lombrozo, T., & Buchak, L. (2014). Because the brain agrees: The impact of neuroscientific explanations for belief. P. Bello, M. Guarini, M. McShane, & B. Scassellati (Ed.), Proceedings of the 36th Annual Conference of the Cognitive Science Society.Abstract
Three experiments investigate whether neuroscientific explanations for belief in some proposition (e.g., that God exists) are judged to reinforce, undermine, or have no effect on confidence that the corresponding proposition is true. Participants learned that an individual’s religious, moral, or scientific belief activated a (fictional) brain region and indicated how this information would and should influence the individual’s confidence. When the region was associated with true or false beliefs (Experiment 1), the predicted and endorsed responses were an increase or decrease in confidence, respectively. However, we found that epistemically-neutral but “normal” neural function was taken to reinforce belief, and “abnormal” function to have no effect or to undermine it, whether the (ab)normality was explicitly stated (Experiment 2) or implied (Experiment 3), suggesting that proper functioning is treated as a proxy for epistemic reliability. These findings have implications for science communication, philosophy, and our understanding of belief revision and folk epistemology.
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Ruggeri, A., & Lombrozo, T. (2014). Learning by asking: How children ask questions to achieve efficient search. P. Bello, M. Guarini, M. McShane, & B. Scassellati (Ed.), Proceedings of the 36th Annual Conference of the Cognitive Science Society.Abstract
One way to learn about the world is by asking questions. We investigate how children (n= 287, 7- to 11-year olds) and young adults (n=160 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 unlikely to pay off: when the problem is difficult (Studies 1 and 2) or the solution is one among equally likely alternatives (Study 2). These findings are the first to demonstrate that even young children adapt their strategies for inquiry to increase the efficiency of information search.
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Williams, J. J., Kovacs, G., Walker, C., Maldonado, S. G., & Lombrozo, T. (2014). Learning online via prompts to explain. 32nd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems.Abstract
Prompting learners to explain their beliefs can help them correct misconceptions upon encountering anomalies — facts and observations that conflict with learners’ current understanding. We have developed a way to augment online interfaces for learning by adding prompts for users to explain a fact or observation. We conducted two experiments testing the effects of these explanation prompts, finding that they increase learners’ self-correction of misconceptions, though these benefits of explaining depend on: (1) How many anomalies the prompts require people to explain, and (2) Whether anomalies are distributed so that individual observations guide learners to correct ideas by conflicting with multiple misconceptions at once.
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2013
Harvey, A. G., Soehner, A., Lombrozo, T., Bélanger, L., Rifkin, J., & Morin, C. M. (2013). 'Folk theories' about the causes of insomnia. Cognitive Therapy and Research , 37 (5). https://doi.org/10.1007/s10608-013-9543-2Abstract
The present study investigates 'folk theories' about the causes of insomnia. Participants with insomnia ( = 69) completed a qualitative and quantitative assessment of their folk theories. The qualitative assessment was to speak aloud for 1 minute in response to: 'What do you think causes your insomnia?'. The quantitative assessment involved completing the 'Causal Attributions of My Insomnia Questionnaire' (CAM-I), developed for this study. The three most common folk theories for both the causes of one's own insomnia as well as insomnia in others were 'emotions', 'thinking patterns' and 'sleep-related emotions'. Interventions targeting these factors were also perceived as most likely to be viable treatments. Seventy-five percent of the folk theories of insomnia investigated with the CAM-I were rated as more likely to be alleviated by a psychological versus a biological treatment. The results are consistent with research highlighting that folk theories are generally coherent and inform a range of judgments. Future research should focus on congruence of 'folk theories' between treatment providers and patients, as well as the role of folk theories in treatment choice, engagement, compliance and outcome.
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Williams, J. J., Lombrozo, T., & Rehder, B. (2013). The hazards of explanation: overgeneralization in the face of exceptions. Journal of Experimental Psychology: General , 142 (4), 1006-14. 10.1037/a0030996Abstract
Seeking explanations is central to science, education, and everyday thinking, and prompting learners to explain is often beneficial. Nonetheless, in 2 category learning experiments across artifact and social domains, we demonstrate that the very properties of explanation that support learning can impair learning by fostering overgeneralizations. We find that explaining encourages learners to seek broad patterns, hindering learning when patterns involve exceptions. By revealing how effects of explanation depend on the structure of what is being learned, these experiments simultaneously demonstrate the hazards of explaining and provide evidence for why explaining is so often beneficial. For better or for worse, explaining recruits the remarkable human capacity to seek underlying patterns that go beyond individual observations.
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Lombrozo, T. (2013). Review: Evolution challenges – Integrating research and practice in teaching and learning about evolution. Reports of the National Center for Science Education , 33 (5). PDF
Edwards, B. J., Williams, J. J., & Lombrozo, T. (2013). Effects of explanation and comparison on category learning. M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Ed.), Proceedings of the 35th Annual Conference of the Cognitive Science Society.Abstract
Generating explanations and making comparisons have both been shown to improve learning. While each process has been studied individually, the relationship between explanation and comparison is not well understood. Three experiments evaluated the effectiveness of explanation and comparison prompts in learning novel categories. In Experiment 1, participants explained items’ category membership, performed pairwise comparisons between items (listed similarities and differences), did both, or did a control task. The explanation task increased the discovery of rules underlying category membership; however, the comparison task decreased rule discovery. Experiments 2 and 3 showed that (1) comparing all four category exemplars was more effective than either within-category or between-category pairwise comparisons, and that (2) “explain” participants reported higher levels of both spontaneous explanation and comparison than “compare” participants. This work provides insights into when explanation and comparison are most effective, and how these processes can work together to maximize learning.
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Pacer, M., Williams, J., Xi, C., Lombrozo, T., & Griffiths, T. L. (2013). Evaluating computational models of explanation using human judgments. Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence.Abstract
We evaluate four computational models of explanation in Bayesian networks by comparing model predictions to human judgments. In two experiments, we present human participants with causal structures for which the models make divergent predictions and either solicit the best explanation for an observed event (Experiment 1) or have participants rate provided explanations for an observed event (Experiment 2). Across two versions of two causal structures and across both experiments we find that the Causal Explanation Tree and Most Relevant Explanation models provide better fits to human data than either Most Probable Explanation or Explanation Tree models. We identify strengths and shortcomings of these models and what they can reveal about human explanation. We conclude by suggesting the value of pursuing computational and psychological investigations of explanation in parallel.
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Walker, C. M., Lombrozo, T., Legare, C. H., & Gopnik, A. (2013). Explaining to others prompts children to favor inductively rich properties. M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Ed.), Proceedings of the 35th Annual Conference of the Cognitive Science Society.Abstract
Three experiments test the hypothesis that engaging in explanation prompts children to favor inductively rich properties when generalizing to novel cases. In Experiment 1, preschoolers prompted to explain during a causal learning task were more likely to override a tendency to generalize according to perceptual similarity and instead extend an internal feature to an object that shared a causal property. In Experiment 2, we replicated this effect of explanation in a case of label extension. Experiment 3 demonstrated that explanation improves memory for internal features and labels, but impairs memory for superficial features. We conclude that explaining can influence learning by prompting children to favor inductively rich properties over surface similarity.
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Williams, J. J., & Lombrozo, T. (2013). Explanation and prior knowledge interact to guide learning. Cognitive Psychology , 66 (1), 55-84. https://doi.org/10.1016/j.cogpsych.2012.09.002Abstract
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.
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Williams, J. J., Walker, C., Maldonado, S. G., & Lombrozo, T. (2013). Effects of explaining anomalies on the generation and evaluation of hypotheses. M. Knauff, M. Pauen, N. Sebanz, & I. Wachsmuth (Ed.), Proceedings of the 35th Annual Conference of the Cognitive Science Society.Abstract
Generating explanations and making comparisons have both been shown to improve learning. While each process has been studied individually, the relationship between explanation and comparison is not well understood. Three experiments evaluated the effectiveness of explanation and comparison prompts in learning novel categories. In Experiment 1, participants explained items’ category membership, performed pairwise comparisons between items (listed similarities and differences), did both, or did a control task. The explanation task increased the discovery of rules underlying category membership; however, the comparison task decreased rule discovery. Experiments 2 and 3 showed that (1) comparing all four category exemplars was more effective than either within-category or between-category pairwise comparisons, and that (2) “explain” participants reported higher levels of both spontaneous explanation and comparison than “compare” participants. This work provides insights into when explanation and comparison are most effective, and how these processes can work together to maximize learning.
2012
Bonawitz, E. B., & Lombrozo, T. (2012). Occam's rattle: children's use of simplicity and probability to constrain inference. Developmental Psychology , 48 (4), 1156-64. https://doi.org/10.1037/a0026471Abstract
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 Occam's razor as an inductive constraint and that this constraint is employed when more reliable bases for inference are unavailable.
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Genone, J., & Lombrozo, T. (2012). Concept possession, experimental semantics, and hybrid theories of reference. Philosophical Psychology , 25 (5), 717–742. https://doi.org/10.1080/09515089.2011.627538Abstract

Contemporary debates about the nature of semantic reference have tended to focus on two competing approaches: theories which emphasize the importance of descriptive information associated with a referring term, and those which emphasize causal facts about the conditions under which the use of the term originated and was passed on. Recent empirical work by Machery and colleagues suggests that both causal and descriptive information can play a role in judgments about the reference of proper names, with findings of cross-cultural variation in judgments that imply differences between individuals with respect to whether they favor causal or descriptive information in making reference judgments. We extend this theoretical and empirical line of inquiry to views of the reference of natural and nominal kind concepts, which face similar challenges to those concerning the reference of proper names. In two experiments, we find evidence that both descriptive and causal factors contribute to judgments of concept reference, with no reliable differences between natural and nominal kinds. Moreover, we find evidence that the same individuals’ judgments can rely on both descriptive and causal information, such that variation between individuals cannot be explained by appeal to a mixed population of “pure descriptive theorists” and “pure causal theorists.” These findings suggest that the contrast between descriptive and causal theories of reference may be inappropriate; intuitions may instead support a hybrid theory of reference that includes both causal and descriptive factors. We propose that future research should focus on the relationship between these factors, and describe several possible frameworks for pursuing these issues. Our findings have implications for theories of semantic reference, as well as for theories of conceptual structure.

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Lombrozo, T., & Rehder, B. (2012). Functions in biological kind classification. Cognitive Psychology , 65 (4), 457-485. https://doi.org/10.1016/j.cogpsych.2012.06.002Abstract
Biological traits that serve functions, such as a zebra's coloration (for camouflage) or a kangaroo's tail (for balance), seem to have a special role in conceptual representations for biological kinds. In five experiments, we investigate whether and why functional features are privileged in biological kind classification. Experiment 1 experimentally manipulates whether a feature serves a function and finds that functional features are judged more diagnostic of category membership as well as more likely to have a deep evolutionary history, be frequent in the current population, and persist in future populations. Experiments 2-5 reveal that these inferences about history, frequency, and persistence account for nearly all the effect of function on classification. We conclude that functional features are privileged because their relationship with the kind is viewed as stable over time and thus as especially well suited for establishing category membership, with implications for theories of classification and folk biological understanding.
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Lombrozo, T. (2012). Explanation and abductive inference. In K. J. Holyoak & R. G. Morrison (Ed.), Oxford handbook of thinking and reasoning (pp. 260–276). https://doi.org/10.1093/oxfordhb/9780199734689.013.0014Abstract
Everyday cognition reveals a sophisticated capacity to seek, generate, and evaluate explanations for the social and physical worlds around us. Why are we so driven to explain, and what accounts for our systematic explanatory preferences? This chapter reviews evidence from cognitive psychology and cognitive development concerning the structure and function of explanations, with a focus on the role of explanations in learning and inference. The findings highlight the value of understanding explanation and abductive inference both as phenomena in their own right and for the insights they provide concerning foundational aspects of human cognition, such as representation, learning, and inference.
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