Conference Proceedings

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.
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.
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.
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.
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.
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.
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.
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.
Walker, C., Williams, J. J., Lombrozo, T., & Gopnik, A. (2012). Explaining influences children’s reliance on evidence and prior knowledge in causal induction. Proceedings of the 34th Annual Conference of the Cognitive Science Society.Abstract
In two studies, we examine how prompting 5- and 6-year-olds to explain observed outcomes influences causal learning. In Study 1, children were presented with data consistent with two causal regularities. Explainers outperformed controls in generalizing the regularity that accounted for more observations. In Study 2, this regularity was pitted against an alternative that accounted for fewer observations but was consistent with prior knowledge. Explainers were less likely than controls to generalize the regularity that accounted for more observations. These findings suggest that explaining drives children to favor causal regularities that they expect to generalize, where current observations and prior knowledge both provide cues.
Williams, J. J., Walker, C., & Lombrozo, T. (2012). Explaining increases belief revision in the face of (many) anomalies. Proceedings of the 34th Annual Conference of the Cognitive Science Society.Abstract
How does explaining novel observations influence the extent to which learners revise beliefs in the face of anomalies — observations inconsistent with their beliefs? On one hand, explaining could recruit prior beliefs and reduce belief revision if learners “explain away” or discount anomalies. On the other hand, explaining could promote belief revision by encouraging learners to modify beliefs to better accommodate anomalies. We explore these possibilities in a statistical judgment task in which participants learned to rank students’ performance across courses by observing sample rankings. We manipulated whether participants were prompted to explain the rankings or to share their thoughts about them during study, and also the proportion of observations that were anomalous with respect to intuitive statistical misconceptions. Explaining promoted greater belief revision when anomalies were common, but had no effect when rare. In contrast, increasing the number of anomalies had no effect on belief revision without prompts to explain.
 
Williams, J. J., Lombrozo, T., & Rehder, B. (2011). Explaining drives the discovery of real and illusory patterns. L. Carlson, C. Hoelscher, & T. F. Shipley (Ed.), Proceedings of the 33rd Annual Conference of the Cognitive Science Society.Abstract
Children’s and adults’ attempts to explain the world around them plays a key role in promoting learning and understanding, but little is known about how and why explaining has this effect. An experiment investigated explaining in the social context of learning to predict and explain individuals’ behavior, examining if explaining observations exerts a selective constraint to seek patterns or regularities underlying the observations, regardless of whether such patterns are harmful or helpful for learning. When there were reliable patterns- such as personality types that predict charitable behavior- explaining promoted learning. But when these patterns were misleading, explaining produced an impairment whereby participants exhibited less accurate learning and prediction of individuals’ behavior. This novel approach of contrasting explanation’s positive and negative effects suggests that explanation’s benefits are not merely due to increased motivation, attention or time, and that explaining may undermine learning in domains where regularities are absent, spurious, or unreliable.
Gwynne, N. Z., & Lombrozo, T. (2010). The cultural transmission of explanations: Evidence that teleological explanations are preferentially remembered. S. Ohlsson & R. Catrambone (Ed.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society.Abstract
Teleological explanations – explanations in terms of functions, purposes, or goals – are pervasive in religion and feature prominently in intuitive theories about the world, such as theory of mind and folk biology. Previous findings suggest that such explanations reflect a deep, explanatory preference. Here we explore the mechanisms underlying the prevalence and persistence of such explanations, following a method developed by Boyer and Ramble (2001) to examine which religious concepts are likely to survive processes of cultural transmission. Specifically, we test the prediction that novel teleological explanations are remembered better than mechanistic explanations, even when effects of an explanation’s quality are taken into account. Two experiments support this prediction for artifact and biological trait explanations, but find the opposite pattern for explanations of non-living natural entities.
Williams, J. J., & Lombrozo, T. (2010). Explanation constrains learning, and prior knowledge constrains explanation. S. Ohlsson & R. Catrambone (Ed.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society.Abstract
A great deal of research has demonstrated that learning is influenced by the learner’s prior background knowledge (e.g. Murphy, 2002; Keil, 1990), but little is known about the processes by which prior knowledge is deployed. We explore the role of explanation in deploying prior knowledge by examining the joint effects of eliciting explanations and providing prior knowledge in a task where each should aid learning. Three hypotheses are considered: that explanation and prior knowledge have independent and additive effects on learning, that their joint effects on learning are subadditive, and that their effects are superadditive. A category learning experiment finds evidence for a superadditive effect: explaining drives the discovery of regularities, while prior knowledge constrains which regularities learners discover. This is consistent with an account of explanation’s effects on learning proposed in Williams & Lombrozo (in press).
Williams, J. J., Lombrozo, T., & Rehder, B. (2010). Why does explaining help learning? Insight from an explanation impairment effect. S. Ohlsson & R. Catrambone (Ed.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society.Abstract
Engaging in explanation, even to oneself, can enhance learning. What underlies this effect? Williams & Lombrozo (in press) propose that explanation exerts subsumptive constraints on processing, driving learners to discover underlying patterns. A category-learning experiment demonstrates that explanation can enhance or impair learning depending on whether these constraints match the structure of the material being learned. Explaining can help learning when reliable patterns are present, but actually impairs learning when patterns are misleading. This explanation impairment effect is predicted by the subsumptive constraints account, but challenges alternative hypotheses according to which explaining helps learning by increasing task engagement through motivation, attention, or processing time. The findings have both theoretical and practical implications for learning and education.
Uttich, K., & Lombrozo, T. (2009). Moral norms inform mental state ascriptions: An alternative explanation for the side-effect effect. N. A. Taatgen & H. van Rijn (Ed.), Proceedings of the 31st Annual Conference of the Cognitive Science Society.Abstract
Theory of mind, the capacity to understand and ascribe mental states, has traditionally been conceptualized as analogous to a scientific theory. However, recent work in philosophy and psychology has documented a “side-effect effect” suggesting that moral evaluations influence mental state ascriptions, and in particular whether a behavior is described as having been performed ‘intentionally.’ This evidence challenges the idea that theory of mind is analogous to scientific psychology in serving the function of predicting and explaining, rather than evaluating, behavior. In three experiments, we demonstrate that moral evaluations do inform ascriptions of intentional action, but that this relationship arises because behavior that conforms to norms (moral or otherwise) is less informative about underlying mental states than is behavior that violates norms. This analysis preserves the traditional understanding of theory of mind as a tool for predicting and explaining behavior, but also suggests the importance of normative considerations in social cognition. to accomplish the function of predicting and explaining behavior.

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