Explaining increases belief revision in the face of (many) anomalies

Explaining increases belief revision in the face of (many) anomalies Joseph Jay Williams (joseph_williams@berkeley.edu) Caren M. Walker (caren.walker@berkeley.edu) Tania Lombrozo (lombrozo@berkeley.edu) Department of Psychology, 3410 Tolman Hall Berkeley, CA, 94709 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. Keywords: explanation, self-explanation, learning, generalization, statistics, misconceptions, anomalies. Introduction Human learning relies on the ability to use novel observations about the world to revise current beliefs. This raises basic questions about how observations impact beliefs, and in particular how different cognitive processes influence the process of belief revision. The current paper examines how explaining observations that are anomalous with respect to a learners’ current beliefs influences the nature of belief revision. Previous research reveals that seeking and generating explanations can play an important role in learning and reasoning across a variety of task and domains, including learning novel categories (Williams & Lombrozo, 2010), inferring causal relationships (Koslowski, 1996), and generalizing the properties of people and objects from known to unknown cases (Rehder, 2009; Sloman, 1994). In addition, generating explanations can drive conceptual development in children (Wellman & Liu, 2006) and has been shown to have important pedagogical benefits in a variety of educational contexts (e.g. Fonseca & Chi, 2011). Despite widespread appreciation for the impact of explaining on learning and belief revision, current accounts of explanation’s effects pose a challenging puzzle. On the one hand, explaining is frequently hypothesized to encourage learners to accommodate novel observations in the context of their prior beliefs (Ahn, Brewer & Mooney, 1992; Chi et al, 1994; Lombrozo, 2006; Walker, Williams, Lombrozo & Gopnik, under review; Williams & Lombrozo, 2010). This suggests that explaining could lead learners to draw on a range of belief-preserving strategies in the face of anomalous observations (Chinn & Brewer, 1993; Kuhn, 1962; Koslowski, 1996; Lord, Lepper, & Ross, 1979). Learners who seek explanations could engage in less belief revision (relative to those who do not explain) if they “explain away” anomalies by discounting them as implausible, or reinterpret observations as consistent with or irrelevant to preferred theories. On the other hand, explaining anomalies could prompt learners to reject their currently-held beliefs and construct new theories that better accommodate the anomalous observations. For example, explaining could ensure that anomalies are not simply ignored, increasing learners’ allocation of attention and processing time to these unanticipated observations (Legare, 2010; Siegler, 2002). In addition, explaining could encourage learners to seek and discover general patterns that go beyond prior beliefs to capture the anomalies being explained (Walker, Williams, Lombrozo & Gopnik, under review; Williams & Lombrozo, A third possibility is that explaining has the potential to produce both effects. Whether explaining preserves or revises beliefs could depend on the nature of the evidence provided by the anomalies. For example, relative to other learning strategies, explaining could encourage learners to discount anomalous observations if they are infrequent or there is no alternative theory available to explain them. However, seeking explanations for more extensive inconsistencies could emphasize the limitations of current beliefs and guide learners to alternative theories, thus promoting more radical belief revision. We investigate these possibilities in the context of a statistical judgment task in which participants learn a system for ranking students’ performance across different courses. Participants learn to rank by observing sample rankings, and they are either prompted to provide explanations for the rankings or to engage in free study as a control condition. Previous research suggests that participants’ prior beliefs will favor rankings on the basis of statistically problematic principles rather than one that is normatively defensible (e.g., Schwartz & Martin, 2003; Belenky & Nokes-Malach, in press). We can therefore manipulate how many of the sample rankings happen to be consistent with the non-