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Causal judgments are widely known to be sensitive to violations of both prescriptive norms (e.g., immoral events) and statistical norms (e.g., improbable events). There is ongoing discussion as to whether both effects are best explained in a unified way through changes in the relevance of counterfactual possibilities, or whether these two effects arise from unrelated cognitive mechanisms. Recent work has shown that moral norm violations affect causal judgments of agents, but not inanimate artifacts used by those agents. These results have been interpreted as showing that prescriptive norm violations only affect causal reasoning about intentional agents, but not the use of inanimate artifacts, thereby providing evidence that the effect of prescriptive norm violations arises from mechanisms specific to reasoning about intentional agents, and thus casting doubt on a unified counterfactual analysis of causal reasoning. Four experiments explore this recent finding and provide clear support for a unified counterfactual analysis. Experiment 1 demonstrates that these newly observed patterns in causal judgments are closely mirrored by judgments of counterfactual relevance. Experiment 2 shows that the relationship between causal and counterfactual judgments is moderated by causal structure, as uniquely predicted by counterfactual accounts. Experiment 3 directly manipulates the relevance of counterfactual alternatives and finds that causal judgments of intentional agents and inanimate artifacts are similarly affected. Finally, Experiment 4 shows that prescriptive norm violations (in which artifacts malfunction) affect causal judgments of inanimate artifacts in much the same way that prescriptive norm violations (in which agents act immorally) affect causal judgments of intentional agents., (© 2019 Cognitive Science Society, Inc.)

Watching another person take actions to complete a goal and making inferences about that person's knowledge is a relatively natural task for people. This ability can be especially important in educational settings, where the inferences can be used for assessment, diagnosing misconceptions, and providing informative feedback. In this paper, we develop a general framework for automatically making such inferences based on observed actions; this framework is particularly relevant for inferring student knowledge in educational games and other interactive virtual environments. Our approach relies on modeling action planning: We formalize the problem as a Markov decision process in which one must choose what actions to take to complete a goal, where choices will be dependent on one's beliefs about how actions affect the environment. We use a variation of inverse reinforcement learning to infer these beliefs. Through two lab experiments, we show that this model can recover people's beliefs in a simple environment, with accuracy comparable to that of human observers. We then demonstrate that the model can be used to provide real-time feedback and to model data from an existing educational game., (Copyright © 2014 Cognitive Science Society, Inc.)

We report on five experiments investigating response choices and response times to simple science questions that evoke student "misconceptions," and we construct a simple model to explain the patterns of response choices. Physics students were asked to compare a physical quantity represented by the slope, such as speed, on simple physics graphs. We found that response times of incorrect answers, resulting from comparing heights, were faster than response times of correct answers comparing slopes. This result alone might be explained by the fact that height was typically processed faster than slope for this kind of task, which we confirmed in a separate experiment. However, we hypothesize that the difference in response time is an indicator of the cause (rather than the result) of the response choice. To support this, we found that imposing a 3-s delay in responding increased the number of students comparing slopes (answering correctly) on the task. Additionally a significant proportion of students recognized the correct written rule (compare slope), but on the graph task they incorrectly compared heights. Finally, training either with repetitive examples or providing a general rule both improved scores, but only repetitive examples had a large effect on response times, thus providing evidence of dual paths or processes to a solution. Considering models of heuristics, information accumulation models, and models relevant to the Stroop effect, we construct a simple relative processing time model that could be viewed as a kind of fluency heuristic. The results suggest that misconception-like patterns of answers to some science questions commonly found on tests may be explained in part by automatic processes that involve the relative processing time of considered dimensions and a priority to answer quickly., (Copyright © 2014 Cognitive Science Society, Inc.)

Syntactic complexity effects have been investigated extensively with respect to comprehension (e.g., Demberg & Keller, 2008; Gibson, 1998, 2000; Gordon et al., 2001, 2004; Grodner & Gibson, 2005; King & Just, 1991; Lewis & Vasishth, 2005; Lewis et al., 2006; McElree et al., 2003; Wanner & Maratsos, 1978). According to one prominent class of accounts (experience-based accounts; e.g., Hale, 2001; Levy, 2008; Gennari & MacDonald, 2008, 2009; Wells et al., 2009), certain structures cause comprehension difficulty due to their scarcity in the language. But why are some structures less frequent than others? In two elicited-production experiments we investigated syntactic complexity effects in relative clauses (Experiment 1) and wh-questions (Experiment 2) varying in whether or not they contained non-local dependencies. In both experiments, we found reliable durational differences between subject-extracted structures (which only contain local dependencies) and object-extracted structures (which contain nonlocal dependencies): Participants took longer to begin and produce object-extractions. Furthermore, participants were more likely to be disfluent in the object-extracted constructions. These results suggest that there is a cost associated with planning and uttering the more syntactically complex, object-extracted structures, and that this cost manifests in the form of longer durations and disfluencies. Although the precise nature of this cost remains to be determined, these effects provide one plausible explanation for the relative rarity of object-extractions: They are more costly to produce., (Copyright © 2014 Cognitive Science Society, Inc.)