The role of covariation versus mechanism information in causal attribution.

By Woo-Kyoung Ahn, C. Kalish, Douglas L. Medin, Susan A. Gelman. 1995

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “Relevant research on causal reasoning relevant to explanation includes: Ahn (1998); Ahn et al. (1995); Ahn and Kalish (2000); Chater and Oaksford, (2006); Cheng (1997, 2000); Einhorn and Hogarth (1986); Eiter and Lukasiewicz (2002, 2006); Feltovich, Hoffman, Woods, and Roesler (2004); Graesser,…”
Judgment dissociation theory: an analysis of differences in causal, counterfactual, and covariational reasoning. (2003, background): “Hence, JDT draws conceptual connections to accounts that posit the importance of perceived mechanisms (Ahn & Kalish, 2000; Ahn, Kalish, Medin, & Gelman, 1995; Harre ´ & Madden, 1975; Michotte, 1946/1963; Shultz, Fisher, Pratt, & Rulf, 1986) and perceived changes of propensity (Kahneman & Varey, 19”
Semantic Cognition: A Parallel Distributed Processing Approach (2004, background): “Seminal experiments described by Keil (1989), the influential work of Ahn and colleagues (Ahn, 1998; Ahn et al., 2002; Ahn, Gelman, Amsterlaw, Hohenstein, & Kalish, 2000; Ahn et al., 1995), and studies described by Gopnik (e.”
Scientific Thinking and Reasoning (2012, background): “Researchers from a number of cognitive psychology laboratories have found that people prefer to gather more information about an underlying mechanism than covariation between a cause and an effect (e.g., Ahn et al., 1995).”
From covariation to causation: A causal power theory. (1997, background): “Ahn et al. (1995), for example, wrote that "people seek out and prefer information about causal mechanisms rather than information about covariation" (p. 299) and that "when direct information about mechanisms is difficult or impossible to attain, . . . covariation methods can be useful heuristics"…”
Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers (2004, background): “There is evidence that children do use their existing knowledge of causal mechanisms to learn new causal relations and make causal inferences (Ahn, Gelman, Amsterlaw, Hohenstein, & Kalish, 2000; Ahn, Kalish, Medin, & Gelman, 1995; Bullock et al., 1982; Shultz, 1982).”
Knowledge mediates the timeframe of covariation assessment in human causal induction (2002, background): “…but research has mainly focused on how prior knowledge determines whether a given covariation will give rise to causal impressions or not (e.g., Ahn et al., 1995; White, 1995; but see also Lien & Cheng, 2000), and how previously acquired category structures (Waldmann & Hagmayer, 1999) or…”
Explanation and evidence in informal argument (2000, background): “More recently, researchers have found that students who explain study materials to themselves show better comprehension of that material, are more successful in solving problems using those materials, and exhibit improved transfer (McNamara, Kintsch, Songer & Kintsch, 1996; Chi, Bassok, Lewis, Reima”