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• W2768144283 abstract "A Structure-dependent Causal Diversity Effect in Diagnostic Reasoning Felix G. Rebitschek (felix.rebitschek@uni-greifswald.de) University of Greifswald, Department of Psychology Josef F. Krems (josef.krems@phil.tu-chemnitz.de) Technische Universitat Chemnitz, Department of Psychology Georg Jahn (georg.jahn@uni-greifswald.de) University of Greifswald, Department of Psychology Abstract each into two effects. The effects 1 and 2 form a proximal pair of effects (likewise effects 3 and 4). In contrast, distally located effects, which do not share an intermediate cause form a diverse pair (e.g., effects 2 and 3). The purpose in diagnostic reasoning is to find the cause of observed effects by applying knowledge about the effects and their potential causes. In the causal structure linking causes and effects, effects can share causes or be linked more indirectly. The causal diversity effect reflects the increased support of a cause by a more widespread distribution of effects within the underlying causal structure. We report two experiments, in which participants acquired knowledge about causal structures and then evaluated diverse and proximal effect patterns with regard to their support for inferring a cause. The diversity effect in diagnostic reasoning was stronger if participants had acquired integrated knowledge about causal structures. Moreover, teaching a reduced structure with less nodes open to alternative causation of proximal effects decreased the diversity effect. This confirmed that the causal diversity effect results from considering alternative causation and more generally that diagnostic reasoning draws on causal representations. Figure 1: Exemplary causal structure depicted as a graphical causal model; equals the causal structure regarding a single chemical in Experiment 2. Keywords: causal diversity effect; diagnostic reasoning; causal reasoning; causal models Introduction In finding the cause of observed effects, a diagnostician consults knowledge about the effects and their relation to potential causes. However, there are varying ideas about how this causal knowledge is represented. Reasoning about causality can proceed without representations of causal concepts (e.g. Goldvarg & Johnson-Laird, 2001; Shanks, 2010). Nevertheless, causal model representations have many proponents (Krynski & Tenenbaum, 2007; Sloman, 2005; Waldmann & Hagmayer, 2013), and are assumed in causal learning (Holyoak & Cheng, 2011) and causal reasoning (Fernbach & Erb, 2013; Sloman & Lagnado, 2005; Waldmann , 2000). Evidence consistent with causal model representations was provided, for instance, by the causal status effect (Ahn, Kim, Lassaline, & Dennis, 2000). Furthermore, the distribution of attributes in causal networks affects categorization (Rehder & Hastie, 2001), and causal diversity of symptoms was found to influence diagnostic reasoning (Kim & Keil, 2003). The present study examines the causal diversity effect. Diversity in a causal structure means the widespread distribution of effects. Regarding Figure 1, a single root cause spreads into two intermediates, which in turn spread If your task was to evaluate support for the root cause, would you rate the support provided by a diverse pair of effects as higher than the support provided by a proximal pair? You should. Scientists are encouraged to search widely varying support for their hypotheses (Heit, Hahn, & Feeney, 2005) and statistical theory points to the value of diverse evidence (Heit, 1998; Horwich, 1982): According to Bayes’ theorem, hardly likely diverse evidence rules out many of the most plausible causes, from which probably one or more would cause proximal effects. In psychological research, effects of diversity of information on thinking and reasoning have been shown in the evaluation of categorical arguments (premise diversity phenomenon in categorical induction, Osherson, Smith, Wilkie, & Lopez, 1990), in the testing of arguments (Lopez, 1995), in the search for diagnostic information (Kim, Yopchick, & Kwaadsteniet, 2008), and in diagnostic reasoning (Kim & Keil, 2003). Kim and Keil’s (2003) participants were taught a causal structure similar to the one shown in Figure 2. The causal chains linking the root cause with the effects were presented separately in order to prevent visuo-spatial encoding of the distance between effects. The subsequent diagnostic reasoning task required to decide for whom of two patients the root cause was more probable. One patient presented a pair of proximal symptoms (e.g. in Figure 2, impaired speech and disability of motion), the other patient presented a pair of diverse symptoms (e.g., disability of motion and stomachache). The patient with the diverse symptoms was" @default.
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• W2768144283 date "2014-01-01" @default.
• W2768144283 modified "2023-09-23" @default.
• W2768144283 title "A Structure-dependent Causal Diversity Effect in Diagnostic Reasoning" @default.
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