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• W2586649600 abstract "Revision of Simple Causal Hypotheses: Inferring Interaction Across Multiple Contexts Mimi Liljeholm (mlil@ucla.edu) Department of Psychology, UCLA Patricia Cheng (cheng@psych.ucla.edu) Department of Psychology, UCLA Beatrice Leung (bealeung@ucla.edu) Department of Psychology, UCLA Abstract to change across contexts, the reasoner may infer that c interacts with unobserved background causes occurring in those contexts. We demonstrate our novel method using the scenarios illustrated in Figure 1. Two competing psychological models of causal strength estimation make different predictions regarding when simple causal hypotheses will be rejected in favor of more complex ones. Two experiments tested these predictions, employing a novel method for indirectly assessing perceived causal strength. In both experiments, the task required a judgment regarding the existence of an interaction between a candidate cause and unobserved background causes. Results indicate that reasoners revise simple causal hypotheses based on the mental construct of causal power (Cheng, 1997). Keywords: Causal power; interaction; indirect assessment; coherent generalization Medicine X Inferring an Interaction Across Contexts The psychological debate about human causal learning has focused on the distinction between covariation and causation, and has been carried out in the context of two models that both yield estimates of causal strength: The ΔP rule (e.g., Jenkins & Ward, 1965), which strictly assesses covariation, and the power-PC theory (Cheng, 1997), according to which reasoners make a priori assumptions that allow them to explain covariation by unobservable causal powers in the distal world. Currently, there is evidence for and against both approaches. Specifically, when causal power is held constant while ΔP varies, ratings of causal strength vary ordinally as predicted by ΔP; in contrast, when ΔP is held constant and causal power varies, ratings vary ordinally as predicted by causal power (e.g., Buehner and Cheng, 1997). In other words, strength estimates deviated from both measures. Buehner, Cheng, and Clifford (2003) found that support for the two opposing measures differed depending on the causal-strength question asked. One might argue that the differences were due to biases in the explicit verbal- assessment questions. In the current experiments, to avoid potential demand characteristics of the strength questions, we employed an indirect measure of perceived causal strength based on reasoners’ decision to reject a simple causal hypothesis in favor of a more complex one. Presumably, if the strength of candidate cause c is perceived Medicine X Figure 1: Two hypothetical scenarios across which causal power is constant while ΔP varies. Imagine that you are presented with data from two studies that both test the influence of an allergy medicine (Medicine X) on headache (a possible side effect). In each study, allergy patients are randomly assigned to two groups: a treatment group and a no-treatment (i.e., control) group. Headache (indicated by a frowning face, as opposed to a smiling face) occurs with different relative frequencies across the two control groups (top panels in the figure), as well as across the two treatment groups (bottom panels). What is your best bet, based on the results from both studies, on whether Medicine X interacts with unobserved background causes that vary across the two studies? As mentioned, if one perceives a “change” in the results across the two studies one might infer the existence of such an interaction. But what constitutes a “change”? Equivalently, what is assumed to be invariant, and hence to generalize, across contexts?" @default.
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• W2586649600 date "2007-01-01" @default.
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• W2586649600 title "Revision of Simple Causal Hypotheses: Inferring Interaction Across Multiple Contexts" @default.
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