People routinely draw inferences about others ’ preferences by observing their decisions. We study these inferences by characterizing a space of simple observed decisions. Previous work on attribution theory has identified several factors that predict whether a given decision provides strong evidence for an underlying preference. We identify one additional factor and show that a simple probabilistic model captures all of these factors. The model goes beyond verbal formulations of attribution theory by generating quantitative predictions about the full set of decisions that we consider. We test some of these predictions in two experiments: one with decisions involving positive effects and one with decisions involving negative effects. The second experiment confirms that inferences vary in systematic ways when positive effects are replaced by negative effects.

Traditional approaches to causal attribution propose that information about covariation of factors is used to identify causes of events. In contrast, we present a series of studies showing that people seek out and prefer information about causal mechanisms rather than information about covariation. Experiments 1, 2 and 3 asked subjects to indicate the kind of information they would need for causal attribution. The subjects tended to seek out information that would provide evidence for or against hypotheses about underlying mechanisms. When asked to provide causes, the subjects' descriptions were also based on causal mechanisms. In Experiment 4, subjects received pieces of conflicting evidence matching in covariation values but differing in whether the evidence included some statement of a mechanism. The influence of evidence was significantly stronger when it included mechanism information. We conclude that people do not treat the task of causal attribution as one of identifying a novel causal relationship between arbitrary factors by relying solely on covariation information. Rather, people attempt to seek out causal mechanisms in developing a causal explanation for a specific event.

this paper we argue that the two phenomena are based on a single process and they can both occur with the same stimulus materials under certain situations. Before presenting our own view, the following section briefly reviews previous causal attribution theories in order to clarify our approach to this issue

People use information about the covariation between a putative cause and an outcome to determine if a causal relationship obtains. When there are two candidate causes and one is more strongly related to the effect than the other, the influence of the second is underestimated. This phenomenon is called causal discounting. In two studies, we adapted paradigms for studying causal learning to apply signal detection analysis to this phenomenon. We investigated whether the presence of a stronger alternative makes the task more difficult (indexed by differences in d’), or if people change the standard by which they assess causality (measured by β). Our results indicate the effect is due to bias. R579B Causal Discounting 3 Humans can use knowledge of covariation to predict events and to infer their underlying causes (Cheng, 1997). Although research has demonstrated a number of systematic phenomena in covariation and causal judgment, it is unclear whether these effects occur during the learning or decision process. Here we use signal detection theory (SDT) to tease apart these alternatives for one phenomenon: causal discounting. Discounting is a cue interaction effect, in which someone judges a moderately effective

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Integration of contingency information underlies many cognitive tasks including causal, covariational, and probability judgments. The authors'feature-analytic approach was used to account for the findings that people differentially weight specific types of conjunctive information in causal (Experiment 1) and noncausal (Experiment 2) contingency judgments. These findings were explained in terms of positive-test and sufficiency-test biases, which were found in both judgment domains. The same biases, however, were not observed in normative conditional-probability judgments (Experiment 3). The authors argue that this discrepancy is owing to the differential clarity of normative criteria in these domains. Much of human learning and inferential thinking depends on the integration of contingency information. To test hypotheses and revise beliefs; to explain past events and predict future ones; to establish categories, form stereotypes, and develop impressions of others, humans integrate a vast amount of information about interevent contingencies. In short, the ability to discriminate contingencies in the physical

Causation by omission is instantiated when an effect occurs from an absence, as in The absence of nicotine causes withdrawal or Not watering the plant caused it to wilt. The phenomenon has been viewed as an insurmountable problem for process theories of causation, which specify causation in terms of conserved quantities, like force, but not for theories that specify causation in terms of statistical or counterfactual dependencies. A new account of causation challenges these assumptions. According to the force theory, absences are causal when the removal of a force leads to an effect. Evidence in support of this account was found in three experiments in which people classified animations of complex causal chains involving force removal, as well as chains involving virtual forces, that is, forces that were anticipated but never realized. In a fourth experiment, the force theory’s ability to predict synonymy relationships between different types of causal expressions provided further evidence for this theory over dependency theories. The findings show not only how causation by omission can be grounded in the physical world, but also why only certain absences, amongst the potentially infinite number of absences, are causal.

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Framing effects occur when logically equivalent redescriptions of objects or outcomes lead to different behavior, and such effects have traditionally been seen as irrational. However, recent evidence has shown that a speaker’s choice among logically equivalent attribute frames can implicitly convey (or “leak”) normatively relevant information about, among other things, the speaker’s reference point. Reinterpreting data published elsewhere, this article shows that some common effects in inference tasks (covariation assessment and hypothesis testing) can also be seen as framing effects, thereby expanding the domain of framing. It is also shown that these framing effects are normatively defensible because normatively relevant information about event rarity is leaked through the description of data and through the phrasing of hypotheses, thereby broadening the information leakage approach to explaining framing effects. Information leakage can also explain why framing effects in these inference tasks disappear under certain conditions. Framing Effects in Inference Tasks-- 3 Framing Effects in Inference Tasks – And Why They’re Normatively Defensible A trend in research on reasoning is to explain, in rational terms, behavior that has