Deviations from the predictions of covariational models of causal attribution have often been reported in the literature. These include a bias against using consensus information, a bias toward attributing effects to a person, and a tendency to make a variety of unpredicted conjunctive attributions. It is contended that these deviations, rather than representing irrational biases, could be due to (a) unspecified information over which causal inferences are computed and (b) the questionable normativeness of the models against which these deviations have been measured. A probabilistic extension of Kelley's analysis-of-variance analogy is proposed. An experiment was performed to assess the above biases and evaluate the proposed model against competing ones. The results indicate that the inference process is unbiased.

The covariation component of everyday causal inference has been depicted, in both cognitive and social psychology as well as in philosophy, as heterogeneous and prone to biases. The models and biases discussed in these domains are analyzed with respect to focal sets: contextually determined sets of events over which covariation is computed. Moreover, these models are compared to our probabilistic contrast model, which specifies causes as first and higher order contrasts computed over events in a focal set. Contrary to the previous depiction of covariation computation, the present assessment indicates that a single normative mechanism-the computation of probabilistic contrasts-underlies this essential component of natural causal induction both in everyday and in scientific situations.We do not perceive the visual world as a two-dimensional mosaic of bits of light patches. Instead, these data from the retina are processed by our central visual system to yield a coherent perception of the world, reflecting its visual and spatial structures. Similarly, we do not perceive our lives or the world beyond as a stream of unconnected elemental events. Here, too, central processes act on the data to yield an organized view, structured in terms of commonsensical and scientific theories. Causal induction is an example of such organizing processes. When a government resorts to violent suppression of its people or yields to peaceful reform, when a couple decides to date or a marriage breaks up, or when an epidemic strikes or a new vaccine controls it, we seek out causes.How do ordinary people induce the causes of events? Moreover, given that the primary goals of causal induction are the recovery of the causal structure of the world and the prediction of future events, is the mechanism underlying natural causal induction adequate for satisfying these goals? Covariation-the change in the probability of an effect given the presence versus the absence of a potential cause-has generally been regarded as a necessary (although insufficient) criterion of normative causal induction. 1 The computation of covariation has generated a considerable body of research in the cognitive and social literatures, both of which have presented rather messy pictures of the psychological mechanism. These literatures suggest that the covariation component in natural causal induction is non-

We report the results of 2 experiments and a verbal protocol study examining the component processes of solving mathematical word problems by analogy. College students first studied a problem and its solution, which provided a potential source for analogical transfer. Then they attempted to solve several analogous problems. For some problems, subjects received one of a variety of hints designed to reduce or eliminate the difficulty of some of the major processes hypothesized to be involved in analogical transfer. Our studies yielded 4 major findings. First, the process of mapping xhc features of the source and target problems and the process of adapting the source solution procedure for use in solving the target problem were clearly distinguished: (a) Successful mapping was found to be insufficient for successful transfer and (b) adaptation was found to be a major source of transfer difficulty. Second, we obtained direct evidence that schema induction is a natural consequence of analogical transfer. The schema was found to co-exist with the problems from which it was induced, and both the schema and the individual problems facilitated later transfer. Third, for our multiple-solution problems, the relation between analogical transfer and solution accuracy was mediated by the degree of time pressure exerted for the test problems. Finally, mathematical expertise was a significant predictor of analogical transfer, but general analogical reasoning ability was not. The implications of the results for models of analogical transfer and for instruction were considered.

The discovery of conjunctive causes-factors that act in concert to produce or prevent an effect-has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-power theory that overcomes the problems, and reports empirical evidence favoring the new theory. Unlike earlier models, the new theory derives (a) the conditions under which covariation implies conjunctive causation and (b) functions relating observable events to unobservable conjunctive causal strength. This psychological theory, which concerns simple cases involving 2 binary candidate causes and a binary effect, raises questions about normative statistics for testing causal hypotheses regarding categorical data resulting from discrete variables.A single causal factor is often perceived as contributing toward producing an effect yet insufficient to produce it on its own. Low body resistance in the absence of a flu virus is not by itself sufficient to cause one to have the flu; neither, typically, is the presence of a flu virus per se. The two in conjunction, however, often do cause one to come down with the flu. Likewise, striking a match per se does not cause it to light-there must be oxygen in the environment, the match must be combustible, and so forth. Cigarette smoke per se does not cause lung cancer-the smoke must be inhaled over a relatively long interval, the smoker must be susceptible to the disease, and so forth. The susceptibility itself is probably in turn specified by multiple genetic factors. Hard work alone typically does not produce success; it must be combined with talent and opportunity. Most causes in the real world, like these examples, are complex, involving a conjunction of factors acting in concert, rather than simple, involving a single factor acting alone. How do reasoners come to know that there is something special about the conjunction of several factors such that it can produce or prevent an effect?We first present some phenomena that are inexplicable by previous psychological accounts of conjunctive causation. To explain these phenomena, as well as to solve other problems that beset previous accounts, we propose our causal-power theory of the assessment of interactive causal influence. We then review previous findings in the literature in light of our new theory and report new empirical evidence in support of the theory. Finally, we discuss some implications of our approach for the normative testing of causal hypotheses regarding data resulting from discrete variables.Our new theory, like many previous psychological accounts of conjunctive causation (Cheng & Novick, 1990Forsterling, 1989;Hewstone & Jaspars, 1987;Hilton & Slugoski, 1986;Kelley, 1967), is covariational in that it bases causal inferences on concomitant variations in observed events as well as on other observable features such as t...

No abstract