Recent research on contingency judgment indicates that the judged predictiveness of a cue is dependent on the predictive strengths of other cues. Two classes of models correctly predict such cue interaction: associative models and statistical models. However, these models differ in their predictions about the effect of trial order on cue interaction. In five experiments reported here, college students viewed trial-by-trial data regarding several medical symptoms and a disease, judging the predictive strength of each symptom with respect to the disease. The results indicate that trial order influences the manner in which cues interact, but that neither the associative nor the statistical models can fully account for the data pattern. A possible variation of an associative account is discussed. The ability to detect predictive relationships among envi-ronmental events grants humans and other animals a distinct benefit. Therefore, the mechanisms underlying this ability are of considerable interest. Recent research with humans on judgments of contingencies has shed light on these mecha-nisms. It has suggested two classes of theoretical models that

The rationality of human causal judgments has been the focus of a great deal of recent research. We argue against two major trends in this research, and for a quite different way of thinking about causal mechanisms and probabilistic data. Our position rejects a false dichotomy between "mechanistic " and "probabilistic " analyses of causal inference-- a dichotomy that both overlooks the nature of the evidence that supports the induction of mechanisms and misses some important probabilistic implications of mechanisms. This dichotomy has obscured an alternative conception of causal learning: for discrete events, a central adaptive task is to induce causal mechanisms in the environment from probabilistic data and prior knowledge. Viewed from this perspective, it is apparent that the probabilistic norms assumed in the human causal judgment literature often do not map onto the mechanisms generating the probabilities. Our alternative conception of causal judgment is more congruent with both scientific uses of the notion of causation and observed causal judgments of untutored reasoners. We illustrate some of the relevant variables under this conception, using a framework for causal representation now widely adopted in computer science and, increasingly, in statistics. We also review the formulation and evidence for a theory of human causal induction (Cheng, 1997) that adopts this alternative conception. 1. The Old Mechanism Approach A long and still popular tradition in the study of human causal reasoning insists on a dramatic bifurcation between "mechanistic " conceptions of causalGlymour & Cheng inference and "probabilistic " or "covariational " conceptions of this process (e.g., Ahn

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computation. In preparation. Address for correspondence:

this paper. Correspondence should be addressed to M. J. Dennis, Madsen Center, Augustana College, 2001 S. Summit Ave., Sioux Falls, SD 57197 (e-mail: dennis@inst.augie . edu)

Causal induction provides a nice test domain for examining the influence of individual-difference factors on cognition. The phenomena of both conditionalization and discounting reflect attention to multiple potential causes when people infer what caused an effect. We explored the hypothesis that individuals with an independent self-construal are relatively less sensitive to context (other causes) than are individuals with an interdependent self-construal in this domain. We found greater levels of conditionalization and data consistent with discounting for participants in whom we primed an interdependent self-construal than for participants in whom we primed an independent self-construal. Research on cultural differences and expertise has highlighted the presence of significant individual differences in performance on cognitive tasks that have often been thought to represent more universal cognitive tendencies

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