Patricia W. Cheng

Pragmatic reasoning schemas

We propose that people typically reason about realistic situations using neither content-free syntactic inference rules nor representations of specific experiences. Rather, people reason using knowledge structures that we term pragmatic reasoning schemas, which are generalized sets of rules defined in relation to classes of goals. Three experiments examined the impact of a “permission schema” on deductive reasoning. Experiment 1 demonstrated that by evoking the permission schema it is possible to facilitate performance in Wasons selection paradigm for subjects who have had no experience with the specific content of the problems. Experiment 2 showed that a selection problem worded in terms of an abstract permission elicited better performance than one worded in terms of a concrete but arbitrary situation, providing evidence for an abstract permission schema that is free of domain-specific content. Experiment 3 provided evidence that evocation of a permission schema affects not only tasks requiring procedural knowledge, but also a linguistic rephrasing task requiring declarative knowledge. In particular, statements in the form if p then q were rephrased into the form p only if q with greater frequency for permission than for arbitrary statements, and rephrasings of permission statements produced a pattern of introduction of modals (must, can) totally unlike that observed for arbitrary conditional statements. Other pragmatic schemas, such as “causal” and “evidence” schemas can account for both linguistic and reasoning phenomena that alternative hypotheses fail to explain.

A Probabilistic Contrast Model of Causal Induction

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 Kelleys 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.

Assessing Interactive Causal Influence

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.

Bayesian Generic Priors for Causal Learning

The article presents a Bayesian model of causal learning that incorporates generic priors--systematic assumptions about abstract properties of a system of cause-effect relations. The proposed generic priors for causal learning favor sparse and strong (SS) causes--causes that are few in number and high in their individual powers to produce or prevent effects. The SS power model couples these generic priors with a causal generating function based on the assumption that unobservable causal influences on an effect operate independently (P. W. Cheng, 1997). The authors tested this and other Bayesian models, as well as leading nonnormative models, by fitting multiple data sets in which several parameters were varied parametrically across multiple types of judgments. The SS power model accounted for data concerning judgments of both causal strength and causal structure (whether a causal link exists). The model explains why human judgments of causal structure (relative to a Bayesian model lacking these generic priors) are influenced more by causal power and the base rate of the effect and less by sample size. Broader implications of the Bayesian framework for human learning are discussed.

From covariation to causation: a test of the assumption of causal power.

How humans infer causation from covariation has been the subject of a vigorous debate, most recently between the computational causal power account (P. W. Cheng, 1997) and associative learning theorists (e.g., K. Lober & D. R. Shanks, 2000). Whereas most researchers in the subject area agree that causal power as computed by the power PC theory offers a normative account of the inductive process. Lober and Shanks, among others, have questioned the empirical validity of the theory. This article offers a full report and additional analyses of the original study featured in Lober and Shankss critique (M. J. Buehner & P. W. Cheng, 1997) and reports tests of Lober and Shankss and other explanations of the pattern of causal judgments. Deviations from normativity, including the outcome-density bias, were found to be misperceptions of the input or other artifacts of the experimental procedures rather than inherent to the process of causal induction.

Causal Learning and Inference as a Rational Process: The New Synthesis

Over the past decade, an active line of research within the field of human causal learning and inference has converged on a general representational framework: causal models integrated with bayesian probabilistic inference. We describe this new synthesis, which views causal learning and inference as a fundamentally rational process, and review a sample of the empirical findings that support the causal framework over associative alternatives. Causal events, like all events in the distal world as opposed to our proximal perceptual input, are inherently unobservable. A central assumption of the causal approach is that humans (and potentially nonhuman animals) have been designed in such a way as to infer the most invariant causal relations for achieving their goals based on observed events. In contrast, the associative approach assumes that learners only acquire associations among important observed events, omitting the representation of the distal relations. By incorporating bayesian inference over distributions of causal strength and causal structures, along with noisy-logical (i.e., causal) functions for integrating the influences of multiple causes on a single effect, human judgments about causal strength and structure can be predicted accurately for relatively simple causal structures. Dynamic models of learning based on the causal framework can explain patterns of acquisition observed with serial presentation of contingency data and are consistent with available neuroimaging data. The approach has been extended to a diverse range of inductive tasks, including category-based and analogical inferences.

Causes versus enabling conditions

People distinguish between a cause (e.g., a malfunctioning component in an airplane causing it to crash) and a condition (e.g., gravity) that merely enables the cause to yield its effect. This distinction cannot be explained by accounts of reasoning formulated purely in terms of necessity and sufficiency, because causes and enabling conditions hold the same logical relationship to the effect in those terms. Proposals to account for this apparent deviation from accounts based on necessity and sufficiency may be classified into three types. One approach explains the distinction in terms of an inferential rule based on the normality of the potential causal factors. Another approach explains the distinction in terms of the conversational principle of being informative to the inquirer given assumptions about his or her state of knowledge. The present paper evaluates variants of these two approaches, and presents our probabilistic contrast model, which takes a third approach. This approach explains the distinction between causes and enabling conditions by the covariation between potential causes and the effect in question over a focal set--a set of events implied by the context. Covariation is defined probabilistically, with necessity and sufficiency as extreme cases of the components defining contrasts. We report two experiments testing our model against variants of the normality and conversational views.

Distinguishing genuine from spurious causes: a coherence hypothesis.

Two opposing views have been proposed to explain how people distinguish genuine causes from spurious ones: the power view and the covariational view. This paper notes two phenomena that challenge both views. First, even when 1) there is no innate specific causal knowledge about a regularity (so that the power view does not apply) and 2) covariation cannot be computed while controlling for alternative causes (so that the covariation view should not apply), people are still able to systematically judge whether a regularity is causal. Second, when an alternative cause explains the effect, a spurious cause is judged to be spurious with greater confidence than otherwise (in both cases, no causal mechanism underlies the spurious cause). To fill the gap left by the traditional views, this paper proposes a new integration of these views. According to the coherence hypothesis, although a genuine cause and a spurious one may both covary with an effect in a way that does not imply causality at some level of abstraction, the categories to which these candidate causes belong covary with the effect differently at a more abstract level: one covariation implies causality; the other does not. Given this superordinate knowledge, the causal judgments of a reasoner who seeks to explain as much as possible with as few causal rules as possible will exhibit the properties that challenge the traditional views. Two experiments tested and supported the coherence hypothesis. Both experiments involved candidate causes that covary with an effect without implying causality at some level, manipulating whether covariation that implies causality has been acquired at a more abstract level. The experiments differed on whether an alternative cause explains the effect.

Why Causation Need not Follow From Statistical Association: Boundary Conditions for the Evaluation of Generative and Preventive Causal Powers

In experimental design, a tacit principle is that to test whether a candidate cause c (i.e., a manipulation) prevents an effect e, e must occur at least some of the time without the introduction of c. This principle is the preventive analogue of the explicit principle of avoiding a ceiling effect in tests of whether c produces e. Psychological models of causal inference that adopt either the covariation approach or the power approach, among their other problems, fail to explain these principles. The present article reports an experiment that demonstrates the operation of these principles in untutored reasoning. The results support an explanation of these principles according to the power PC theory, a theory that integrates the previous approaches to overcome the problems that cripple each.

Evoking the Permission Schema: The Impact of Explicit Negation and a Violation-checking Context

Cheng and Holyoak (1985) proposed that realistic reasoning in deontic contexts is based on pragmatic schemas such as those for assessing compliance with or violation of permission and obligation rules, and that the evocation of these schemas can facilitate performance in Wasons (1966) selection task. The inferential rules in such schemas are intermediate in generality between the content-independent rules proposed by logicians and specific cases stored in memory. In one test of their theory, Cheng and Holyoak demonstrated that facilitation could be obtained even for an abstract permission rule that is devoid of concrete thematic content. Jackson and Griggs (1990) argued on the basis of several experiments that such facilitation is not due to evocation of a permission schema, but, rather, results from a combination of presentation factors: the presence of explicit negatives in the statement of cases and the presence of a violation-checking context. Their conclusion calls into question both the generality of content effects in reasoning and the explanation of these effects. We note that Jackson and Griggs did not test whether the same combination of presentation factors would produce facilitation for an arbitrary rule that does not involve deontic concepts, as their proposal would predict. The present study tested this prediction. Moreover, we extended Jackson and Griggs’ comparisons between performance with an abstract permission rule versus an arbitrary rule, introducing clarifications in the statement of each. No facilitation was observed for an arbitrary rule even when explicit negatives and a violation-checking context were used, whereas strong facilitation was found for the abstract permission rule under the same conditions. Performance on the arbitrary rule was not improved even when the instructions indicated that the rule was conditional rather than biconditional. In contrast, a small but reliable degree of facilitation was obtained for the abstract permission rule, with violation-checking content even in the absence of explicit negatives. The theory of pragmatic reasoning schemas can account for both the present findings and those reported by Jackson and Griggs.