A theory of categorization is presented in which knowledge of causal relationships between category features is represented in terms of asymmetric and probabilistic causal mechanisms. According to causal‐model theory, objects are classified as category members to the extent they are likely to have been generated or produced by those mechanisms. The empirical results confirmed that participants rated exemplars good category members to the extent their features manifested the expectations that causal knowledge induces, such as correlations between feature pairs that are (...) directly connected by causal relationships. These expectations also included sensitivity to higher‐order feature interactions that emerge from the asymmetries inherent in causal relationships. Quantitative fits of causal‐model theory were superior to those obtained with extensions to traditional similarity‐based models that represent causal knowledge either as higher‐order relational features or “prior exemplars” stored in memory. (shrink)

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. (shrink)

Research on human causal induction has shown that people have general prior assumptions about causal strength and about how causes interact with the background. We propose that these prior assumptions about the parameters of causal systems do not only manifest themselves in estimations of causal strength or the selection of causes but also when deciding between alternative causal structures. In three experiments, we requested subjects to choose which of two observable variables was the cause and which the effect. We found (...) strong evidence that learners have interindividually variable but intraindividually stable priors about causal parameters that express a preference for causal determinism. These priors predict which structure subjects preferentially select. The priors can be manipulated experimentally and appear to be domain-general. Heuristic strategies of structure induction are suggested that can be viewed as simplified implementations of the priors. (shrink)

The significance of counterfactual thinking in the causal judgement process has been emphasized for nearly two decades, yet no previous research has directly compared the relative effect of thinking counterfactually versus factually on causal judgement. Three experiments examined this comparison by manipulating the task frame used to focus participants' thinking about a target event. Prior to making judgements about causality, preventability, blame, and control, participants were directed to think about a target actor either in counterfactual terms (what the actor could (...) have done to change the outcome) or in factual terms (what the actor had done that led to the outcome). In each experiment, the effect of counterfactual thinking did not differ reliably from the effect of factual thinking on causal judgement. Implications for research on causal judgement and mental representation are discussed. (shrink)

The study of argument has never before been so wide-ranging. The evidence for this claim is to be found in a growing number of different conceptions of argument, each of which purports to describe some component of argument that is effectively over-looked by other conceptions of this notion. Just this same sense that a vital component of argument is being overlooked by current conceptions of this notion is what motivates Dale Hample to pursue a specifically cognitive conception of argument. However, (...) Hamples contribution to the study of argument extends beyond his development of a view of argument as cognition. For Hample is reflective on the interrelationship of his cognitive conception of argument to two other views of argument within which most conceptions of this notion may be taken to fall, the traditional view of argument as a textual product and the view of argument as a social phenomenon. I will argue, however, that what starts out as a well-intentioned aim on the part of Hample to pursue a comprehensive analysis of the notion of argument ends in the circumscription of this concept through Hample s denial of the primacy of argument. I will also argue that a circumscribed concept of argument is an unintelligible concept of argument. The context of my claims will be a similar charge of unintelligibility by Hilary Putnam against a logical positivistic conception of rationality. (shrink)

Why does discourse so often seem shallow, with people arguing past one another more than with one another? Might contributing causes be individual and logical rather than only dialogical? We consider here whether there exist errors in reasoning that could be particularly damaging in their effects on argumentive discourse. In particular, we examine implications for discourse of two such errors – explanation as a replacement for evidence and neglecting the likelihood of multiple causes contributing to an outcome. In Studies 1 (...) and 2, we show these errors to be prevalent in a cross section of adults, as well as samples of community college students and young adolescents, with minimal age-related improvement. They also occur, although less frequently, among a sample of highly educated adults, and in Study 3, we examine their role in the discourse of college-educated adults. We point finally to evidence that these individual reasoning errors are potentially addressable through education. (shrink)

The field of causal learning and reasoning (largely overlooked in the target article) provides an illuminating case study of how the modern Bayesian framework has deepened theoretical understanding, resolved long-standing controversies, and guided development of new and more principled algorithmic models. This progress was guided in large part by the systematic formulation and empirical comparison of multiple alternative Bayesian models.

This experiment investigated the use of positive and negative hypothesis and target tests by groups in an adaptation of the 2-4-6 Wason task. The experimental variables were range of rule (small vs large), amount of evidence (low vs high), and trial block (1 vs 2). The results were in accordance with Klayman and Ha's (1987) analysis of base rate probabilities of falsification and with additional theoretical considerations. Base rate probabilities were more descriptive of participants' behaviour in target than in hypothesis (...) tests, under low than under high amount of evidence, and at the beginning of the process than at its end. The percentage of positive tests was higher under small than large range of rule. More falsifications than verifications resulted from hypothesis tests than would be expected by a random process. When evidence is richly available, the relative importance of falsification seems to decrease. An analysis of the group compositions before and after group discussion by the PCD model (Crott, Werner, & Hoffmann, 1996) revealed that the normative weight was approximately twice as large as the informational. Groups produced fewer false answers than their members individually. (shrink)

Good explanations are not only true or probably true, but are also relevant to a causal question. Current models of causal explanation either only address the question of the truth of an explanation, or do not distinguish the probability of an explanation from its relevance. The tasks of scenario construction and conversational explanation are distinguished, which in turn shows how scenarios can interact with conversational principles to determine the truth and relevance of explanations. The proposed model distinguishes causal discounting from (...) causal backgrounding , and makes predictions concerning the differential effects of contextual information about alternative explanations on: (a) the kind of mental models constructed; (b) belief revision about probable cause; and (c) the perceived quality of a focal explanation. Four experiments are reported that test these predictions. The significance of the notion of explanatory relevance for research on causal explanation is then discussed. (shrink)

Causal induction in the real world often has to be quick and efficient as well as accurate. We propose that people use two different frames to achieve these goals. The A-frame consists of heuristic processes that presuppose rarity and can detect causally relevant factors quickly. The B-frame consists of analytic processes that can be highly accurate in detecting actual causes. Our dual frame theory implies that several factors affect whether people use the A-frame or the B-frame in causal induction: among (...) these are symmetrical negation, intervention and commitment. This theory is tested and sustained in two experiments. The results also provide broad support for dual process accounts of human thinking in general. (shrink)

In this article, 41 models of covariation detection from 2 × 2 contingency tables were evaluated against past data in the literature and against data from new experiments. A new model was also included based on a limiting case of the normative phi‐coefficient under an extreme rarity assumption, which has been shown to be an important factor in covariation detection (McKenzie & Mikkelsen, 2007) and data selection (Hattori, 2002; Oaksford & Chater, 1994, 2003). The results were supportive of the new (...) model. To investigate its explanatory adequacy, a rational analysis using two computer simulations was conducted. These simulations revealed the environmental conditions and the memory restrictions under which the new model best approximates the normative model of covariation detection in these tasks. They thus demonstrated the adaptive rationality of the new model. (shrink)

This paper outlines a theory and computer implementation of causal meanings and reasoning. The meanings depend on possibilities, and there are four weak causal relations: A causes B, A prevents B, A allows B, and A allows not‐B, and two stronger relations of cause and prevention. Thus, A causes B corresponds to three possibilities: A and B, not‐A and B, and not‐A and not‐B, with the temporal constraint that B does not precede A; and the stronger relation conveys only the (...) first and last of these possibilities. Individuals represent these relations in mental models of what is true in the various possibilities. The theory predicts a number of phenomena, and, contrary to many accounts, it implies that the meaning of causation is not probabilistic, differs from the meaning of enabling conditions, and does not depend on causal powers or mechanisms. The theory also implies that causal deductions do not depend on schemas or rules. (shrink)

Three experiments investigated reasoners' beliefs about causal powers; that is, their beliefs about the capacity of a putative cause to produce a given effect. Covariation-based theories (e.g., Cheng, 1997; Kelley, 1973; Novick & Cheng, 2004) posit that beliefs in causal power are represented in terms of the degree of covariation between the cause and its effect; covariation is defined in terms of the degree to which the effect occurs in the presence of the cause, and fails tooccur in the absence (...) of the cause. To test the degree to which beliefs incausal power are reflected in beliefs about covariation information, participants in three experiments rated their beliefs that putative causes have the capacity to produce a given effect (i.e., possess the causal power to produce an effect) as well as their beliefs regarding the degree to which the putative cause and effectcovary. A strong positive correlation was discovered between participants' beliefs in causal power and their beliefs that the effect occurs in the presence of the cause. However, no direct relationship was found between participants' beliefs in causal power and their belief that the effect will fail tooccur in the absence of the cause. These findings were replicated using bothwithin- (Experiments 1 and 3) and between-subject designs (Experiment 2). In Experiment 3, we extended these analyses to measures of familiarity, imageability, and detailedness of the representation. We found that participants' beliefs in causal power were strongly associated with familiarity, and imageability, but not the perceived detailedness of the cause and effect relationship. These data provide support for a multidimensional account of causal knowledge whereby people's representations of causation include, but are not limited to, the covariation, familiarity, and imageability of cause and effect relationships. (shrink)