This article presents a theory of categorization that accounts for the effects of causal knowledge that relates the features of categories. According to causal-model theory, people explicitly represent the probabilistic causal mechanisms that link category features and classify objects by evaluating whether they were likely to have been generated by those mechanisms. In 3 experiments, participants were taught causal knowledge that related the features of a novel category. Causal-model theory provided a good quantitative account of the effect of this knowledge on the importance of both individual features and interfeature correlations to classification. By enabling precise model fits and interpretable parameter estimates, causal-model theory helps place the theory-based approach to conceptual representation on equal footing with the well-known similarity-based approaches. For the last several decades, research on the topic of categorization has focused on the problem of learning new categories via examples of category members, that is, from empirical observations. The result has been a host of categorization models that are based on representational ideas such as central prototypes, stored exemplars, and variabilized rules, and on processing principles such as similarity, that have considerable explanatory power and experimental support. More recently, the influence of the prior “theoretical ” knowledge that learners often contribute to their representations of categories has also been a topic of study (Carey,

This paper is twofold. First, we presentes an extended system EPDL of propositional dynamic logic by allowing a proposition as a modality in order to represent and specify indirect effects of actions and causal propagation. An axiomatic deductive system is given which is sound and complete with respect to the corresponding semantics. The resultant system provides a unified treatment of direct and indirect effects of actions. Second, we reduce the EPDL into a mutlimodal logic by deleting the component of action in order to obtain an axiomatized logical system for causal propagation. A characterization theorem of the logic is given. Properties of causal reasoning with the logic are discussed.

This paper is concerned with establishing broadly-based system-theoretic foundations and practical techniques for the problem of system identification that are rigorous, intuitively clear and conceptually powerful. A general formulation is first given in which two order relations are postulated on a class of models: a constant one of complexity; and a variable one of approximation induced by an observed behaviour. An admissible model is such that any less complex model is a worse approximation. The general problem of identification is that of finding the admissible subspace of models induced by a given behaviour. It is proved under very general assumptions that, if deterministic models are required then nearly all behaviours require models of nearly maximum complexity. A general theory of approximation between models and behaviour is then developed based on subjective probability concepts and semantic information theory The role of structural constraints such as causality, locality, finite memory, etc., are then discussed as rules of the game. These concepts and results are applied to the specific problem or stochastic automaton, or grammar, inference. Computational results are given to demonstrate that the theory is complete and fully operational. Finally the formulation of identification proposed in this paper is analysed in terms of Klir’s epistemological hierarchy and both are discussed in terms of the rich philosophical literature on the acquisition of knowledge. 1

Commonsense causal discourse requires a language with which to express varying degrees of causal connectedness. This paper presents a commonsense language for reasoning about action and causation whose semantics is expressed by way of counterfactuals. Causal relations are analyzed along several dimensions including notions of resource consumption, degree of responsibility, instrumentality, and degree of causal contribution. Grounding the semantics in a level of counterfactual reasoning is shown to play an important role in constraining the set of allowable event descriptions instantiating reports expressed by any of the relations in the language. These ideas are also applied to a causal analysis of rational action: by adopting an explanatory stance, one can characterize action through descriptions that refer to causal connections between mental states and actions. Such a causal analysis resolves some well-known difficulties in correctly ascribing agency and intentionality. Finally, an ...

Research suggests that causal judgment is influenced primarily by counterfactual or covariational reasoning. In contrast, the author of this article develops judgment dissociation theory (JDT), which predicts that these types of reasoning differ in function and can lead to divergent judgments. The actuality principle proposes that causal selections focus on antecedents that are sufficient to generate the actual outcome. The substitution principle proposes that ad hoc categorization plays a key role in counterfactual and covariational reasoning such that counterfactual selections focus on antecedents that would have been sufficient to prevent the outcome or something like it and covariational selections focus on antecedents that yield the largest increase in the probability of the outcome or something like it. The findings of 4 experiments support JDT but not the competing counterfactual and covariational accounts. If causation is the cement of the universe, as the philosopher David Hume (1740/1938) put it, then it is fair to say that causal knowledge is the cement that binds together each person’s representational universe. Causal reasoning—the process that generates this glue—confers many functional advantages. In virtually every sphere of human interest, our abilities to learn and categorize

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The significance of counterfactual thinking in the causal judgment 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 judgment. Three experiments examined this comparison by manipulating the task frame used to focus participants’ thinking about a target event. Prior to making judgments 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 judgment. Implications for research on causal judgment and mental representation are discussed.

Privacy policies often place requirements on the purposes for which a governed entity may use personal information. For example, regulations, such as HIPAA, require that hospital employees use medical information for only certain purposes, such as treatment. Thus, using formal or automated methods for enforcing privacy policies requires a semantics of purpose requirements to determine whether an action is for a purpose or not. We provide such a semantics using a formalism based on planning. We model planning using a modified version of Markov Decision Processes, which exclude redundant actions for a formal definition of redundant. We use the model to formalize when a sequence of actions is only for or not for a purpose. This semantics enables us to provide an algorithm for automating auditing, and to describe formally and compare rigorously previous enforcement methods. This research was supported by the US Army Research Office under grant numbers W911NF0910273 and DAAD-190210389. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of any sponsoring institution, the U.S. government or any other entity. This manuscript was submitted to the 24th IEEE Computer Security Foundations Symposium.

Although past theory and research have suggested that counterfactual thoughts (representations of alternatives to past outcomes) weaken the hindsight bias (after-thefact exaggeration of an outcome’s a priori likelihood), the present research shows the opposite (i.e., positive) relation. Experiment 1 demonstrated that counterfactual thinking can heighten the hindsight bias, and that the effect of counterfactuals on causal inferences can account for this relation. Experiment 2 indicated that postoutcome elaboration of the causal linkage between an antecedent and outcome is essential for thehindsightbias,and that this biasmay be redefinedto includepostoutcomecertainty regarding ‘‘what should have been’ ’ as well as what was. Experiment 3 provided more direct evidence that causal inferences mediate the facilitative effect of counterfactual thinking on the hindsight bias. © 1996 Academic Press, Inc. I just knew I should have picked door number two. L et’s Make a Deal contestant The above comment exemplifies a perception familiar perhaps not only to gameshowcontestants,but tomany ofus.Anunfortunateset of circumstances befalls us and we recognize instantly—alas, too late—that we might have This research was supported by a postdoctoral fellowship awarded to Neal Roese and a researchgrant awardedto James Olson,both fromthe SocialSciencesand HumanitiesResearch Council of Canada. We thank Dave Hamilton, Dale Miller, Mike Ross, Richard Sorrentino, Yaacov Trope, and two anonymous reviewers for providing valuable comments on various versionsof the manuscript.We are also grateful to Eileenda Pena for herassistance in runningthe first experiment. Correspondence and reprint requests should be addressed to Neal Roese,

A knowledge digraph defines a set of semantic (or syntactic) associative relationships among propositions in a text (e.g., Graesser and Clark (1985) conceptual graph structures and the causal network analysis of Trabasso & van den Broek, 1985). This paper introduces the Knowledge Digraph Contribution (KDC) data analysis methodology for quantitatively measuring the degree to which a given knowledge digraph can account for the occurrence of specific sequences of propositions in recall, summarization, talkaloud, and question-answering protocol data. KDC data analysis provides statistical tests for selecting the knowledge digraph which "best-fits" a given data set. KDC data analysis also allows one to test hypotheses about the relative contributions of each member in a set of knowledge digraphs. The validity of specific knowledge digraph representational assumptions may be evaluated by comparing human protocol data with protocol data generated by sampling from the KDC distribution. Specifi...