Abstract. I argue that psychologists interested in human causal judgment should understand and adopt a representation of causal mechanisms by directed graphs that encode conditional independence (screening off) relations. I illustrate the benefits of that representation, now widely used in computer science and increasingly in statistics, by (i) showing that a dispute in psychology between ‘mechanist’ and ‘associationist ’ psychological theories of causation rests on a false and confused dichotomy; (ii) showing that a recent, much-cited experiment, purporting to show that human subjects, incorrectly let large causes ‘overshadow ’ small causes, misrepresents the most likely, and warranted, causal explanation available to the subjects, in the light of which their responses were normative; (iii) showing how a recent psychological theory (due to P. Cheng) of human judgment of causal power can be considerably generalized: and (iv) suggesting a range of possible experiments comparing human and computer abilities to extract causal information from associations.

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

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

has elicited considerable criticism from the education research community, but this criticism has not focused on a key assumption of the report—its Humean, regularity conception of causality. It is argued that this conception, which also underlies other arguments for “scientifically-based research, ” is narrow and philosophically outdated, and leads to a misrepresentation of the nature and value of qualitative research for causal explanation. An alternative, realist approach to causality is presented that supports the scientific legitimacy of using qualitative research for causal investigation, reframes the arguments for experimental methods in educational research, and can support a more productive collaboration between qualitative and quantitative researchers. Amajor effort to establish “scientifically-based research” (SBR) in education has been under way for some time

Management (KM) are important, often synonymous, terms. In KM they often refer to private or personal knowledge that needs to be made public. The original reference of “tacit knowledge ” is to the work of the late scientist and philosopher, Michael Polanyi (Polanyi, 1969), but there is substantial evidence that the KM discourse has poorly understood Polanyi’s term. Two theoretical problems in Knowledge Management’s notion of “implicit knowledge, ” which undermine empirical work in this area, are examined. The first problem involves understanding the term “knowledge ” according to a folk-psychology of mental representation to model expression. The second is epistemological and social: understanding Polanyi’s term, tacit knowing as a psychological concept instead of as an epistemological problem, in general, and one of social epistemology and of the epistemology of the sciences, in particular. Further, exploring Polanyi’s notion of tacit knowing in more detail yields important insights into the role of knowledge in science, including empirical work in information science. This article has two parts: first, there is a discussion of the folk-psychology model of representation and the need to replace this with a more expressionist model. In the second part, Polanyi’s concept of tacit knowledge in relation to the role of analogical thought in expertise is examined. The works of philosophers, particularly Harré and Wittgenstein, are brought to bear on these problems. Conceptual methods play several roles in information science that cannot satisfactorily be performed empirically at all or alone. Among these roles, such methods may examine historical issues, they may critically engage foundational assumptions, and they may deploy new concepts. In this article the last two roles are examined.

This report is concerned with modeling of maneuvering situations as control situations using Talmy's force dynamics. The target domain is large container carriers and the particular aim is to represent the crew's understanding of maneuvering situation at a specific point in time -- so-called situation models. Situation models play a vital role in decision-making process integrating the basic decision-making activities: state identification, goal formation and action planing. On the basis of a concrete scenario concerned with maneuvering operations in letting go the lines of the container carrier M/S Sally Mrsk during a departure from Felixstowe harbor UK, it is shown that the concepts underlying Talmy's force dynamics captures the essential content of the crew's changing situation models. 3 Table of Contents 1 Introduction........................................................................................................... 5 2 Talmy's Force Dynamics ...............................

There seems to be a great potential for using Multilevel Flow Modeling as a framework for reasoning in supervisory control of complex systems. It is a precondition, however, that knowledge about the causal relations between flow functions is represented. Previous attempts have used generic causation rules, specifying possible influences between specific types of flow functions. The problem with this approach is that the generic nature of such rules sometimes leads to invalid reasoning results. This paper presents a method for representing more precisely the actual causal structure of the system being modeled, directly in MFM. The method is based on a set of generic relations, which can be used to make explicit the causal relations hidden in the MFM connection relation. Implications of the method are illustrated by means of simple examples. 1. Introduction In the field of Cognitive Systems Engineering a great deal of research has been concerned with the development of operator support...

This paper focuses on problems related to the analysis of maritime work domains using the abstraction hierarchy proposed by Rasmussen (1986). It is argued that there is need for a causal basis of the means-end relations of the abstraction hierarchy. The main reason for this is that an account of the functions realized by physical objects, denoted as means, depends not only on the intrinsic properties of such objects but also on the properties of causally related objects in their immediate environment. This is particularly pertinent to the maritime domain where the forces produced by control devices (e.g. main propeller, rudder and thruster) are highly dependent on external properties of the water, hull, etc. Through simple causal models of force production of control devices it is illustrated that a means-end view can be understood as a partial causal view. That is, when adopting a means-end view the focus is on a specific individual physical object (having specific causal propensities), leaving the other objects with which the focus-object interacts causally in the background.

The aim of the present thesis is to examine the idea tea ment3 stt3 and consciousness in general arenot144 above and beyond neural processes int he human brain. TotM4 end, prominent positnt fromto modern philosophy oft3 mind are crit723M reviewed. A detailed

Armstrong is one of the few analytic philosophers who have constructed a scientific metaphysics. He is a realist with respect to universals: properties, relations, laws of nature and causal relations are repeatables. The confrontation of a world view presupposing some kind of