Online research in philosophy

The mind's arrows , by Clark Glymour, combines several of the author's previous essays on causal inference. Glymour deploys causal Bayes nets (CBNs) to provide a descriptive psychological model of human causal inference and a prescriptive model for making inferences in cognitive neuropsychology and the social sciences. Though The mind's arrows is highly original and provocative, its labyrinthine organization and technical style render it inaccessible to the uninitiated. Here we attempt to distill, package and dress some of Glymour's more interesting theses. We note that the psychological model is developed with minimal attention to evidence concerning human causal inference and that his prescriptive models fail to do justice either to the many sources of evidence in cognitive neuropsychology or to the serious challenges of making causal inferences in the social sciences. Considerable work remains to be done to complete Glymour's ambitious projects and to clearly communicate them to others.

In the last several decades, a confluence of work in the social sciences, philosophy, statistics, and computer science has developed a theory of causal inference using directed graphs. This theory typically rests either explicitly or implicitly on two major assumptions.

My project in this paper is to extend the interventionist analysis of causation to give an account of causation in psychology. Many aspects of empirical investigation into psychological causation fit straightforwardly into the interventionist framework. I address three problems. First, the problem of explaining what it is for a causal relation to be properly psychological rather than merely biological. Second, the problem of rational causation: how it is that reasons can be causes. Finally, I look at the implications of an interventionist analysis for the idea that an inquiry into psychological causes must be an inquiry into causal mechanisms. I begin by setting out the main ideas of the interventionist approach.

Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism.

Philosophically inclined psychologists and psychologically inclined philosophers often hold that the substantive discoveries of psychology can provide an empirical foundation for epistemology. In this paper it is argued that the ambition to found epistemology empirically faces certain unnoticed difficulties. Empirical theories concerned with knowledge?gaining abilities have been historically associated with specific epistemological views such that the epistemology gives preferential support to the substantive theory, while the theory empirically supports the epistemology. Theories attribute to the subject just those epistemic abilities which associated epistemologies attribute to the scientist. The concept of epistemological significance is introduced as the significance a psychological theory can have for modifying the epistemological suppositions with which the theory was originally associated. Substantive psychological theories are strongly constrained by the epistemologies used in their development; the endorsement an epistemology receives from its associated theory should carry no weight. The alliance between psychology and epistemology is not progressive to the development of either field. Alternative sources of progress in epistemology and psychology are suggested.

I analyze the frame problem and its relation to other epistemological problems for artificial intelligence, such as the problem of induction, the qualification problem and the "general" AI problem. I dispute the claim that extensions to logic (default logic and circumscriptive logic) will ever offer a viable way out of the problem. In the discussion it will become clear that the original frame problem is really a fairy tale: as originally presented, and as tools for its solution are circumscribed by Pat Hayes, the problem is entertaining, but incapable of resolution. The solution to the frame problem becomes available, and even apparent, when we remove artificial restrictions on its treatment and understand the interrelation between the frame problem and the many other problems for artificial epistemology. I present the solution to the frame problem: an adequate theory and method for the machine induction of causal structure. Whereas this solution is clearly satisfactory in principle, and in practice real progress has been made in recent years in its application, its ultimate implementation is in prospect only for future generations of AI researchers.

The frame problem is the problem of how we selectively apply relevant knowledge to particular situations in order to generate practical solutions. Some philosophers have thought that the frame problem can be used to rule out, or argue in favor of, a particular theory of belief states. But this is a mistake. Sentential theories of belief are no better or worse off with respect to the frame problem than are alternative theories of belief, most notably, the “map” theory of belief.

Newsome ((2003). The debate between current versions of covariation and mechanism approaches to causal inference. Philosophical Psychology, 16, 87-107.) recently published a critical review of psychological theories of human causal inference. In that review, he characterized covariation and mechanism theories, the two dominant theory types, as competing, and offered possible ways to integrate them. I argue that Newsome has misunderstood the theoretical landscape, and that covariation and mechanism theories do not directly conflict. Rather, they rely on distinct sets of reliable indicators of causation, and focus on different types of causation (type vs. token). There are certainly debates in the research field, but the theoretical landscape is not as fractured as Newsome suggests, and a potential unifying framework has already emerged using causal Bayes nets. Philosophical work on causal epistemology matters for psychologists, but not in the way Newsome suggests.

Words, Thoughts and Theories argues that infants and children discover the physical and psychological features of the world by a process akin to scientific inquiry, more or less as conceived by philosophers of science in the 1960s (the theory theory). This essay discusses some of the philosophical background to an alternative, more popular, “modular” or “maturational” account of development, dismisses an array of philosophical objections to the theory theory, suggests that the theory theory offers an undeveloped project for artificial intelligence, and, relying on recent psychological work on causation, offers suggestions about how principles of causal inference may provide a developmental solution to the “frame problem”.

Words, Thoughts and Theories arguesthat infants and children discover the physical and psychological featuresof the world by a process akin to scientific inquiry, more or less asconceived by philosophers of science in the 1960s (the theory theory).This essay discusses some of the philosophical background to analternative, more popular, ``modular'''' or ``maturational'''' account ofdevelopment, dismisses an array of philosophical objections to the theorytheory, suggests that the theory theory offers an undeveloped project forartificial intelligence, and, relying on recent psychological work oncausation, offers suggestions about how principles of causal inference mayprovide a developmental solution to the ``frame problem''''.