Online research in philosophy

It is argued that standard arguments for the Externalism of mental states do not succeed in the case of pre-linguistic mental states. Further, it is noted that standard arguments for Internalism appeal to the principle that our individuation of mental states should be driven by what states are explanatory in our best cognitive science. This principle is used against the Internalist to reject the necessity of narrow individuation of mental states, even in the prelinguistic case. This is done by showing how the explanation of some phenomena requires quantification over broadly-individuated, world-involving states; sometimes externalism is required. Although these illustrative phenomena are not mental, they are enough to show the general argumentative strategy to be incorrect: scientific explanation does not require narrowly-individuated states.

It is widely assumed that common sense psychological explanations of human action are a species of causal explanation. I argue against this construal, drawing on Ramsey et al.'s paper, “Connectionism, eliminativism, and the future of folk psychology”. I argue that if certain connec-tionist models are correct, then mental states cannot be identified with functionally discrete causes of behavior, and I respond to some recent attempts to deny this claim. However, I further contend that our common sense psychological practices are not committed to the falsity of such connectionist models. The paper concludes that common sense psychology is not committed to the identification of mental states with functionally discrete causes of behavior, and hence that common sense psychology is not committed to the causal account of action explanation.

Recent discussions in the philosophy of science have devoted considerable attention to the analysis of conceptual issues relating to the methodology of explanation and prediction in the sciences. Part of this literature has been devoted to clarifying the very ideas of explanation and prediction. But the discussion has also ranged over various related topics, including the status of laws to be used for explanatory and predictive purposes, the logical interrelationships between explanatory and predictive reasonings, the differences in the strategy of explanatory argumentation in different branches of science, the nature and possibility of teleological explanation, etc. The aim of the present article is to examine the issues involved in such questions from the specialized perspective afforded by one particular kind of physical systems--namely, systems, here to be characterized as discrete state systems, whose behavior has been studied extensively in the scientific literature under the general heading of Markov chains. These systems have been chosen as our focus because their behavior over time can be analyzed at once with great ease and with extraordinary precision.

We present a logically detailed case-study of explanation and prediction in Newtonian mechanics. The case in question is that of a planet’s elliptical orbit in the Sun’s gravitational field. Care is taken to distinguish the respective contributions of the mathematics that is being applied, and of the empirical hypotheses that receive a mathematical formulation. This enables one to appreciate how in this case the overall logical structure of scientific explanation and prediction is exactly in accordance with the hypotheticodeductive model.

I offer an account of the relation between explanations of behaviour in terms of psychological states and explanations in terms of neural states that: makes it transparent how they can be true together; explains why explanations in terms of psychological states are characteristically of behaviour described in general and relational terms, and explains why certain sorts of neurological investigations undermine psychological explanations of behaviour, while others leave them intact. In the course of the argument, I offer an account of implicit theories.

The distinction between explanation and prediction has received much attention in recent literature, but the equally important distinction between explanation and description (or between prediction and description) remains blurred. This latter distinction is particularly important in the social sciences, where probabilistic models (or theories) often play dual roles as explanatory and descriptive devices. The distinction between explanation (or prediction) and description is explicated in the present paper in terms of information theory. The explanatory (or predictive) power of a probabilistic model is identified with information taken from (or transmitted by) the environment (e.g., the independent, experimentally manipulated variables), while the descriptive power of a model reflects additional information taken from (or transmitted by) the data. Although information is usually transmitted by the data in the process of estimating parameters, it turns out that the number of free parameters is not a reliable index of transmitted information. Thus, the common practice of treating parameters as degrees-of-freedom in testing probabilistic models is questionable. Finally, this information-theoretic analysis of explanation, prediction, and description suggests ways of resolving some recent controversies surrounding the pragmatic aspects of explanation and the so-called structural identity thesis.

The theory-theory claims that the explanation and prediction of behavior works via the application of a theory, while the simulation theory claims that explanation works by putting ourselves in others' places and noting what we would do. On either account, in order to develop a prediction or explanation of another person's behavior, one first needs to have a characterization of that person's current or recent actions. Simulation requires that I have some grasp of the other person's behavior to project myself upon; whereas theorizing requires a subject matter to theorize about. The frame problem shows that multiple, true characterizations are possible for any behavior or situation. However, only one or a few of these characterizations are relevant to explaining or predicting behavior. Since different characterizations of a behavior lead to different predictions or explanations, much of the work of interpersonal interpretation is done in the process of finding this characterization - that is, prior to either theorizing or simulating. Moreover, finding this characterization involves extensive knowledge of the physical, cultural, and social worlds of the persons involved.

It is almost universally agreed that the main business of commonsense psychology is that of providing generally reliable predictions and explanations of the actions of others. In line with this, it is also generally assumed that we are normally at theoretical remove from others such that we are always ascribing causally efficacious mental states to them for the purpose of prediction, explanation and control. Building on the work of those who regard our primary intersubjective interactions as a form of 'embodied practice', I defend a secondpersonal approach in this paper.

The three cardinal aims of science are prediction, control, and explanation; but the greatest of these is explanation. Also the most inscrutable: prediction aims at truth, and control at happiness, and insofar as we have some independent grasp of these notions, we can evaluate science’s strategies of prediction and control from the outside. Explanation, by contrast, aims at scientific understanding, a good intrinsic to science and therefore something that it seems we can only look to science itself to explicate.

Although prediction has been largely absent from discussions of explanation for the past 40 years, theories of explanation can gain much from a reintroduction. I review the history that divorced prediction from explanation, examine the proliferation of models of explanation that followed, and argue that accounts of explanation have been impoverished by the neglect of prediction. Instead of a revival of the symmetry thesis, I suggest that explanation should be understood as a cognitive tool that assists us in generating new predictions. This view of explanation and prediction clarifies what makes an explanation scientific and why inference to the best explanation makes sense in science. *Received August 2009; revised September 2009. †To contact the author, please write to: Department of Philosophy, University of Tennessee, 801 McClung Tower, Knoxville, TN 37920‐0480; e‐mail: hdouglas@utk.edu.