Evan Fales

Some philosophers deny that science can investigate the supernatural - specifically, the nature and actions of God. If a divine being is atemporal, then, indeed, this seems plausible - but only, I shall argue, because such a being could not causally interact with anything. Here I discuss in detail two major attempts, those of Stump and Kretzmann, and of Leftow, to make sense of theophysical causation on the supposition that God is eternal. These views are carefully worked out, and their failures are instructive for any attempt to reconcileeternality with causal efficacy. I conclude by arguing that if knowledge of God is possible, in virtue of His effects upon the world, then it is science that must play the preeminent role in producing that knowledge.

In Part I of this paper, I took up a challenge posed by Alston , Wainwright , Yandell , and other theists who hold the rather natural view that mystical experiences provide perceptual contact with God, roughly on a par with the access sense experience affords to the natural world. These theists recognize, at the same time, that the plausibility of this view would be significantly compromised by the possibility of scientifically explaining mystical experiences – especially if a scientific explanation were incompatible with, ruled out, or made unlikely the supposition that God has anything special to do with the occurrences of these experiences

Theories of how organisms learn about cause-effect relations have a history dating back at least to the associationist/mechanistic hypothesis of David Hume. Some contemporary theories of causal learning are descendants of Hume's mechanistic models of conditioning, but others impute principled, rule-based reasoning. Since even primitive animals are conditionable, it is clear that there are built-in mechanical algorithms that respond to cause/effect relations. The evidence suggests that humans retain the use of such algorithms, which are surely adaptive when causal judgments must be rapidly made. But we know very little about what these algorithms are and about when and with what ratiocinative procedures they are sometimes replaced. Nor do we know how the concept of causation originates in humans. To clarify some of these issues, this paper surveys the literature and explores the behavioral predictions made by two contrasting theories of causal learning: the mechanical Rescorla-Wagner model and the sophisticated reasoning codified in Bayes' Theorem

Donnellan's distinction between the referential and attributive uses of definite descriptions is shown not to cover exhaustive and exclusive alternatives but to fix the termini of a continuum of cases. in fact, donnellan's distinction rests on a mixed classification: the referential use, concerned with intended referents regardless of what speakers may say about them; the attributive use, concerned with definite descriptions used in using sentences, that something or other may satisfy. given this feature of his account, it is easy to construct counterexamples of a hybrid nature. related views (stalnaker's, kaplan's, stampe's) are briefly canvassed