I examine the central theoretical construct of ecological psychology, the concept of an affordance. In the first part of the paper, I illustrate the role affordances play in Gibson's theory of perception. In the second part, I argue that affordances are to be understood as dispositional properties, and explain what I take to be their characteristic background circumstances, triggering circumstances and manifestations. The main purpose of my analysis is to give affordances a theoretical identity enriched by Gibson's visionary insight, but independent of the most controversial claims of the Gibsonian movement.

Scientists and laypeople alike use the sense of understanding that an explanation conveys as a cue to good or correct explanation. Although the occurrence of this sense or feeling of understanding is neither necessary nor sufficient for good explanation, it does drive judgments of the plausibility and, ultimately, the acceptability, of an explanation. This paper presents evidence that the sense of understanding is in part the routine consequence of two well-documented biases in cognitive psychology: overconfidence and hindsight. In light of the prevalence of counterfeit understanding in the history of science, I argue that many forms of cognitive achievement do not involve a sense of understanding, and that only the truth or accuracy of an explanation make the sense of understanding a valid cue to genuine understanding.

Evidence that led to the hypothesis of a backwards referral of conscious sensory experiences in time, and the experimental tests of its predictions, is summarized. Criticisms of the data and the conclusion by Churchland that this hypothesis is untenable are analysed and found to be based upon misconceptions and faulty evaluations of facts and theory. Subjective referral in time violates no neurophysiological principles or data and is compatible with the theory of “mental” and “physical” correspondence.

This paper presents a drastically revised version of the theory of causality, based on analyses of causal processes and causal interactions, advocated in Salmon (1984). Relying heavily on modified versions of proposals by P. Dowe, this article answers penetrating objections by Dowe and P. Kitcher to the earlier theory. It shows how the new theory circumvents a host of difficulties that have been raised in the literature. The result is, I hope, a more satisfactory analysis of physical causality.

This paper examines Wesley Salmon's “process” theory of causality, arguing in particular that there are four areas of inadequacy. These are that the theory is circular, that it is too vague at a crucial point, that statistical forks do not serve their intended purpose, and that Salmon has not adequately demonstrated that the theory avoids Hume's strictures about “hidden powers”. A new theory is suggested, based on “conserved quantities”, which fulfills Salmon's broad objectives, and which avoids the problems discussed.

Churchland's paper “Perceptual Plasticity and Theoretical Neutrality“ offers empirical, semantical and epistemological arguments intended to show that the cognitive impenetrability of perception “does not establish a theory-neutral foundation for knowledge” and that the psychological account of perceptual encapsulation that I set forth in The Modularity of Mind “[is] almost certainly false“. The present paper considers these arguments in detail and dismisses them.

Reductionism is often understood to include two theses: (1) every singular occurrence that the special sciences can explain also can be explained by physics; (2) every law in a higher-level science can be explained by physics. These claims are widely supposed to have been refuted by the multiple realizability argument, formulated by Putnam (1967, 1975) and Fodor (1968, 1975). The present paper criticizes the argument and identifies a reductionistic thesis that follows from one of the argument's premises.

It is not the aim of the present essay to defend the principle of empiricism against apriorism or anti-empiricist metaphysics. Taking empirism for granted, we wish to discuss, the question what is meaningful. The word ‘meaning’ will here be taken in its empiricist sense; an expression of language has meaning in this sense if we know how to use it in speaking about empirical facts, either actual or possible ones. Now our problem is what expressions are meaningful in this sense. We may restrict this question to sentences because expressions other than sentences are meaningful if and only if they can occur in a meaningful sentence.

Charles S. Peirce argued that, besides deduction and induction, there is a third mode of inference which he called “hypothesis” or “abduction.” He characterized abduction as reasoning “from effect to cause,” and as “the operation of adopting an explanatory hypothesis.” Peirce's ideas about abduction, which are related also to historically earlier accounts of heuristic reasoning (the method of analysis), have been seen as providing a logic of scientific discovery. Alternatively, abduction is interpreted as giving reasons for pursuing a hypothesis. Inference to the best explanation (IBE) has also been regarded as an important mode of justification, both in everyday life, detective stories, and science. In particular, scientific realism has been defended by an abductive nomiracle argument (Smart, Putnam, Boyd), while the critics of realism have attempted to show that this appeal to abduction is question-begging, circular, or incoherent (Fine, Laudan, van Fraassen). This paper approaches these issues by distinguishing weaker and stronger forms of abduction, and by showing how these types of inferences can be given Peircean and Bayesian probabilistic reconstructions.

After clarifying the probabilistic conception of causality suggested by Good (1961–2), Suppes (1970), Cartwright (1979), and Skyrms (1980), we prove a sufficient condition for transitivity of causal chains. The bearing of these considerations on the units of selection problem in evolutionary theory and on the Newcomb paradox in decision theory is then discussed.

This article argues that the basic account of mechanism and mechanistic explanation, involving sequential execution of qualitatively characterized operations, is itself insufficient to explain biological phenomena such as the capacity of living organisms to maintain themselves as systems distinct from their environment. This capacity depends on cyclic organization, including positive and negative feedback loops, which can generate complex dynamics. Understanding cyclically organized mechanisms with complex dynamics requires coordinating research directed at decomposing mechanisms into parts (entities) and operations (activities) with research using computational models to recompose mechanisms and determine their dynamic behavior. This coordinated endeavor yields dynamic mechanistic explanations.

This essay grows out of the conviction that recent work by psychologists studying human reasoning has important implications for a broad range of philosophical issues. To illustrate our thesis we focus on Nelson Goodman's elegant and influential attempt to “dissolve” the problem of induction. In the first section of the paper we sketch Goodman's account of what it is for a rule of inference to be justified. We then marshal empirical evidence indicating that, on Goodman's account of justification, patently invalid inferential rules turn out to be “justified.” We conclude that something is seriously wrong with Goodman's story about justification. In the second section we attempt to patch Goodman's account. The notion of epistemic authority and the social aspect of justification play central roles in the alternative account of justification that we propose.

The semantic concept of information is one of the most important, and one of the most problematical concepts in biology. I suggest a broad definition of biological information: a source becomes an informational input when an interpreting receiver can react to the form of the source (and variations in this form) in a functional manner. The definition accommodates information stemming from environmental cues as well as from evolved signals, and calls for a comparison between information-transmission in different types of inheritance systems—the genetic, the epigenetic, the behavioral, and the cultural-symbolic. This comparative perspective highlights the different ways in which information is acquired and transmitted, and the role that such information plays in heredity and evolution. Focusing on the special properties of the transfer of information, which are very different from those associated with the transfer of materials or energy, also helps to uncover interesting evolutionary effects and suggests better explanations for some aspects of the evolution of communication.

We currently have on offer a variety of different theories of causation. Many are strikingly good, providing detailed and plausible treatments of exemplary cases; and all suffer from clear counterexamples. I argue that, contra Hume and Kant, this is because causation is not a single, monolithic concept. There are different kinds of causal relations imbedded in different kinds of systems, readily described using thick causal concepts. Our causal theories pick out important and useful structures that fit some familiar cases—cases we discover and ones we devise to fit.

According to an adequacy-for-purpose view, models should be assessed with respect to their adequacy or fitness for particular purposes. Such a view has been advocated by scientists and philosophers alike. Important details, however, have yet to be spelled out. This article attempts to make progress by addressing three key questions: What does it mean for a model to be adequate-for-purpose? What makes a model adequate-for-purpose? How does assessing a model’s adequacy-for-purpose differ from assessing its representational accuracy? In addition, responses are given to some objections that might be raised against an adequacy-for-purpose view.

Epistemologists and philosophers of science have often attempted to express formally the impact of a piece of evidence on the credibility of a hypothesis. In this paper we will focus on the Bayesian approach to evidential support. We will propose a new formal treatment of the notion of degree of confirmation and we will argue that it overcomes some limitations of the currently available approaches on two grounds: (i) a theoretical analysis of the confirmation relation seen as an extension of logical deduction and (ii) an empirical comparison of competing measures in an experimental inquiry concerning inductive reasoning in a probabilistic setting.

The article defends the doctrine that Linnaean taxa, including species, have essences that are, at least partly, underlying intrinsic, mostly genetic, properties. The consensus among philosophers of biology is that such essentialism is deeply wrong, indeed incompatible with Darwinism. I argue that biological generalizations about the morphology, physiology, and behavior of species require structural explanations that must advert to these essential properties. The objection that, according to current “species concepts,” species are relational is rejected. These concepts are primarily concerned with what it is for a kind to be a species and throw little light on the essentialist issue of what it is for an organism to be a member of a particular kind. Finally, the article argues that this essentialism can accommodate features of Darwinism associated with variation and change.

Biological knowledge does not fit the image of science that philosophers have developed. Many argue that biology has no laws. Here I criticize standard normative accounts of law and defend an alternative, pragmatic approach. I argue that a multidimensional conceptual framework should replace the standard dichotomous law/accident distinction in order to display important differences in the kinds of causal structure found in nature and the corresponding scientific representations of those structures. To this end I explore the dimensions of stability, strength, and degree of abstraction that characterize the variety of scientific knowledge claims found in biology and other sciences.

It has been the dominant view that probabilistic explanations of particular facts must be inductive in character. I argue here that this view is mistaken, and that the aim of probabilistic explanation is not to demonstrate that the explanandum fact was nomically expectable, but to give an account of the chance mechanism(s) responsible for it. To this end, a deductive-nomological model of probabilistic explanation is developed and defended. Such a model has application only when the probabilities occurring in covering laws can be interpreted as measures of objective chance, expressing the strength of physical propensities. Unlike inductive models of probabilistic explanation, this deductive model stands in no need of troublesome requirements of maximal specificity or epistemic relativization.