In this paper I assess the obstacles to a transfer of Lakatos's methodology of scientific research programmes to mathematics. I argue that, if we are to use something akin to this methodology to discuss modern mathematics with its interweaving theoretical development, we shall require a more intricate construction and we shall have to move still further away from seeing mathematical knowledge as a collection of statements. I also examine the notion of rivalry within mathematics and claim that this appears to (...) be significant only at a high level. In addition, ideas of ‘progress’ in mathematics are outlined. (shrink)

Empirical success is a central criterion for scientific decision-making. Yet its understanding in philosophical studies of science deserves renewed attention: Should philosophers think differently about the advancement of science when they deal with the uncertainty of outcome in ongoing research in comparison with historical episodes? This paper argues that normative appeals to empirical success in the evaluation of competing scientific explanations can result in unreliable conclusions, especially when we are looking at the changeability of direction in ongoing investigations. The challenges (...) we encounter arise from the inherent dynamics of disciplinary and experimental objectives in research practice. In this paper we discuss how these dynamics inform the evaluation of empirical success by analyzing three of its requirements: data accommodation, instrumental reliability, and predictive power. We conclude that the assessment of empirical success in developing inquiry is set against the background of a model’s interactive success and prospective value in an experimental context. Our argument is exemplified by the analysis of an apparent controversy surrounding the model of a quantum nose in research on olfaction. Notably the public narrative of this controversy rests on a distorted perspective on measures of empirical success. (shrink)

This paper is based on a criterion recently proposed by Richard Fumerton for demarcating philosophy of mind and cognitive science. I suggest to extend it to a demarcation criterion between philosophy and science in general, and put it in the context of the historical changes of boundaries between the philosophical and the scientifi c fi eld. I point to a number of philosophical claims and approaches that have been made utterly obsolete by the advancement of science, and conjecture that a (...) similar thing may happen in the future with today’s philosophy of mind: under the supposition that cognitive science manages to progress very successfully in a certain direction, our concepts for mental states could change, and the type of philosophical interest we put in them, thus reshaping thewhole debate on the subject. (shrink)

Cosmological speculation about the ultimate nature of the universe, being necessary for science to be possible at all, must be regarded as a part of scientific knowledge itself, however epistemologically unsound it may be in other respects. The best such speculation available is that the universe is comprehensible in some way or other and, more specifically, in the light of the immense apparent success of modern natural science, that it is physically comprehensible. But both these speculations may be false; in (...) order to take this possibility into account, we need to adopt an hierarchy of increasingly contentless cosmological conjectures until we arrive at the conjecture that the universe is such that it is possible for us to acquire some knowledge of something, a conjecture which we are justified in accepting as knowledge since doing so cannot harm the pursuit of knowledge in any circumstances whatsoever. As a result of adopting such an hierarchy of increasingly contentless cosmological conjectures in this way, we maximize our chances of adopting conjectures that promote the growth of knowledge, and minimize our chances of taking some cosmological assumption for granted that is false and impedes the growth of knowledge. The hope is that as we increase our knowledge about the world we improve (lower level) cosmological assumptions implicit in our methods, and thus in turn improve our methods. As a result of improving our knowledge we improve our knowledge about how to improve knowledge. Science adapts its own nature to what it learns about the nature of the universe, thus increasing its capacity to make progress in knowledge about the world. This aim-oriented empiricist conception of science solves outstanding problems in the philosophy of science such as the problems of induction, simplicity and verisimilitude. (shrink)

What are the relevant values to the appraisal of research programs? This question remains hotly debated, as philosophers have recently proposed many lists of values potentially relevant to scientific appraisal. Surprisingly, despite being mentioned in many lists, little attention has been paid to fruitfulness. It is unclear how fruitfulness should be explicated, and whether it has any substantial role in scientific appraisal. In this paper, I argue we should explicate fruitfulness as the capacity to develop of research programs. Moreover, I (...) provide a novel strategy to assess and compare the fruitfulness of programs focused on their research questions and heuristics. To illustrate how this strategy would work, I will discuss a case study, namely the adaptationist program in evolutionary psychology. (shrink)

Classical symbolic computational models of cognition are at variance with the empirical findings in the cognitive psychology of memory and inference. Standard symbolic computers are well suited to remembering arbitrary lists of symbols and performing logical inferences. In contrast, human performance on such tasks is extremely limited. Standard models donot easily capture content addressable memory or context sensitive defeasible inference, which are natural and effortless for people. We argue that Connectionism provides a more natural framework in which to model this (...) behaviour. In addition to capturing the gross human performance profile, Connectionist systems seem well suited to accounting for the systematic patterns of errors observed in the human data. We take these arguments to counter Fodor and Pylyshyn's (1988) recent claim that Connectionism is, in principle, irrelevant to psychology. (shrink)