Scientists use a variety of modes of representation in their work, but philosophers have studied mainly sentences expressing propositions. I ask whether diagrams are mere conveniences in expressing propositions or whether they are a distinct, ineliminable mode of representation in scientific texts. The case of path analysis, a statistical method for quantitatively assessing the relative degree of causal determination of variation as expressed in a causal path diagram, is discussed. Path analysis presents a worst case for arguments against eliminability since (...) path diagrams are usually presumed to be mathematically or logically “equivalent” in an important sense to sets of linear path equations. I argue that path diagrams are strongly generative, i.e., that they add analytical power to path analysis beyond what is supplied by linear equations, and therefore that they are ineliminable in a strong scientific sense. (shrink)

We respond to Morris and Richardson 's claim that Pickering and Chater's arguments about the lack of a relation between cognitive science and folk psychology are flawed. We note that possible controversies about the appropriate uses for the two terms do not affect our arguments. We then address their claim that computational explanation of knowledge-rich processes has proved possible in the domains of problem solving, scientific discovery, and reasoning. We argue that, in all cases, computational explanation is only possible for (...) aspects of those processes that do not make reference to general knowledge. We conclude that consideration of the issues raised by Morris and Richardson reinforces our original claim that there are two fundamentally distinct projects for understanding the mind, one based on justification, and the other on computational explanation, and that these apply to non-overlapping aspects of mental life. (shrink)

A number of philosophers have argued that a naturalized epistemology cannot be normative, and thus that the norms that govern science cannot themselves be established empirically. Three arguments for this conclusion are here developed and then responded to on behalf of naturalized epistemology. The response is developed in three stages. First, if we view human knowers as part of the natural world, then the attempt to establish epistemic norms that are immune to scientific evaluation faces difficulties that are at least (...) as severe as the purported difficulties for naturalized epistemology. Second, some examples of the ways in which norms are proposed and evaluated in the course of scientific research are considered and it is concluded that science has no need for trans?scientific norms. Finally, the three arguments introduced at the outset have no force when they are viewed in the light of the preceding discussion. (shrink)

Drawing on the results of modem psychology and cognitive science we suggest that the traditional theory of concepts is no longer tenable, and that the alternative account proposed by Kuhn may now be seen to have independent empirical support quite apart from its success as part of an account of scientific change. We suggest that these mechanisms can also be understood as special cases of general cognitive structures revealed by cognitive science. Against this background, incommensurability is not an insurmountable obstacle (...) to accepting Kuhn's position, as many philosophers of science still believe. Rather it becomes a natural consequence of cognitive structures that appear in all human beings. (shrink)

The Minimalist Program in generative linguistics is predicated on the idea that simplicity is a defining property of the human language faculty, on the one hand; on the other, a central aim of linguistic theorising. Worryingly, however, justifications for either claim are hard to come by in the literature. We sketch a proposal that would allow for both shortcomings to be addressed, and that furthermore honours the program’s declared commitment to naturalism. We begin by teasing apart and clarifying the different (...) conceptions of simplicity underlying generative inquiry, in both ontological and theoretical capacities. We then trace a path towards a more robust justification for each type of simplicity principle, drawing on recent work in cognitive science and in philosophy of science, respectively. The resulting proposal hinges on the idea that simplicity is an evolved, virtuous cognitive bias—one that is a condition of our scientific understanding and, ultimately, of successful scientific practice. Finally, we make a case for why minimalists should take this proposal seriously, on the one hand; and for why generative linguistics would make for an interesting case study for philosophy of science, on the other. (shrink)

In order to develop sophisticated models of the core domains of knowledge that support complex cognitive processing in infants and children, developmental psychologists have mapped out the content of these knowledge domains. This research strategy may provide a blueprint for advancing research on adult cognitive processing. I illustrate this suggestion with examples from analogical reasoning and decision making.

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology – namely, Behaviorism and evolutionary psychology – that set aside mechanistic explanations or make use of optimality assumptions. Through (...) these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements. (shrink)