In their article entitled “Ethical Particularism and Patterns”, Frank Jackson, Philip Pettit, and Michael Smith (JPS henceforth) argue that moral particularism is a cognitively implausible theory since it appears to entail the view that one might have a skill that is not grounded in an ability to recognise and represent natural patterns in the world. This charge echoes the complaints of computational theorists of cognition against their embodied cognition counterparts, namely that, theories of cognition that eschew talk of mental representation (...) are implausible qua theories of cognition. In both debates, the cognitive role of generalisation is central to the discussion; however, contrary to the received wisdom, I want to suggest that the dispute is not between generalisation or mental representation on the one hand and no generalisation or mental representation on the other, but rather between what I will call global and local generalisation. Using the dialogue between JPS and Dancy (our paradigm particularist) to frame this discussion, I show that by replacing Dancy's connectionist model for particularist reasoning with a case-based one, we not only vindicate his response to JPS, but we also gain insight into how it is the global/local distinction rather than the generalisation/no generalisation distinction that divides the two views. (shrink)

In this paper I explore the question of how artificial life might be used to get a handle on philosophical issues concerning the mind-body problem. I focus on questions concerning what the physical precursors were to the earliest evolved versions of intelligent life. I discuss how cellular automata might constitute an experimental platform for the exploration of such issues, since cellular automata offer a unified framework for the modeling of physical, biological, and psychological processes. I discuss what it would take (...) to implement in a cellular automaton the evolutionary emergence of cognition from non-cognitive artificial organisms. I review work on the artificial evolution of minimally cognitive organisms and discuss how such projects might be translated into cellular automata simulations. (shrink)

I show that gene regulation networks are qualitatively consistent and therefore sufficiently similar to linearly seperable connectionist networks to warrant that the connectionist framework be applied to gene regulation. On this view, natural selection designs gene regulation networks to overcome the difficulty of development. I offer some general lessons about their evolvability that can be learned by examining the generic features of connectionist networks.

A similarity space is a hyperspace in which the dimensions represent various dimensions on which objects may differ. The similarity space theory of concepts is the thesis that concepts are regions of similarity spaces that are somehow realized in the brain. Proponents of such a theory of concepts include Paul Churchland and Peter Gärdenfors. This paper argues that the similarity space theory of concepts is mistaken because regions of similarity spaces cannot serve as the components of judgments. It emerges that (...) although similarity spaces cannot model concepts, they may model a kind of nonconceptual representation. (shrink)

This paper advocates explicitness about the type of entity to be considered as content- bearing in connectionist systems; it makes a positive proposal about how vehicles of content should be individuated; and it deploys that proposal to argue in favour of representation in connectionist systems. The proposal is that the vehicles of content in some connectionist systems are clusters in the state space of a hidden layer. Attributing content to such vehicles is required to vindicate the standard explanation for some (...) classificatory networks’ ability to generalise to novel samples their correct classification of the samples on which they were trained. (shrink)

Sometimes your concept and mine have exactly the same content. When this is so, it is comparatively easy for me to understand what you say when you deploy your concept, for us to disagree, agree, and so on. But what if your concept and mine do not have exactly the same content? This question has occupied a number of philosophers, including Paul Churchland, Jerry Fodor, and Ernie Lepore. This paper develops a novel and rigorous measure of concept similarity, Proportion, such (...) that concepts with different contents but sufficiently high Proportion scores will also conduce to understanding, agreement, and disagreement. (shrink)

The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically these choices threaten to bias conclusions about phenomena drawn from data. Contact between the practices of multivariate pattern analysis (...) and philosophy of science can help to illuminate the conditions under which we can use artificial neural networks to better understand neural mechanisms. This paper considers a specific technique for MVPA called representational similarity analysis. I develop a theoretically-informed account of RSA that draws on early connectionist research and work on idealization in the philosophy of science. By bringing a philosophical account of cognitive modelling in conversation with RSA, this paper clarifies the practices of neuroscientists and provides a generalizable framework for using artificial neural networks to study neural mechanisms in the brain. (shrink)

Inferential Role Semantics is often criticized for being incompatible with the platitude that words of different speakers can mean the same thing. While many assume that this platitude can be accommodated by understanding sameness of meaning in terms of similarity of meaning, no worked out proposal has ever been produced for Inferential Role Semantics. I rectify this important omission by giving a detailed structural account of meaning similarity in terms of graph theory. I go on to argue that this account (...) has a number of attractive features, prominent among them that it makes sameness of meaning probabilistically determine co-reference. (shrink)

In this paper I will present three reservations about the claims made by Hutto and Satnet. First of all, though TNOC is presented as drawing on teleological theories of mental content for a conception of Ur-Intentionaltiy, what is separated out after objectionable claims are removed from teleological accounts may not retain enough to give us directed intelligence. This problem raises a question about what we need in a naturalistic basis for an account of the mental. Secondly, I think that the (...) authors’ objections to cognitive science’s supposed notion of a mental representation in fact attack a philosophers’ invention, one that sprung into vibrant being in the mid-1970’s. It is not employed in cognitive neuroscience to any significant extent and it isn’t needed by other disciplines involved in cognitive science. Finally, I worry that the account of the origin of content – namely, explanatory narrative - presents us with a too limited view of human discourse, particularly as it is needed for a social theory of mind. (shrink)

Fodor’s radical concept nativism flowed from his view that hypothesis testing is the only route to concept acquisition. Many have successfully objected to the overly-narrow restriction to learning by hypothesis testing. Existing representations can be connected to a new representational vehicle so as to constitute a sustaining mechanism for a new representation, without the new representation thereby being constituted by or structured out of the old. This paper argues that there is also a deeper objection. Connectionism shows that a more (...) fundamental assumption underpinning the debate can also be rejected: the assumption that the development of a new representation must be explained in content-involving terms if innateness is to be avoided. -/- Fodor has argued that connectionism offers no new resources to explain concept acquisition: unless it is merely an uninteresting claim about neural implementation, connectionism’s defining commitment to distributed representations reduces to the claim that some representations are structured out of others (which is the old, problematic research programme). Examination of examples of representational development in connectionist networks shows, however, that some such models explain the development of new representational capacities in non-representational terms. They illustrate the possibility of representational development that is not explicable-by-content. Connectionist representations can be distributed in an important sense, which is incompatible with the assumption of explanation-by-content: they can be distributed over non-representational resources that account for their development. Rejecting the assumption of explanation-by-content thereby opens up a more radical way of rejecting Fodor’s argument for radical concept nativism. (shrink)

Jerry Fodor and Ernest Lepore have launched a powerful attack against Paul Churchland's connectionist theory of semantics—aka State Space Semantics. In one part of their overall attack, they exploit the potentially orthogonal histories of different individuals to introduce what they labeled ‘the collateral information problem’. Aarre Laakso and Gary Cottrell have recently put forward a mathematical technique for measuring conceptual similarity across neural networks. Churchland uses Laakso and Cottrell's tecnique to defend State Space Semantics. In this paper I shall highlight (...) a potential problem for Laakso and Cottrell's technique, and for Churchland's subsequent defence of connectionist semantics that has been ignored in the connectionist literature. I shall argue that a connectionist sympathiser of Churchland could not make use of Laakso and Cottrell's neurosimulations to address Fodor and Lepore's collateral information challenge. (shrink)

This paper deals with the problem of understanding semiosis and meaning in cognitive systems. To this aim we argue for a unified two-factor account according to which both external and internal information are non-independent aspects of meaning, thus contributing as a whole in determining its nature. To overcome the difficulties stemming from this approach we put forward a theoretical scheme based on the definition of a suitable representation space endowed with a set of transformations, and we show how it can (...) be implemented, in the case of a single agent, by a neural network architecture. Numerical experiments conducted on different instances of the latter show that similar representations are developed as a consequence of the fact that these instances are facing a similar semantic task. This allows to model social and environmental influences through a system of interacting agents, each described by a specific implementation of this model architecture. (shrink)

Jerry Fodor and Ernest Lepore [(1992) Holism: a shopper's guide, Oxford: Blackwell; (1996) in R. McCauley (Ed.) The Churchlands and their critics , Cambridge: Blackwell] have launched a powerful attack against Paul Churchland's connectionist theory of semantics--also known as state space semantics. In one part of their attack, Fodor and Lepore argue that the architectural and functional idiosyncrasies of connectionist networks preclude us from articulating a notion of conceptual similarity applicable to state space semantics. Aarre Laakso and Gary Cottrell [(1998) (...) in M. A. Gernsbacher & S. Derry (Eds) Proceedings of the 20th Annual Conference of the Cognitive Science Society, Mahway, NJ: Erlbaum; Philosophical Psychology ] 13, 47-76 have recently run a number of simulations on simple feedforward networks and applied a mathematical technique for measuring conceptual similarity in the representational spaces of those networks. Laakso and Cottrell contend that their results decisively refute Fodor and Lepore's criticisms. Paul Churchland [(1998) Journal of Philosophy, 95, 5-32 ] goes further. He uses Laakso and Cottrell's neurosimulations to argue that connectionism does furnish us with all we need to construct a robust theory of semantics and a robust theory of translation. In this paper I shall argue that whereas Laakso and Cottrell's neurocomputational results may provide us with a rebuttal of Fodor and Lepore's argument, Churchland's conclusion is far too optimistic. In particular, I shall try to show that connectionist modelling does not provide any objective criterion for achieving a one-to-one accurate translational mapping across networks. (shrink)