Recent work in the methodology of connectionist explanation has I'ocrrsccl on the notion of levels of explanation. Specific issucs in conncctionisrn hcrc intersect with rvider areas of debate in the philosophy of psychology and thc philosophy of science generally. The issues I raise in this chapter, then, are not unique to cognitive science; but they arise in new and important contexts when connectionism is taken seriously as a model of cognition. The general questions are the relation between levels and the (...) status of levels which have no obvious relation to others. In speaking of levels, what is the connection, if there is one, between explanation and ontology? Which, if any, conccpt of reduction is applicable to connectionist systems? What kind of legitinrtcy can the constructs of common sense psychology, or of that vclsion ol intentional realism represented by classical symbol-systems n I, hirvc irr ir full-scale connectionist theory of mind? (shrink)

Commonsense psychology and cognitive science both regularly assume the existence of representational states. I propose a naturalistic theory of representation sufficient to meet the pretheoretical constraints of a "folk theory of representation", constraints including the capacities for accuracy and inaccuracy, selectivity of proper objects of representation, perspective, articulation, and "efficacy" or content-determined functionality. The proposed model states that a representing device is a device which changes state when information is received over multiple information channels originating at a single source. The (...) changed state of a representing device is a representation. The unitary information source which would give rise to the information impinging on the representing device, and hence, give rise to the representation, is the content of the representation. The model meets the pretheoretic constraints, and also conforms to available neurobiological data for two invertebrate species. (shrink)

Connectionism seems to avoid many of the problems of classical artificial intelligence, but has it avoided all of them? In this thesis I examine the problem that Intentionality, the directedness of thought to an object, raises for connectionism. As a preliminary approach, I consider the role of Intentionality in classical artificial intelligence from the programmer’s point of view. In this investigation, one problem I identify with classical artificial intelligence is that the Intentionality of the programmer seems to be projected onto (...) the system, rather than the programmer creating a system whereby Intentionality arises intrinsically within the system. -/- In considering the current practice of connectionism, the same problem with Intentionality reappears. The assignment of Intentional content to input or output nodes in a neural network likewise projects the Intentionality of the programmer onto the system, and that projection is often reinforced by the training process of the neural network. However, connectionism seems to have an advantage over classical artificial intelligence in this respect, in that there is a form of neural network in which the network itself organizes the output nodes. The challenge is to utilize these self-organizing networks without having the programmer project Intentionality onto the system in an act of interpretation. -/- I suggest that the way to overcome these problems in connectionism is to embody the neural network within a world, whether physical or virtual, and to allow the system to develop concepts purely empirically. In this manner, connectionism can make scientific observations concerning the nature of cognition without the risk of contamination from the programmer’s own Intentionality. (shrink)

This paper traces missing links in the history of economic thought. In outlining Hume's concept of 'the reflexive mind' it shows that this opened frontiers between philosophy and psychology which Bertrand Russell denied and which logical positivism in philosophy and positive economics displaced. It relates this to Hume's influence not only on Smith, but also on Schopenhauer and the later Wittgenstein, with parallels in Gestalt psychology and recent findings from neural research and cognitive psychology. It critiques Kant's reaction to Hume's (...) claim that one may assume but cannot prove cause and effect and how Samuelson's Foundations of Economic Analysis has been Kantian but wrong in claims for axioms that are universal truths. It illustrates how Samuelson's presumption that language and mathematics are 'identical' was as mistaken as the logical atomism of Russell and the early Wittgenstein, relates this to Kleinian splitting, denial and projective identification and suggests that recovery of greater realism in economics needs to regain links with such philosophy and psychology. (shrink)

Connectionism provides hope for unifying work in neuroscience, computer science, and cognitive psychology. This promise has met with some resistance from Classical Computionalists, which may have inspired Connectionists to retaliate with bold, inflationary claims on behalf of Connectionist models. This paper demonstrates, by examining three intimately connected issues, that these inflationary claims made on behalf of Connectionism are wrong. This should not be construed as an attack on Connectionism, however, since the inflated claims made on its behalf have the look (...) of cures for which there are no ailments. There is nothing wrong with Connectionism for its failure to solve illusory problems. (shrink)

In this paper, I reject that animal reasoning, negation in particular, necessarily involves the representation of absences, as suggested by Bermúdez (2003, 2006, 2007), since this would still work as a logical negation (unavailable for non-linguistic creatures). False belief, pretense, and communication experiments show that non-human animals (at least some primates) have difficulties representing absent entities or properties. I offer an alternative account resorting to the sub-symbolic similarity judgments proposed by Vigo & Allen (2009) and expectations: animal proto-negation takes place (...) through the incompatibility between an expected and the actual representation. Finally, I propose that the expectation paradigm be extrapolated to other experi-ments in cognitive psychology (both with pre-linguistic children and animals) in order to design fair experiments that test other minds considering their true abilities. (shrink)

A review of Dianna Coole and Samantha Frost, New Materialisms: Ontology, Agency, and Politics and William E. Connolly, A World of Becoming.

Connectionist research first emerged in the 1940s. The first phase of connectionism attracted a certain amount of media attention, but scant philosophical interest. The phase came to an abrupt halt, due to the efforts of Minsky and Papert (1969), when they argued for the intrinsic limitations of the approach. In the mid-1980s connectionism saw a resurgence. This marked the beginning of the second phase of connectionist research. This phase did attract considerable philosophical attention. It was of philosophical interest, as it (...) offered a way of counteracting the conceptual ties to the philosophical traditions of atomism, rationalism, logic, nativism, rule realism and a concern with the role symbols play in human cognitive functioning, which was prevalent as a consequence of artificial intelligence research. The surge in philosophical interest waned, possibly in part due to the efforts of some traditionalists and the so-called black box problem. Most recently, what may be thought of as a third phase of connectionist research, based on so-called deep learning methods, is beginning to show some signs of again exciting philosophical interest. (shrink)

The empirical study of visual space has centered on determining its geometry, whether it is a perspective projection, flat or curved, Euclidean or non-Euclidean, whereas the topology of space consists of those properties that remain invariant under stretching but not tearing. For that reason distance is a property not preserved in topological space whereas the property of spatial order is preserved. Specifically the topological properties of dimensionality, orientability, continuity, and connectivity define “real” space as studied by physics and are the (...) spatial properties that characterize the physical universe as being an integral whole. By contrast the geometrical analysis of VS has taken little cognizance of its topology. Instead such properties have been presupposed a priori rather than being established a posteriori by empirical means, perhaps because these properties are self-evident. Applying the method of coordinative definition expounded by Hans Reichenbach for determining geometrical and topological properties of physical space, it can be shown that VS fulfills the topological criteria of being a “real” space sui generis. Though theorized to be produced by the brain, the topology of VS is not topologically equivalent with the structure and activity of the brain because, as will be shown, the topology of VS cannot be formed from the topology of the brain without tearing and/or cutting and pasting. (shrink)

Fodor and Pylyshyn (1988), Fodor and McLaughlin (1990) and McLaughlin (1993) challenge connectionists to explain systematicity without simply implementing a classical architecture. In this paper I argue that what makes the challenge difficult for connectionists to meet has less to do with what is to be explained than with what is to count as an explanation. Fodor et al. are prepared to admit as explanatory, accounts of a sort that only classical models can provide. If connectionists are to meet the (...) challenge, they are going to have to insist on the propriety of changing what counts as an explanation of systematicity. Once that is done, there would seem to be as yet no reason to suppose that connectionists are unable to explain systematicity. (shrink)

Neural activity and social activity share close parallels, particularly the fact that spontaneous adaptations are paramount in both realms. Environmental pressures require organisms and societies to adapt to new and uncertain situations. Adaptations create, respectively, stronger neural and social networks that may, in turn, make the system more resilient to future uncertainties—but only if the adaptations are beneficial to the system.