Carruthers considers and rejects a mixed position according to which we have interpretative access to unconscious thoughts, but introspective access to conscious ones. I argue that this is too hasty. Given a two-level view of the mind, we can, and should, accept the mixed position, and we can do so without positing additional introspective mechanisms beyond those Carruthers already recognizes.

By pointing to deep philosophical confusions endemic to cognitive science, Wittgenstein might seem an enemy of computational approaches. We agree that while Wittgenstein would reject the classicist’s symbols and rules approach, his observations align well with connectionist or neural network approaches. While many connectionisms that dominated the later twentieth century could fall prey to criticisms of biological, pedagogical, and linguistic implausibility, current connectionist approaches can resolve those problems in a Wittgenstein-friendly manner. We present the basics of a Vector Symbolic Architecture (...) formalism, inspired by Smolensky, and indicate how high-dimensional vectors can operate in a context-sensitive and object-independent manner in biologically plausible time scales, reflecting Wittgenstein’s notions of language-games and family resemblance; we show how “soft” symbols for such a formalism can be formed with plausible learning cycles using Sparse Distributed Memory, resolving disputes surrounding Wittgenstein’s private language argument; and show how connectionist networks can extrapolate meaningful patterns to solve problems, providing “ways to go on” without explicit rules, which indicates linguistic plausibility. Connectionism thus provides a systematicity and productivity that is more than a mere implementation of a classical approach, and provides Wittgenstein-friendly and Wittgenstein-illuminating models of mind and language for cognitive science. (shrink)

Models of memory in cognitive science and philosophy have traditionally explained human remembering in terms of storage and retrieval. This tendency has been entrenched by reliance on computationalist explanations over the course of the twentieth century; even research programs that eschew computationalism in name, or attempt the revision of traditional models, demonstrate tacit commitment to computationalist assumptions. It is assumed that memory must be stored by means of an isomorphic trace, that memory processes must divide into conceptually distinct systems and (...) phases, and that human remembering consists in inner, cognitive processes that are implemented by distinct neural processes. This dissertation draws on recent empirical work, and on philosophical arguments from Ludwig Wittgenstein and others, to demonstrate that this latent computationalism in the study of memory is problematic, and that it can and should be eliminated. Cognitive psychologists studying memory have encountered numerous data in recent decades that belie archival models. In cognitive neuroscience, establishing the neural basis of storage and retrieval processes has proven elusive. A number of revised models on offer in memory science, that have taken these issues into account, fail to sufficiently extricate the archival framework. Several impasses in memory science are products of these underlying computationalist assumptions. Wittgenstein and other philosophers offer a number of arguments against the need for, and the efficacy of, the storage and retrieval of traces in human remembering. A study of these arguments clarifies the ways that these computationalist assumptions are presently impeding the science of memory, and provides ways forward in removing them. We can and should characterize and model human memory without invoking the storage and retrieval of traces. A range of work in connectionism, dynamical systems theory, and recent philosophical accounts of memory demonstrate how the science of memory can proceed without these assumptions, toward non-archival models of remembering. (shrink)

Alan Turing anticipated many areas of current research incomputer and cognitive science. This article outlines his contributionsto Artificial Intelligence, connectionism, hypercomputation, andArtificial Life, and also describes Turing's pioneering role in thedevelopment of electronic stored-program digital computers. It locatesthe origins of Artificial Intelligence in postwar Britain. It examinesthe intellectual connections between the work of Turing and ofWittgenstein in respect of their views on cognition, on machineintelligence, and on the relation between provability and truth. Wecriticise widespread and influential misunderstandings of theChurch–Turing thesis (...) and of the halting theorem. We also explore theidea of hypercomputation, outlining a number of notional machines thatcompute the uncomputable. (shrink)

A conscious being is characterized by its ability to cope with the environment--to perceive it, sometimes change it, and perhaps reflect on it. Surprisingly, most studies of the mind's place in nature show little interest in such interaction. It is often implicitly assumed that the main questions about consciousness just concern the status of various entities, levels, etc., within the individual. The intertwined notions of " experience" and " consciousness" are considered. The predominant use of these notions in cognitive science (...) can be traced back to Cartesianism. What is important is the survival of the central methodological commitments despite seemingly profound changes of metaphysical outlook. The author argues that cognitive scientists typically assimilate perception to sensation, thereby ignoring ways in which descriptions of perception and descriptions of the environment are logically intertwined; that this involves methodological solipsism and an unacknowledged sceptical position that was originally part of Descartes' Dream argument; and that it is impossible to identify the object supposedly to be studied by a science of the phenomenal consciousness. A somewhat parallel argument is identified in Kant's critique of rationalist psychology. (shrink)

Given (1) Wittgensteins externalist analysis of the distinction between following a rule and behaving in accordance with a rule, (2) prima facie connections between rule-following and psychological capacities, and (3) pragmatic issues about training, it follows that most, even all, future artificially intelligent computers and robots will not use language, possess concepts, or reason. This argument suggests that AIs traditional aim of building machines with minds, exemplified in current work on cognitive robotics, is in need of substantial revision.

Carruthers presents an interesting analysis of confabulation and a clear attack on introspection. Yet his theory-based alternative is a mechanistic view of which neglects the fact that social understanding occurs within a network of social relationships. In particular, the role of language in his model is too simple.