This paper replies to the first 36 commentaries on my target article on “Is human information processing conscious?” (Behavioral and Brain Sciences,1991, pp.651-669). The target article focused largely on experimental studies of how consciousness relates to human information processing, tracing their relation from input through to output, while discussion of the implications of the findings both for cognitive psychology and philosophy of mind was relatively brief. The commentaries reversed this emphasis, and so, correspondingly, did the reply. The sequence of topics (...) in the reply roughly follows that of the target article. The discussion begins with a reconsideration of the details of the empirical findings, whether they can be extrapolated to non-laboratory settings, and the extent to which one can rely on their use of subjective reports. This is followed by an in-depth discussion of what is meant by “conscious processing” and of how phenomenal consciousness relates to attentional processing. We then turn to broader philosophical and theoretical issues. I point out some of the reasons why I do not support epiphenomenalism, dualist-interactionism, or reductionism, and elaborate on how first- and third-person views of the mind can be regarded as complementary and mutually irreducible. I suggest how the relation of conscious experiences to their neural correlates can be understood in terms of a dual-aspect theory of information,and how this might be used to resolve some of the paradoxes surrounding the causal interactions of consciousness and brain. I also suggest that, viewed from a first-person perspective, consciousness gives purpose to existence, which allows a different way of viewing its role in evolution. (shrink)

Eliminative functionalism is the view that mental attributes, of humans and other machines, consist ultimately in behavioural abilities or dispositions. Hence, ‘Strong AI’: if a machine consistently acts as if it were fully conscious, then conscious it is. From these assumptions, optimistic futurists have derived a variety of remarkable visions of our ‘post-human’ future; from widely-recognised ‘robot rights’ to ‘mind uploading’, immortality, ‘apotheosis’ and beyond. It is argued here, however, that eliminative functionalism is false; for at least on our present (...) knowledge, the subjectively qualitative characteristics of conscious experience are neither deducible from, nor logically required to generate, the performance of any sort of overtly ‘intelligent’, or indeed, characteristically human behaviour. Thus, a machine could easily be designed to report awareness of phenomenal qualities, without necessarily possessing them; and Alan Turing’s ‘Imitation Game’ test for artificial thinking is unable to determine whether or not a machine is sentient. An alternative test is proposed, in which the machine is asked phenomenological questions under conditions designed to detect any form of cheating—whilst also, potentially revealing evidence for the occurrence of genuine qualitative experience. (shrink)

Investigations of the function of consciousness in human information processing have focused mainly on two questions: (1) where does consciousness enter into the information processing sequence and (2) how does conscious processing differ from preconscious and unconscious processing. Input analysis is thought to be initially "preconscious," "pre-attentive," fast, involuntary, and automatic. This is followed by "conscious," "focal-attentive" analysis which is relatively slow, voluntary, and flexible. It is thought that simple, familiar stimuli can be identified preconsciously, but conscious processing is needed (...) to identify complex, novel stimuli. Conscious processing has also been thought to be necessary for choice, learning and memory, and the organization of complex, novel responses, particularly those requiring planning, reflection, or creativity. (shrink)

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory (...) to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year. (shrink)

Central to contemporary cognitive science is the notion that mental processes involve computations defined over internal representations. This view stands in sharp contrast to the to visual perception and cognition, whose most prominent proponent has been J.J. Gibson. In the direct theory, perception does not involve computations of any sort; it is the result of the direct pickup of available information. The publication of Gibson's recent book (Gibson 1979) offers an opportunity to examine his approach, and, more generally, to contrast (...) the theory of direct perception with the computational/representational view. In the first part of the present article (Sections 2direct perceptioncase study”: the problem of perceiving the three-dimensional shape of moving objects is examined. This problem, which has been extensively studied within the immediate perception framework, serves to illustrate some of the inherent shortcomings of that approach. Finally, in Section 5, an attempt is made to place the theory of direct perception in perspective by embedding it in a more comprehensive framework. (shrink)

This position paper presents the beginnings of a general theory of representations starting from the notion that an intelligent agent is essentially a control system with multiple control states, many of which contain information (both factual and non-factual), albeit not necessarily in a propositional form. The paper attempts to give a general characterisation of the notion of the syntax of an information store, in terms of types of variation the relevant mechanisms can cope with. Similarly concepts of semantics pragmatics and (...) inference are generalised to apply to information-bearing sub-states in control systems. A number of common but incorrect notions about representation are criticised (such as that pictures are in some way isomorphic with what they represent). (shrink)

In this paper, we focus on the development of geometric cognition. We argue that to understand how geometric cognition has been constituted, one must appreciate not only individual cognitive factors, such as phylogenetically ancient and ontogenetically early core cognitive systems, but also the social history of the spread and use of cognitive artifacts. In particular, we show that the development of Greek mathematics, enshrined in Euclid’s Elements, was driven by the use of two tightly intertwined cognitive artifacts: the use of (...) lettered diagrams; and the creation of linguistic formulae. Together, these artifacts formed the professional language of geometry. In this respect, the case of Greek geometry clearly shows that explanations of geometric reasoning have to go beyond the confines of methodological individualism to account for how the distributed practice of artifact use has stabilized over time. This practice, as we suggest, has also contributed heavily to the understanding of what mathematical proof is; classically, it has been assumed that proofs are not merely deductively correct but also remain invariant over various individuals sharing the same cognitive practice. Cognitive artifacts in Greek geometry constrained the repertoire of admissible inferential operations, which made these proofs inter-subjectively testable and compelling. By focusing on the cognitive operations on artifacts, we also stress that mental mechanisms that contribute to these operations are still poorly understood, in contrast to those mechanisms which drive symbolic logical inference. (shrink)