Online research in philosophy

How do we find out whether someone is conscious of some information or not? A simple answer is “We just ask them”! However, things are not so simple. Here, we review recent developments in the use of subjective and objective methods in implicit learning research and discuss the highly complex methodological problems that their use raises in the domain.

Over the past few years numerous proposals have appeared that attempt to characterize consciousness in terms of what could be called its computational correlates: Principles of information processing with which to characterize the differences between conscious and unconscious processing. Proposed computational correlates include architectural specialization (such as the involvement of specific regions of the brain in conscious processing), properties of representations (such as their stability in time or their strength), and properties of specific processes (such as resonance, synchrony, interactivity, or information integration). In exactly the same way as one can engage in a search for the neural correlates of consciousness, one can thus search for the computational correlates of consciousness. The most direct way of doing is to contrast models of conscious versus unconscious information processing. In this paper, I review these developments and illustrate how computational modeling of specific cognitive processes can be useful in exploring and in formulating putative computational principles through which to capture the differences between conscious and unconscious cognition. What can be gained from such approaches to the problem of consciousness is an understanding of the function it plays in information processing and of the mechanisms that subtend it. Here, I suggest that the central function of consciousness is to make it possible for cognitive agents to exert ?exible, adaptive control over behavior. From this perspective, consciousness is best characterized as involving (1) a graded continuum de?ned over quality of representation, such that availability to consciousness and to cognitive control correlates with properties of representation, and (2) the implication of systems of meta-representations.

Characterizing the relationships between conscious and unconscious processes is one of the most important and long-standing goals of cognitive psychology. Renewed interest in the nature of consciousness — long considered not to be scientifically explorable —, as well as the increasingly widespread availability of functional brain imaging techniques, now offer the possibility of detailed exploration of the neural, behavioral, and computational correlates of conscious and unconscious cognition. This entry reviews some of the relevant experimental work, highlights the methodological challenges involved in establishing the extent to which cognition can occur unconsciously, and situates ongoing debates in the theoretical context provided by current thinking about consciousness.

What do people learn when they do not know that they are learning? Until recently, all of the work in the area of implicit learning focused on empirical questions and methods. In this book, Axel Cleeremans explores unintentional learning from an information-processing perspective. He introduces a theoretical framework that unifies existing data and models on implicit learning, along with a detailed computational model of human performance in sequence-learning situations.

This paper is an attempt to put the work of the past several decades on the problems of implicit learning and unconscious cognition into an evolutionary context. Implicit learning is an inductive process whereby knowledge of a complex environment is acquired and used largely independently of awareness of either the process of acquisition or the nature of that which has been learned. Characterized this way, implicit learning theory can be viewed as an attempt to come to grips with the classic epistemological issues of knowledge acquisition, representation and use. The argument is made that the process, despite its seeming cognitive sophistication, is of considerable evolutionary antiquity and that it antedates awareness and the capacity for conscious control of mentation. Various classic heuristics from evolutionary biology are used to substantiate this claim and several specific entailments of this line of argument are outlined.

Implicit learning – broadly construed as learning without awareness – is a complex, multifaceted phenomenon that defies easy definition. Frensch (1998) listed as many as eleven definitions in an overview, a diversity that is undoubtedly symptomatic of the conceptual and methodological challenges that continue to pervade the field forty years after the term first appeared in the literature (Reber, 1967). According to Berry and Dienes (1993), learning is implicit when an individual acquires new information without intending to do so and in such a way that the resulting knowledge is difficult to express. In this, implicit learning thus contrasts strongly with explicit learning (e.g., as when learning how to solve a problem or learning a concept), which is typically hypothesisdriven and fully conscious. Implicit learning is the process through which one becomes sensitive to certain regularities in the environment: (1) without trying to learn regularities, (2) without knowing that one is learning regularities, and (3) in such a way that the resulting knowledge is unconscious.

Perruchet and Vinter stop short of fully embracing the implications of their own SOC framework, and hence end up defending an implausible perspective on consciousness. We suggest instead that consciousness should be viewed as a graded dimension defined over quality of representation. This graded perspective eliminates the most problematic aspects of the cognitive unconscious without denying its existence altogether.

Perruchet and Vinter stop short of fully embracing the implications of their own SOC framework and hence end up defending an implausible perspective on consciousness. We suggest instead that consciousness should be viewed as a graded dimension defined over quality of representation. This graded perspective eliminates the most problematic aspects of the cognitive unconscious without denying its existence altogether.

While the study of implicit learning is nothing new, the field as a whole has come to embody — over the last decade or so — ongoing questioning about three of the most fundamental debates in the cognitive sciences: The nature of consciousness, the nature of mental representation (in particular the difficult issue of abstraction), and the role of experience in shaping the cognitive system. Our main goal in this chapter is to offer a framework that attempts to integrate current thinking about these three issues in a way that specifically links consciousness with adaptation and learning. Our assumptions about this relationship are rooted in further assumptions about the nature of processing and of representation in cognitive systems. When considered together, we believe that these assumptions offer a new perspective on the relationships between conscious and unconscious processing and on the function of consciousness in cognitive systems.

In this chapter, I sketch a conceptual framework which takes it as a starting point that conscious and unconscious cognition are rooted in the same set of interacting learning mechanisms and representational systems. On this view, the extent to which a representation is conscious depends in a graded manner on properties such as its stability in time or its strength. Crucially, these properties are accrued as a result of learning, which is in turn viewed as a mandatory process that always accompanies information processing. From this perspective, consciousness is best characterized as involving (1) a graded continuum defined over “quality of representation”, such that availability to consciousness and to cognitive control correlates with quality , and (2) the implication of systems of metarepresentations. A first implication of these ideas is that the main function of consciousness is to make flexible, adaptive control over behavior possible. A second, much more speculative implication, is that we learn to be conscious. This I call the “radical plasticity thesis” — the hypothesis that consciousness emerges in systems capable not only of learning about their environment, but also about their own internal representations of it.