Online research in philosophy

Stanovich & West's target article undervalues the power of implicit learning (particularly reinforcement learning). Implicit learning may allow the learning of more rational responses–and sometimes even generalisation of knowledge–in contexts where explicit, abstract knowledge proves only of limited value, such as for economic decision-making. Four other comments are made.

In this new volume in the Oxford Psychology Series, the author presents a highly readable account of the cognitive unconscious, focusing in particular on the problem of implicit learning. Implicit learning is defined as the acquisition of knowledge that takes place independently of the conscious attempts to learn and largely in the absence of explicit knowledge about what was acquired. One of the core assumptions of this argument is that implicit learning is a fundamental, "root" process, one that lies at the very heart of the adaptive behavioral repertoire of every complex organism. The author's goals are to outline the essential features of implicit learning that have emerged from the many studies that have been carried out in a variety of experimental laboratories over the past several decades; to present the various alternative perspectives on this issue that have been proposed by other researchers and to try to accommodate these views with his own; to structure the literature so that it can be seen in the context of standard heuristics of evolutionary biology; to present the material within a functionalist approach and to try to show why the experimental data should be seen as entailing particular epistemological perspectives; and to present implicit processing as encompassing a general and ubiquitous set of operations that have wide currency and several possible applications. Chapter 1 begins with the core problem under consideration in this book, a characterization of "implicit learning" as it has come to be used in the literature. Reber puts this seemingly specialized topic into a general framework and suggests a theoretical model based on standard heuristics of evolutionary biology. In his account, Reber weaves a capsule history of interest in and work on the cognitive unconscious. Chapter 2 turns to a detailed overview of the experimental work on the acquisition of implicit knowledge, which currently is of great interest. Chapter 3 develops the evolutionary model within which one can see learning and cognition as richly intertwining issues and not as two distinct fields with one dominating the other. Finally, Chapter 4 explores a variety of entailments and speculations concerning implicit cognitive processes and their general role in the larger scope of human performance.

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.

We present a theoretical account of implicit and explicit learning in terms of ACT-R, an integrated architecture of human cognition as a computational supplement to Dienes & Perner's conceptual analysis of knowledge. Explicit learning is explained in ACT-R by the acquisition of new symbolic knowledge, whereas implicit learning amounts to statistically adjusting subsymbolic quantities associated with that knowledge. We discuss the common foundation of a set of models that are able to explain data gathered in several signature paradigms of implicit learning.

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.

Implicit Learning and Consciousness challenges conventional wisdom and presents the most up-to-date studies to define, quantify and test the predictions of the main models of implicit learning. The chapters include a variety of research from computer modeling, experimental psychology and neural imaging to the clinical data resulting from work with amnesics. The result is a topical book that provides an overview of the debate on implicit learning, and the various philosophical, psychological and neurological frameworks in which it can be placed. It will be of interest to undergraduates, postgraduates and the philosophical, psychological and modeling research community.

Dienes & Perner propose a theory of implicit and explicit knowledge that is not entirely complete. It does not address many of the empirical issues, nor does it explain the difference between implicit and explicit learning. It does, however, provide a possible unified explanation, as opposed to the more binary theories like the systems and the processing theories of implicit and explicit memory. Furthermore, it is consistent with a theory in which implicit learning is viewed as based on the mechanisms of the cognitive architecture, and explicit learning as strategies that exploit these mechanisms.

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.