In this article, Arnon explores the link between implicit learning, statistical learning and language development. She focuses on two central themes, namely the issue of age invariance and the question of variation in learning outcomes. Arnon suggests that the two literatures are studying a fundamentally similar phenomenon and argues in favor of a closer alignment. However, she also raises important methodological concerns.

The concept of tacit knowledge is widely used in social sciences to refer to all those knowledge that cannot be codified and have to be transferred by personal contacts. All this literature has been affected by two kind of biases : (1) the interest has been focused more on the result (tacit knowledge) than on the process (implicit learning); (2) tacit knowledge has been somehow reduced to physical skills or know-how; other possible forms of tacit knowledge have been neglected. These (...) two biases seem interconnected one with each other. A greater consideration of the role and relevance of implicit learning allows us to consider tacit knowledge as something more than pure physical skills or know how. This is the first step in order to develop more detailed categorisation of the different forms that tacit knowledge can assume. (shrink)

This paper tries to express a critical point of view on the computational turn in philosophy by looking at a specific field of study: philosophy of science. The paper starts by briefly discussing the main contributions that information and communication technologies have given to the rising of computational philosophy of science, and in particular to the cognitive modelling approach. The main question then arises, concerning how computational models can cope with the presence of tacit knowledge in science. Would it be (...) possible to develop new ways of handling this specific type of knowledge, in order to incorporate it in computational models of scientific thinking? Or should tacit knowledge lead us to other approaches in using computer sciences to model scientific cognition? These questions are addressed by making reference to a detailed case study of a recent innovation development in the field of biotechnology. (shrink)

Explicit and implicit learning have been attributed to different learning processes that create different types of knowledge structures. Consistent with that claim, our study provides evidence that people integrate stimulus events differently when consciously aware versus unaware of the relationship between the events. In a first, acquisition phase participants sorted words into two categories , which were fully predicted by task-irrelevant primes—the labels of two other, semantically unrelated categories . In a second, test phase participants performed a lexical decision task, (...) in which all word stimuli stemmed from the previous prime categories and the primes were the labels of the previous target categories . Reliable priming effects in the second phase demonstrated that bidirectional associations between the respective categories had been formed in the acquisition phase , but these effects were found only in participants that were unaware of the relationship between the categories! We suggest that unconscious, implicit learning of event relationships results in the rather unsophisticated integration of the underlying event representations, whereas explicit learning takes the meaning of the order of the events into account, and thus creates unidirectional associations. (shrink)

A basic function of cognition is to detect regularities in sensory input to facilitate the prediction and recognition of future events. It has been proposed that these implicit expectations arise from an internal predictive coding model, based on knowledge acquired through processes such as statistical learning, but it is unclear how different types of statistical information affect listeners’ memory for auditory stimuli. We used a combination of behavioral and computational methods to investigate memory for non-linguistic auditory sequences. Participants repeatedly heard (...) tone sequences varying systematically in their information-theoretic properties. Expectedness ratings of tones were collected during three listening sessions, and a recognition memory test was given after each session. Information-theoretic measures of sequential predictability significantly influenced listeners’ expectedness ratings, and variations in these properties had a significant impact on memory performance. Predictable sequences yielded increasingly better memory performance with increasing exposure. Computational simulations using a probabilistic model of auditory expectation suggest that listeners dynamically formed a new, and increasingly accurate, implicit cognitive model of the information-theoretic structure of the sequences throughout the experimental session. (shrink)

As we know, a cognitive architecture is a domain-generic computational cognitive model that may be used for a broad analysis of cognition and behavior. Cognitive architectures embody theories of cognition in computer algorithms and programs. Social simulation with multi-agent systems can benefit from incorporating cognitive architectures, as they provide a realistic basis for modeling individual agents (as argued in Sun 2001). In this survey, an example cognitive architecture will be given, and its application to social simulation will be sketched.

This paper describes how meta-cognitive processes (i.e., the self monitoring and regulating of cognitive processes) may be captured within a cognitive architecture Clarion. Some currently popular cognitive architectures lack sufficiently complex built-in meta-cognitive mechanisms. However, a sufficiently complex meta-cognitive mechanism is important, in that it is an essential part of cognition and without it, human cognition may not function properly. We contend that such a meta-cognitive mechanism should be an integral part of a cognitive architecture. Thus such a mechanism has (...) been developed within the Clarion cognitive architecture. The paper demonstrates how human data of two meta-cognitive experiments are simulated using Clarion. The simulations show that the meta-cognitive processes represented by the experimental data (and beyond) can be adequately captured within the Clarion framework. (shrink)

This thesis aims to develop a psychologically plausible account of concepts by integrating key insights from philosophy (on the metaphysical basis for concept possession) and psychology (on the mechanisms underlying concept acquisition). I adopt an approach known as informational atomism, developed by Jerry Fodor. Informational atomism is the conjunction of two theses: (i) informational semantics, according to which conceptual content is constituted exhaustively by nomological mind–world relations; and (ii) conceptual atomism, according to which (lexical) concepts have no internal structure. I (...) argue that informational semantics needs to be supplemented by allowing content-constitutive rules of inference (“meaning postulates”). This is because the content of one important class of concepts, the logical terms, is not plausibly informational. And since, it is argued, no principled distinction can be drawn between logical concepts and the rest, the problem that this raises is a general one. An immediate difficulty is that Quine’s classic arguments against the analytic/synthetic distinction suggest that there can be no principled basis for distinguishing content-constitutive rules from the rest. I show that this concern can be overcome by taking a psychological approach: there is a fact of the matter as to whether or not a particular inference is governed by a mentally-represented inference rule, albeit one that analytic philosophy does not have the resources to determine. I then consider the implications of this approach for concept acquisition. One mechanism underlying concept acquisition is the development of perceptual detectors for the objects that we encounter. I investigate how this might work, by drawing on recent ideas in ethology on ‘learning instincts’, and recent insights into the neurological basis for perceptual learning. What emerges is a view of concept acquisition as involving a complex interplay between innate constraints and environmental input. This supports Fodor’s recent move away from radical concept nativism: concept acquisition requires innate mechanisms, but does not require that concepts themselves be innate. (shrink)

consciousness. Such unconscious processing always " Cambridge, UK " tends to re?ect habitual or strong responses. From this.

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. (shrink)

This paper explores the interaction between implicit and explicit processes during skill learning, in terms of top-down learning (that is, learning that goes from explicit to implicit knowledge) versus bottom-up learning (that is, learning that goes from implicit to explicit knowledge). Instead of studying each type of knowledge (implicit or explicit) in isolation, we stress the interaction between the two types, especially in terms of one type giving rise to the other, and its effects on learning. The work presents an (...) integrated model of skill learning that takes into account both implicit and explicit processes and both top-down and bottom-up learning. We examine and simulate human data in the Tower of Hanoi task. The paper shows how the quantitative data in this task may be captured using either top-down or bottom-up approaches, although top-down learning is a more apt explanation of the human data currently available. These results illustrate the two different directions of learning (top-down versus bottom-up), and thereby provide a new perspective on skill learning. Ó 2003 Elsevier B.V. All rights reserved. (shrink)

Research in computational cognitive modeling investigates the nature of cognition through developing process-based understanding by specifying computational models of mechanisms (including representations) and processes. In this enterprise, a cognitive architecture is a domaingeneric computational cognitive model that may be used for a broad, multiple-level, multipledomain analysis of behavior. It embodies generic descriptions of cognition in computer algorithms and programs. Developing cognitive architectures is a difficult but important task. In this article, discussions of issues and challenges in developing cognitive architectures will (...) be undertaken, and an example cognitive architecture (CLARION) will be described. (shrink)

What is Computational Intelligence (CI) and what are its relations with Artificial Intelligence (AI)? A brief survey of the scope of CI journals and books with ``computational intelligence'' in their title shows that at present it is an umbrella for three core technologies (neural, fuzzy and evolutionary), their applications, and selected fashionable pattern recognition methods. At present CI has no comprehensive foundations and is more a bag of tricks than a solid branch of science. The change of focus from methods (...) to challenging problems is advocated, with CI defined as a part of computer and engineering sciences devoted to solution of non-algoritmizable problems. In this view AI is a part of CI focused on problems related to higher cognitive functions, while the rest of the CI community works on problems related to perception and control, or lower cognitive functions. Grand challenges on both sides of this spectrum are addressed. (shrink)

Running head: Implicit sequence learning ABSTRACT Can we learn without awareness? Although this issue has been extensively explored through studies of implicit learning, there is currently no agreement about the extent to which knowledge can be acquired and projected onto performance in an unconscious way. The controversy, like that surrounding implicit memory, seems to be at least in part attributable to unquestioned acceptance of the unrealistic assumption that tasks are process-pure, that is, that a given task exclusively involves either implicit (...) or explicit knowledge. (shrink)

Using positron emission tomography (PET) and regional cerebral blood flow (rCBF) measurements, we investigated the cerebral correlates of consciousness in a sequence learning task through a novel application of the Process Dissociation Procedure, a behavioral paradigm that makes it possible to separately assess conscious and unconscious contributions to performance. Results show that the metabolic response in the anterior cingulate / mesial prefrontal cortex (ACC / MPFC) is exclusively and specifically correlated with the explicit component of performance during recollection of a (...) learned sequence. This suggests a significant role for the ACC / MPFC in the explicit processing of sequential material.  2003 Elsevier Science B.V. All rights reserved. (shrink)

Pierre Maquet1,2,6, Steven Laureys1,2, Philippe Peigneux1,2,3, Sonia Fuchs1, Christophe Petiau1, Christophe Phillips1,6, Joel Aerts1, Guy Del Fiore1, Christian Degueldre1, Thierry Meulemans3, André Luxen1, Georges Franck1,2, Martial Van Der Linden3, Carlyle Smith4 and Axel Cleeremans5.