This article explicates the interaction between implicit and explicit processes in skill learning, in contrast to the tendency of researchers to study each type in isolation. It highlights various effects of the interaction on learning (including synergy effects). The authors argue for an integrated model of skill learning that takes into account both implicit and explicit processes. Moreover, they argue for a bottom-up approach (first learning implicit knowledge and then explicit knowledge) in the integrated model. A variety of qualitative data can be accounted for by the approach. A computational model, CLARION, is then used to simulate a range of quantitative data. The results demonstrate the plausibility of the model, which provides a new perspective on skill learning. The role of implicit learning in skill acquisition and the distinction between implicit and explicit learning have been widely recognized in recent years (see, e.g., Cleeremans, Destrebecqz, &

This article explores the view that computational models of cognition may constitute valid theories of cognition, often in the full sense of the term ‘‘theory”. In this discussion, this article examines various (existent or possible) positions on this issue and argues in favor of the view above. It also connects this issue with a number of other relevant issues, such as the general relationship between theory and data, the validation of models, and the practical benefits of computational modeling. All the discussions point to the position that computational cognitive models can be true theories of cognition.

Many experiments have tested the effect of distracting activities during an incubation period between a variety of main tasks, but no stable pattern of results has emerged. In the present paper, we propose a clarification and re-interpretation of the effect of distracting activities on incubation using a well-established cognitive model-- the CLARION cognitive architecture. The resulting predictions are tested in a human experiment. The results confirmed our predictions, which suggests that incubation is a diverse phenomenon, involving diverse cognitive processes. Hence, distracting activities can have different effects on incubation depending on the task used to assess the presence of incubation.

“The most exciting phrase to hear in science, the one that heralds new discoveries, is not 'Eureka! ' (I found it!) but 'That's funny... ' “ Isaac Asimov (Science fiction novelist & scholar: 1920- 1992) As the quote above illustrates, there are facets of the scientific process that are prompted by feelings of intuition. Although this notion may seem closer to stereotypes of art than science, the notion that scientists can rely upon knowledge that is generated without their awareness has an empirical foundation in the literature on implicit learning (e.g. Reber, 1989; Lewicki, Czyzewska, & Hoffman, 1987; Mathews, et al., 1989). So, although the subjective experience of intuition may seem “magical,” researchers have begun to characterize the characteristics of such knowledge. One major theme of this chapter will be that implicit knowledge (knowledge gained directly from experience) and explicit knowledge (e.g. the explicit facts that are typically acquired in science instruction) may be fruitfully combined (and clearly are in mature scientists). To use the example above, the intuition that alerts one to the detection of an anomaly (e.g., “That’s funny”) is likely to spur a more explicit reasoning process that involves trying to locate the source of the anomaly and understand its implications. Most educational settings focus on teaching conceptual (explicit) knowledge rather than setting

The complexity of reinforcement learning problems grows exponentially with the size of the state space, which renders realistic cases unsolvable and underlines the need for guidance. This thesis studies a hybrid agent architecture, in which the toplevel module reuses temporal knowledge in the form of plans that it extracts from a concurrently executing low-level reinforcement learner. The first contribution of this work are significant improvements of the original model and implementation of the agent architecture, resulting in a more effective knowledge extraction and reuse. The second contribution is an extensive exploration of the synergy effects that take place between both layers of the architecture. It is shown that the combination of state abstraction and the reuse of plans as temporal abstraction can lead to a significantly shorter learning time of a reinforcement learning agent. Likewise, the number of decisions to be made by the agent is reduced because a plan is a definite commitment to a course of actions that does not require intermediary reasoning. In addition, we demonstrate that the architecture enables the integration of plans as prior knowledge through a clear and convenient interface. Thus, partial and approximate solutions to

This full-day tutorial introduces participants to CLARION, a dual-process/dual-representation cognitive architecture that focuses on the distinction between explicit and implicit cognitive processes. CLARION is also integrative, involving cognition, motivation, metacognition, and so on. This tutorial presents a detailed description, along with many simulations, advanced topics, and formal results. Although some prior exposure to cognitive architectures and artificial neural networks can be helpful, prior understanding of these areas is not required, as the full-day format allows a detailed presentation of basic, as well as advanced, topics related to cognitive modeling using CLARION. This tutorial will enable participants to apply the basic concepts, theories, and computational models of CLARION to their own work. Overview CLARION is a cognitive architecture composed of four main subsystems: the Action-Centered Subsystem (ACS), the Non-Action-Centered Subsystem (NACS), the Meta-Cognitive Subsystem (MCS), and the Motivational Subsystem (MS). The ACS is used mainly for action decision-making. The NACS is usually a slave system to the ACS and is used to store declarative and episodic knowledge. This subsystem is also responsible for reasoning in CLARION. The MS is responsible for determining motivational drive levels (which in turn lead to the setting of goals). The MCS is responsible for cognitive monitoring and parameter setting in both the ACS and NACS, and makes the goal setting determinations based on drive levels reported from the MS. In addition to the aforementioned subsystem structure, CLARION is based on two other basic assumptions: representational differences and learning differences of two different types of knowledge: implicit versus explicit (Sun,

journal homepage: www.elsevier.com/locate/neucom World survey of artificial brains, Part II: Biologically inspired