This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist (...) and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects. (shrink)

In this paper, I show that the question of how dual process theories of reasoning and judgement account for conflict between System 1 (heuristic) and System 2 (analytic) processes needs to be explicated and addressed in future research work. I demonstrate that a simple additive probability model that describes such conflict can be mapped on to three different cognitive models. The pre-emptive conflict resolution model assumes that a decision is made at the outset as to whether a heuristic or analytic (...) process will control the response. The parallel-competitive model assumes that each system operates in parallel to deliver a putative response, resulting sometimes in conflict that then needs to be resolved. Finally, the default-interventionist model involves the cueing of default responses by the heuristic system that may or may not be altered by subsequent intervention of the analytic system. A second, independent issue also emerges from this discussion. The superior performance of higher-ability participants on reasoning tasks may be due to the fact that they engage in more analytic reasoning ( quantity hypothesis ) or alternatively to the fact that the analytic reasoning they apply is more effective ( quality hypothesis ). (shrink)

This paper presents a novel learning model Clarion , which is a hybrid model based on the two-level approach proposed in Sun (1995). The model integrates neural, reinforcement, and symbolic learning methods to perform on-line, bottom-up learning (i.e., learning that goes from neural to symbolic representations). The model utilizes both procedural and declarative knowledge (in neural and symbolic representations respectively), tapping into the synergy of the two types of processes. It was applied to deal with sequential decision tasks. Experiments and (...) analyses in various ways are reported that shed light on the advantages of the model. (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)