Unified theories of cognition
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Integrating rules and connectionism for robust commonsense reasoning
Integrating rules and connectionism for robust commonsense reasoning
Robust reasoning: integrating rule-based and similarity-based reasoning
Artificial Intelligence
Learning, action and consciousness: a hybrid approach toward modelling consciousness
Neural Networks - 1997 special issue on neural networks for consciousness
The Architecture of Cognition
Soar Papers: Research on Integrated Intelligence
Soar Papers: Research on Integrated Intelligence
Complementary Category Learning Systems Identified Using Event-Related Functional MRI
Journal of Cognitive Neuroscience
Cognitive Architectures: Where do we go from here?
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Proceedings of the 3rd Computer Science Education Research Conference on Computer Science Education Research
Steptorials: mixed-initiative learning of high-functionality applications
Proceedings of the 19th international conference on Intelligent User Interfaces
| Hi-index | 0.00 |
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.