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From implicit skills to explicit knowledge: a bottom‐up model of skill learning
Author(s) -
Sun Ron,
Merrill Edward,
Peterson Todd
Publication year - 2001
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog2502_2
Subject(s) - clarion , connectionism , procedural knowledge , computer science , task (project management) , declarative memory , descriptive knowledge , representation (politics) , reinforcement learning , artificial intelligence , dual (grammatical number) , cognitive science , cognitive psychology , domain knowledge , knowledge management , psychology , cognition , artificial neural network , management , neuroscience , politics , political science , law , economics , art , literature
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.