David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Ezio Di Nucci
Jack Alan Reynolds
Learn more about PhilPapers
We present a skill learning model CLARION. Different from existing models of high-level skill learning that use a topdown approach (that is, turning declarative knowledge into procedural knowledge), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. CLAR- ION is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line learning. We compare the model with human data in a minefield navigation task. A match between the model and human data is found in several respects.
|Keywords||No keywords specified (fix it)|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
Ron Sun (1999). Accounting for the Computational Basis of Consciousness: A Connectionist Approach. Consciousness and Cognition 8 (4):529-565.
Similar books and articles
Ron Sun & Xi Zhang, Accessibility Versus Action-Centeredness in the Representation of Cognitive Skills.
Ron Sun (2005). The Interaction of the Explicit and the Implicit in Skill Learning: A Dual-Process Approach. Psychological Review 112:159-192.
Ron Sun, Todd Peterson & Edward Merrill, Bottom-Up Skill Learning in Reactive Sequential Decision Tasks.
Edward Merrillb & Todd Petersonb (2001). From Implicit Skills to Explicit Knowledge: A Bottom‐Up Model of Skill Learning. Cognitive Science 25 (2):203-244.
Added to index2009-06-13
Total downloads15 ( #238,276 of 1,796,357 )
Recent downloads (6 months)1 ( #467,624 of 1,796,357 )
How can I increase my downloads?