Capturing human data in a letter counting task: accessibility and action-centeredness in representing cognitive skills.
This work presents an integrated model of skill learning that takes into account both implicit and explicit processes and both action-centered and non-action-centered knowledge. The existing distinction between procedural and declarative knowledge unnecessarily confounds these two aspects (action-centeredness and accessibility), and can be made clearer through separating the two aspects. The model is used to simulate human data in a letter counting task. The work shows how the data in this task may be captured using either action-centered knowledge alone or both action-centered and non-action-centered knowledge, though the combined approach produced a better fit. The results demonstrate the difference between these approaches and provide a new perspective on skill learning.
Sun
Ron
R
Cognitive Sciences Department, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. rsun@rpi.edu
Zhang
Xi
X
Mathews
Robert
R
D001185:Artificial Intelligence; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D019359:Knowledge; D007858:Learning / Q000502:physiology*; D009048:Motor Skills / Q000502:physiology*; D016571:Neural Networks, Computer