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Modeling Parallelization and Flexibility Improvements in Skill Acquisition: From Dual Tasks to Complex Dynamic Skills
Author(s) -
Taatgen Niels
Publication year - 2005
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/s15516709cog0000_23
Subject(s) - computer science , flexibility (engineering) , task (project management) , speedup , parallelism (grammar) , dual (grammatical number) , parallel computing , art , statistics , mathematics , management , literature , economics
Emerging parallel processing and increased flexibility during the acquisition of cognitive skills form a combination that is hard to reconcile with rule‐based models that often produce brittle behavior. Rule‐based models can exhibit these properties by adhering to 2 principles: that the model gradually learns task‐specific rules from instructions and experience, and that bottom‐up processing is used whenever possible. In a model of learning perfect time‐sharing in dual tasks (Schumacher et al., 2001), speedup learning and bottom‐up activation of instructions can explain parallel behavior. In a model of a complex dynamic task (Carnegie Mellon University Aegis Simulation Program [CMU‐ASP], Anderson et al., 2004), parallel behavior is explained by the transition from serially organized instructions to rules that are activated by both top‐down (goal‐driven) and bottom‐up (perceptually driven) factors. Parallelism lets the model opportunistically reorder instructions, leading to the gradual emergence of new task strategies.