The Role of Practice in Dual-Task Performance: Toward Workload Modelling in a Connectionist/Control Architecture
CARNEGIE-MELLON UNIV PITTSBURGH PA ARTIFICIAL INTELLIGENCE AND PSYCHOLOGY PROJECT
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The literature on practice effects and transfer from the single- to dual-task performance and part-whole task learning are briefly reviewed. The results suggest that single-task training produces limited transfer to dual-task performance. Past theoretical frameworks for multi-task performance are reviewed. A connectionistcontrol architecture for skill acquisition is presented. The architecture involves neural-like units at the microlevel, with the information transmitted on vectors between modules at the macrolevel. The simulation of the model exhibits five phases of skill acquisition. Dual-task interference and performance are predicted as a function of the phase of practice training the skill has reached. Seven compensatory activities occur in the model during dual-task that do not appear in single-task training 1 task shedding, delay and buffer preloading 2 letting go of high-workload strategies 3 utilizing noncompeting resources 4 time multiplexing 5 shortening transmissions 6 converting interference from concurrent transmissions and 7 chunking transmissions. Future research issues suggested by the architecture include Mapping out the marginal utility of single- to multi-task transfer investigating the classification of multi-task compensatory activities evaluating the role of part-task trainers for multi-task skills and developing and testing quantitative models of skill acquisition.
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