Role of Practice in Dual-Task Performance: Toward Workload Modelling in a Connectionist/Control Architecture.

摘要

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 connectionist/control 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. (aw)