The role of practice in dual-task performance: toward workload modeling in a connectionist/control architecture.

The literature on practice effects and transfer from singleto 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 information transmitted on vectors between modules at the macrolevel. The simulation of the model exhibits five phases of skill acquisition. Dualtask interference and performance are predicted as a function of the phase of practice the skill has reached. Seven compensatory activities occur in the model during dual-task training that do not appear in single-task training: 1) task shedding, delay and buffer pre-loading; 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 singleto 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.