Predicting transfer from training performance
Computing, Health and Science
The research in this paper was designed to examine the extent to which improvement on a training task can be used to predict performance on a transfer task. This aim involved evaluating the proposition that when old skills are executed in the context of new tasks, they continue to improve as if stimulus conditions have not changed. That is, power functions that describe improvement on old skills during their initial acquisition should predict further improvement on these skills during their execution in new tasks. Three experiments were performed to achieve the aim of testing this proposition. Experiment 1 revealed that old skills were executed slower in the context of a new task than was predicted on the basis of training performance. Hence improvement in the old skills appeared to be disrupted by performance of the new task. Experiment 2 was designed to examine whether this disruption was due to an increase in complexity in the task from training to transfer, or simply due to any change in task. The results suggested that any change may cause some disruption, but this disruption was greatest with an increase in task complexity. Experiment 3 was designed to examine two variables that may affect the magnitude of this effect: the relative change in task complexity from training to transfer, and the amount of practice on a task prior to a change in task. The results indicated that only the former variable had any effect. In all three experiments no effects on performance accuracy were noted, and response times in the transfer tasks eventually returned to levels predicted by training learning functions. These results were interpreted as indicating that old skills do continue to improve in new tasks as if conditions are not altered, but that disruptions caused by transfer are related to performance overheads associated with reconceptualising the task.