Evaluation of complex and dynamic safety tasks in human learning using the ACT-R and SOAR skill acquisition theories
This paper provides a human-centered analytical approach to learning dynamic and complex tasks using the Adaptive Control of Thought-Rational (ACT-R) and the State, Operator And Result (SOAR) models by comparing the task times of the model and the subjects. Twenty-one full time assembly line workers at a local computer company (14 men and 7 women) from ages 1832 (Mean=19.86years, SD=0.96years) were randomly selected for this analysis. The task involved the placement of printed circuit board (PCB) components on the flow line of the desktop computer mother board manufacturing process. The overall timed performance of the subjects indicated that the match between the model and the subjects was good, resulting in an R2  value of 0.94. At the unit task level performance, and R2  value of 0.96 for placing the PCBs on the flow line. For tasks involving picking and searching of PCBs, the obtained R2  value was 0.76 and R2 of 0.68 at the keystroke level. Findings revealed that the model already started out with a complete strategy of performing the task, whereas the human participants had to acquire additional learning information during the trials. Efforts will be made in the future to determine how the performance of the human subjects could be enhanced to meet or the same level as the model performance.
act-r soar skill acquisition cognitive psychology declarative knowledge procedural knowledge