Dr. Stephen J. Guastello is a Professor of Psychology at Marquette University, Milwaukee, WI. He earned his degrees in psychology from the Illinois Institute of Technology (Ph.D.), Washington University, St. Louis (MA), and The Johns Hopkins University (BA).
His research interests center on chaos and complexity theories and their applications to problems in industrial-organizational psychology and human factors engineering. Recently Dr. Guastello published a paper titled: ‘The minimum entropy principle and task performance’ in the journal Nonlinear Dynamics, Psychology, and Life Sciences.
Background of the study
Increasing proportions of work done each day by millions of people involve cognitive labor. Although computerization can reduce work in some respects, it can generate new sources of fatigue and workload, particularly if people need to keep up with a fast flow of incoming data or task requests, or to keep up with automatic machine controls that seem to have a mind of their own. The minimum entropy principle holds that, as we learn to do a task, we find ways of making our physical and mental motions as efficient as possible with a minimum of wasted motion or decision time.
The present study developed as part of a larger project on cognitive workload and fatigue, where we were trying to tease apart several conflicting influences on performance as it unfolds over time. This study considered two such conflicts:
- “Best” performers are also expected to be consistent performers, but some variability is needed to remain adaptable to chance events as they arise. Variability is also necessary if performance capabilities are ever going to improve further.
- Switching tasks can reduce fatigue, but it incurs a workload cost because of the added quantity of information we need to keep active in our working memories.
We investigated two types of variability associated with total performance. If we just examined the number of visits to the different performance ranges, the better performers spent less time in the lower performance ranges and were less variable overall. Strategies that involved a complete cycle of seven perceptual-motor tasks before repeating any of the tasks produced the lowest performance variability, but the task switching costs were high in this strategy, which was least often preferred by the participants.
When we examined the length of a performance pattern (a series of ups and downs) before the pattern changed, the people with the longest recurring patterns performed better. Task selection strategies that supported longer patterns involved finishing a series of one type of task before starting another.
Voluntary task switching is more advantageous than machine-driven switching, but modest constraints on individual discretion, such as task quotas, can have a positive impact.
Although we find ourselves multitasking to improve overall output, not all switching patterns are equally efficient. Only about 50% of the participants in the study adopted an efficient strategy. All other emergencies, or machine downtime, being equal, it’s better to finish one type of task before starting another.
Our research strategy is to examine any principles we uncover concerning cognitive workload and fatigue with tasks that require different cognitive resources or place different forms of workload or fatigue demands on the operator. The next installment on the minimum entropy principle, performance, and performance variability will involve financial decision making (optimization and risk-taking) over an extended period of time.