Since its publication in 1908, the paper that Yerkes and Dodson wrote about their experiments, “The Relation of Strength of Stimulus to Rapidity of Habit-Formation,” has come to be recognized as a landmark in the history of psychology. The phenomenon they discovered, known as the Yerkes-Dodson law, has been observed, in various forms, far beyond the world of dancing mice and differently colored doorways. It affects people as well as rodents. In its human manifestation, the law is usually depicted as a bell curve that plots the relation of a person’s performance at a difficult task to the level of mental stimulation, or arousal, the person is experiencing. At very low levels of stimulation, the person is so disengaged and uninspired as to be moribund; performance flat-lines. As stimulation picks up, performance strengthens, rising steadily along the left side of the bell curve until it reaches a peak. Then, as stimulation continues to intensify, performance drops off, descending steadily down the right side of the bell. When stimulation reaches its most intense level, the person essentially becomes paralyzed with stress; performance again flat-lines. Like dancing mice, we humans learn and perform best when we’re at the peak of the Yerkes-Dodson curve, where we’re challenged but not overwhelmed. At the top of the bell is where we enter the state of flow. The Yerkes-Dodson law has turned out to have particular pertinence to the study of automation. It helps explain many of the unexpected consequences of introducing computers into work places and processes. In automation’s early days, it was thought that software, by handling routine chores, would reduce people’s workload and enhance their performance. The assumption was that workload and performance were inversely correlated. Ease a person’s mental strain, and she’ll be smarter and sharper on the job. The reality has turned out to be more complicated. Sometimes, computers succeed in moderating workload in a way that allows a person to excel at her work, devoting her full attention to the most pressing tasks. In other cases, automation ends up reducing workload too much. The worker’s performance suffers as she drifts to the left side of the Yerkes-Dodson curve.
The state of “flow”, where we are best challenged but not overwhelmed, is extremely pertinent to the study of how automation can change our lives for the better, or worse. It brings to the question, whether should we consider the human element, when we automate tasks away for efficiency.
Should a little inefficiency be compromised, for optimal engagement and performance in the worker/customer/person? There is no real true answer to this, but it is important to take this into account when designing processes for people.
