An analog of Ohm's law for modeling mental workload
Abstract
With the increased use of computer-based technology, the work of the human has become more cognitive. As a result, the objective measurement of mental workload has become more vital in the design of jobs and the development of adaptive interfaces. This research focused on developing and validating an Ohm's law analogy for mental workload based on individuality and the human's ability to process information. In this model, current was defined as the rate of information transmission (in bit/s); voltage was the mental workload per unit of processed information; and resistance was a function of task type and individual factors. To test the proposed hypothesis, the analog voltage-current relationship was evaluated in 24 senior nursing students operating a computer-based system. All subjects performed three different, rule-based, self-paced tasks varying in complexity (low, moderate, and high). Regression analysis across all three tasks showed that a linear relationship existed between the analogs of voltage and current (t(1,69) = $-$5.97, p = 0.0001), indicating that a differential resistance was present. For the region of operation, the differential resistance was negative. Analysis for each of the three tasks separately showed a linear relationship for only the high and moderate complexity tasks. Cognitive ability, fatigue, stress, anxiety, computer skill, and content skill were measured as possible components of resistance. Using stepwise regression analysis, cognitive ability was identified as a significant component of resistance (F(1,22) = 14.2, p = 0.0011). A model relating voltage to current and cognitive ability produced an R$\sp2$ = 0.66 for the moderate and high complexity task sets. Similar results were also obtained from a more pragmatic model in which the voltage analog was the mental workload per decision and the current analog was the decision rate (R$\sp2$ = 0.63). This ohmic model could be refined and applied in the workplace for on-line, mental-workload monitoring and in the control of adaptive computer interfaces.
Degree
Ph.D.
Advisors
Salvendy, Purdue University.
Subject Area
Industrial engineering
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.