Development of a measure to assess falling asleep behind-the-wheel
Date of Completion
Psychology, Industrial|Psychology, Psychometrics|Transportation
Variations in subjective state are often associated with concomitant decrements in performance. Transportation research suggests that these decrements in performance are often a precursor to accidents precipitated by vehicle operator error. Although the accurate and timely assessment of subjective state may prevent accidents, many argue that this is a very difficult task and accurate prediction may not be possible due to limitations in the currently used methods. This dissertation explored the possibility that a behaviorally-based subjective measure and the psychophysical method of magnitude estimation might prove to be a reliable way to monitor a driver's subjective state.^ Two studies were designed and completed. In the first study, participants repeatedly made magnitude estimates of how easy it would be to fall asleep or how easy it is to stay awake while performing a computer task battery consisting of simple addition and the Baddeley logical reasoning task. In the second study, participants made the same judgments but performed a simulated driving task rather than the addition and reasoning tasks. Four male graduate students were trained and served as participants. For all sessions, a logarithmic transformation of the magnitude estimation data was described by a significant linear function. These results provide strong evidence that the psychophysical method of magnitude estimation can be used to scale the perceived ease of falling asleep and staying awake. The behaviorally-based subjective measure and the slopes of the linear functions fit to these data were predictive of performance on the computer task battery and the driving simulator. This clearly demonstrates the feasibility of using magnitude estimation functions as a predictor of falling asleep behind-the-wheel. ^
Paley, Michael Jonathan, "Development of a measure to assess falling asleep behind-the-wheel" (1996). Doctoral Dissertations. AAI9707843.