This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

Return to Session

Hierarchical Bayesian Psychometric Curves Reveal Mechanisms of The Vigilance Decrement

Prof. Jason McCarley
Oregon State University ~ School of Psychological Science
Yamani Yusuke

A vigilance task requires observers to monitor for rare signals over long periods of time. The vigilance decrement is a decrease in detection rate that occurs with time on task, sometimes beginning within 5 minutes. Signal detection analyses have ascribed the decrement to changes of response bias or declines of perceptual sensitivity. However, recent work has suggested that sensitivity losses in vigilance are spurious, and that the decrement instead results from attentional lapses.Analysis of psychometric curves provides a way of isolating changes in bias, sensitivity, and lapse rate. Because signal events are rare and trials are partitioned into brief blocks, though, a standard vigilance task does not provide enough data to fit psychometric curves for individual observers. To circumvent this problem, we used hierarchical Bayesian modeling to combine data from a large number of individuals.Participants (N = 99) performed a 20-min vigilance task that required them to judge whether the gap between two probe dots each trial exceeded a criterion value. Signal detectability was manipulated via the method of constant stimuli. Hierarchical psychometric curves were fit to data from the first and last 4-minute blocks of trials. Model fits revealed three changes between blocks: a conservative shift of response bias, a decrease in perceptual sensitivity, and an increase in response lapse rate. Results confirm that sensitivity losses are possible in a sustained attention task, but indicate that mental lapses can also contribute to the vigilance decrement.






There is nothing here, yet. Be the first to create a thread