A hierarchical Bayesian analysis of psychometric curve changes in a vigilance task: an online replication

When human monitors are tasked with detecting rare signals among noise for prolonged periods, they typically exhibit a decline in correct detections over time. This so-called vigilance decrement is usually attributed to losses in the monitor's ability to distinguish signal from noise (i.e., sensitivity) in high event rate, memory-loading tasks (Parasuraman & Davies, 1977). Recent work, however, suggests that shifts in observers’ willingness to respond (i.e., response bias) can masquerade as sensitivity losses (Thomson et al. 2016), prompting reconsideration of the mechanisms underlying the vigilance decrement. The current experiment examined the extent to which observed vigilance decrements reflect changes in sensitivity, response bias, and attentional lapses, using a computational modeling approach. One-hundred twenty-nine participants completed an online, visual signal detection task, judging whether the separation between two probes exceeded a criterion value. Separation was varied across trials using the method of single stimuli and data were fit with logistic psychometric curves. Parameters representing sensitivity, response bias, and attentional lapse rate were compared across the first and last four minutes of the vigil. A hierarchical Bayesian analysis gave decisive evidence of increased attentional lapse rate, strong evidence of conservative shifts in response bias, and anecdotal evidence of decreased sensitivity. These results suggest that the vigilance decrement primarily reflects lapses in operator attention and a decreased willingness to respond ‘signal’ with time-on-task. Understanding the mechanisms underlying the vigilance decrement is important for effectively mitigating it.