Beyond the Vigilance End-Spurt with Event-Related Potentials

Abstract

As task environments become more automated and operators become more passive monitors, it is important to understand the underlying mechanisms of the vigilance decrement to help inform task development and interventions. Some vigilance studies have reported an end-spurt effect, where performance increases at the end of the task. This is commonly purported to be the result of increased resources from motivation or arousal; however, self-regulation of attentional resources throughout the vigil has not been addressed. To provide insight into the mechanisms underlying the vigilance decrement, we utilize event-related potentials to examine the vigilance decrement beyond the end-spurt. Thirty-two individuals aged 18 – 36 ( M = 22.60; SD = 4.08) completed a vigilance task with an unknown end time while EEG data was collected. Results revealed non-monotonic trends in N1 and P3 components, suggesting that individuals might self-regulate resources during the vigil, adapting to the constraints of the task environment.

Gamma Power as an Index of Sustained Attention in Simulated Vigilance Tasks

“…Fig. 2 illustrates the main findings of the study: significant trends indicating decreases in gamma spectral power across time‐on‐task in both the frontal (Fz) and parietal (Pz) regions of the brain, with a significant end‐spurt toward the end of the task (Morris et al., 2020). Borghetti et al.…”

“…More recent alternatives to capacity accounts of effort are those that postulate that finite resources are dynamically allocated as a function of task goals and internal motivation. These frameworks help account for common compensatory behaviors during sustained attention tasks that cannot be easily accounted for by resource-depletion theories, such as significant performance increases toward the end of a vigil, that is, "end-spurt" efforts (Bergum & Klein, 1961;Morris, Haubert, & Gunzelmann, 2020). One particularly successful theory is Hockey's (1997Hockey's ( , 2013Hockey's ( , 2011 cognitive-energetical framework (Fig.…”

“…In the proposed model, however, ζ can occasionally exceed its upper bound, meaning that parameterized effort cannot go below 0 (meaning “absolute” fatigue), but can surpass unity (meaning “extra” effort). Thus, ζ can capture decrements due to fatigue as well as compensatory efforts that offset fatigue, such as the end‐spurt effect (Morris et al., 2020).…”

Hybrid framework of fatigue: connecting motivational control and computational moderators to gamma oscillations

“…Electroencephalography (EEG), assaying oscillatory activity at the millisecond level, enjoys a temporal resolution well-suited to realizing dynamic motivational control. For example, recent work found non-monotonic activity in event-related potentials (ERPs) aggregated across experimental blocks, reflecting pacing and end-spurt effects (Morris et al, 2020 ) interpretable as a coarse representation of energetical effort over time. Unsurprisingly, non-monotonicity emerged in the frontal N1 ERP associated with controlled attentional orientation, while the parietal P3 ERP, indexing context processing, also exhibited a similar pattern.…”

Fatigue Leads to Dynamic Shift in Fronto-parietal Sustained Attention Network

“…For our study, data were separated into five 2-minute bins across the 10-minute task, with the first and fourth bins selected for subsequent analyses. Bin 5 was omitted due to evidence of end-spurts (Morris, Haubert, & Gunzelmann, 2020) in that bin. Selection of beginning and late bins allows comparison of GP across sensors (e.g., Fz→Pz, frontal to parietal; Pz→Fz, parietal to frontal) within time bins, and comparison of Fz→Pz and, separately, of Pz→Fz across beginning and late time bins.…”