Examining attentional and memory mechanisms of the vigilance decrement with event-related potentials

Morris, Megan B.; Rhodes, L. Jack; Borghetti, Lorraine; Haubert, Ashley; Gunzelmann, Glenn

doi:10.1504/ijhfe.2022.127448

Cited by 3 publications

(2 citation statements)

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“…The results provide fine-grained by-participant evidence of a vigilance decrement. Moreover, the decrease in slowest responses from Q4 to Q5 corresponds to endspurt behavior observed in other fatigue and vigilance tasks (Morris et al, 2020 , 2022 ).…”

Section: Resultssupporting

confidence: 57%

“…Unsurprisingly, nonmonotonicity emerged in the frontal N1 ERP associated with controlled attentional orientation, while the parietal P3 ERP, indexing context processing, also exhibited a similar pattern. Other research also demonstrated that non-monotonic ERP activity across diverse vigilance tasks were dominated by frontal ERP components, though energetical activity was also observed in parietal P3 components (Morris et al, 2023). These findings provide converging evidence for the energetical control of basic functions but are less conclusive with respect to cortical orientation and the role of motivation.…”

Section: Neurological Basis Of the Hybrid Frameworkmentioning

confidence: 92%

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

Borghetti,

Curley,

Rhodes

et al. 2024

Front. Neuroergonomics

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IntroductionThere is a need to develop a comprehensive account of time-on-task fatigue effects on performance (i.e., the vigilance decrement) to increase predictive accuracy. We address this need by integrating three independent accounts into a novel hybrid framework. This framework unites (1) a motivational system balancing goal and comfort drives as described by an influential cognitive-energetic theory with (2) accumulating microlapses from a recent computational model of fatigue, and (3) frontal gamma oscillations indexing fluctuations in motivational control. Moreover, the hybrid framework formally links brief lapses (occurring over milliseconds) to the dynamics of the motivational system at a temporal scale not otherwise described in the fatigue literature.MethodsEEG and behavioral data was collected from a brief vigilance task. High frequency gamma oscillations were assayed, indexing effortful controlled processes with motivation as a latent factor. Binned and single-trial gamma power was evaluated for changes in real- and lagged-time and correlated with behavior. Functional connectivity analyses assessed the directionality of gamma power in frontal-parietal communication across time-on-task. As a high-resolution representation of latent motivation, gamma power was scaled by fatigue moderators in two computational models. Microlapses modulated transitions from an effortful controlled state to a minimal-effort default state. The hybrid models were compared to a computational microlapse-only model for goodness-of-fit with simulated data.ResultsFindings suggested real-time high gamma power exhibited properties consistent with effortful motivational control. However, gamma power failed to correlate with increases in response times over time, indicating electrophysiology and behavior relations are insufficient in capturing the full range of fatigue effects. Directional connectivity affirmed the dominance of frontal gamma activity in controlled processes in the frontal-parietal network. Parameterizing high frontal gamma power, as an index of fluctuating relative motivational control, produced results that are as accurate or superior to a previous microlapse-only computational model.DiscussionThe hybrid framework views fatigue as a function of a energetical motivational system, managing the trade-space between controlled processes and competing wellbeing needs. Two gamma computational models provided compelling and parsimonious support for this framework, which can potentially be applied to fatigue intervention technologies and related effectiveness measures.

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Section: Resultssupporting

confidence: 57%

Section: Neurological Basis Of the Hybrid Frameworkmentioning

confidence: 92%