疲劳的混合框架:将动机控制和计算调节器与伽马振荡联系起来。

IF 1.5 Q3 ERGONOMICS Frontiers in neuroergonomics Pub Date : 2024-05-28 eCollection Date: 2024-01-01 DOI:10.3389/fnrgo.2024.1375913
Lorraine Borghetti, Taylor Curley, L Jack Rhodes, Megan B Morris, Bella Z Veksler
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引用次数: 0

摘要

导言:为了提高预测的准确性,有必要对任务时间疲劳对成绩的影响(即警觉性下降)进行全面的阐述。为了满足这一需求,我们将三种独立的解释整合到一个新颖的混合框架中。该框架将(1)有影响力的认知能量理论所描述的平衡目标和舒适驱动力的动机系统与(2)最近的疲劳计算模型所产生的累积微脉冲,以及(3)反映动机控制波动的额叶伽马振荡结合在一起。此外,该混合框架将短暂的失误(发生在几毫秒内)与动机系统的动态正式联系起来,其时间尺度在疲劳文献中没有其他描述:方法:从短暂的警觉任务中收集脑电图和行为数据。对高频伽马振荡进行了测定,以动机作为潜伏因素,对努力控制过程进行指标化。评估了分频和单次试验伽马功率在实时和滞后时间的变化,并将其与行为相关联。功能连通性分析评估了伽马功率在任务时间内额叶-顶叶沟通的方向性。作为潜在动机的高分辨率表征,伽马功率在两个计算模型中被疲劳调节因子缩放。微间隙调节了从努力控制状态到最小努力默认状态的转换。结果显示,混合模型与纯微缩计算模型的拟合度与模拟数据进行了比较:结果:研究结果表明,实时高伽玛功率表现出与努力动机控制相一致的特性。然而,伽马功率未能与反应时间的延长相关联,这表明电生理学和行为学的关系不足以捕捉疲劳效应的全部范围。定向连接证实了额叶伽马活动在额叶-顶叶网络的控制过程中占主导地位。将额叶伽马高功率参数化,作为相对动机控制波动的指标,得出的结果与之前的纯微缩计算模型一样准确,甚至更优:混合框架将疲劳视为能量激励系统的一种功能,它管理着受控过程与相互竞争的健康需求之间的交换空间。两个伽马计算模型为这一框架提供了令人信服的简明支持,该框架有可能应用于疲劳干预技术和相关的有效性测量。
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Hybrid framework of fatigue: connecting motivational control and computational moderators to gamma oscillations.

Introduction: There 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.

Methods: EEG 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.

Results: Findings 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.

Discussion: The 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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Self-perceptual blindness to mental fatigue in mining workers. Self-control enhances vigilance performance in temporally irregular tasks: an fNIRS frontoparietal investigation. Editorial: Neurotechnology for brain-body performance and health: insights from the 2022 Neuroergonomics and NYC Neuromodulation Conference. Editorial: Stress and the brain: advances in neurophysiological measures for mental stress detection and reduction. Editorial: Advances and challenges to bridge computational intelligence and neuroscience for brain-computer interface.
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