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疲劳的混合框架:将动机控制和计算调节因素与伽马振荡联系起来。

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

作者信息

Borghetti Lorraine, Curley Taylor, Rhodes L Jack, Morris Megan B, Veksler Bella Z

机构信息

Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH, United States.

ORISE at Air Force Research Laboratory, Wright-Patterson Air Force Base, Dayton, OH, United States.

出版信息

Front Neuroergon. 2024 May 28;5:1375913. doi: 10.3389/fnrgo.2024.1375913. eCollection 2024.

DOI:10.3389/fnrgo.2024.1375913
PMID:38864094
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11165150/
Abstract

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.

摘要

引言

有必要对任务执行时间对绩效的疲劳影响(即警觉性下降)进行全面描述,以提高预测准确性。我们通过将三个独立的描述整合到一个新颖的混合框架中来满足这一需求。该框架将(1)一个由有影响力的认知能量理论描述的平衡目标驱动和舒适驱动的动机系统,与(2)来自最近的疲劳计算模型中积累的微失误,以及(3)指示动机控制波动的额叶伽马振荡结合在一起。此外,该混合框架在疲劳文献中未描述的时间尺度上,将短暂失误(发生在毫秒级)与动机系统的动态正式联系起来。

方法

从一个简短的警觉任务中收集脑电图(EEG)和行为数据。检测高频伽马振荡,将以动机为潜在因素的努力控制过程作为指标。对分箱和单次试验的伽马功率进行实时和滞后时间变化评估,并与行为进行关联。功能连接分析评估任务执行过程中额叶-顶叶通信中伽马功率的方向性。作为潜在动机的高分辨率表示,伽马功率在两个计算模型中由疲劳调节因子进行缩放。微失误调节从努力控制状态到最小努力默认状态的转变。将混合模型与仅包含微失误的计算模型进行比较,以评估与模拟数据的拟合优度。

结果

研究结果表明,实时高伽马功率表现出与努力动机控制一致的特性。然而,伽马功率未能与反应时间随时间的增加相关联,这表明电生理学与行为之间的关系不足以捕捉疲劳影响的全貌。方向性连接证实了额叶伽马活动在额叶-顶叶网络控制过程中的主导地位。将高额叶伽马功率参数化,作为相对动机控制波动的指标,其产生的结果与之前仅包含微失误的计算模型一样准确或更优。

讨论

混合框架将疲劳视为能量动机系统的一个函数,管理着控制过程与相互竞争的幸福感需求之间的权衡空间。两个伽马计算模型为该框架提供了有说服力且简洁的支持,该框架可能适用于疲劳干预技术及相关有效性测量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e49/11165150/8007f2397d30/fnrgo-05-1375913-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e49/11165150/d48a361bd84e/fnrgo-05-1375913-g0001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e49/11165150/b942fb097c6b/fnrgo-05-1375913-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e49/11165150/a57735d4c116/fnrgo-05-1375913-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e49/11165150/4fbff67f4cf1/fnrgo-05-1375913-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e49/11165150/8007f2397d30/fnrgo-05-1375913-g0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e49/11165150/d48a361bd84e/fnrgo-05-1375913-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e49/11165150/63f6c98fd999/fnrgo-05-1375913-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e49/11165150/b942fb097c6b/fnrgo-05-1375913-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e49/11165150/a57735d4c116/fnrgo-05-1375913-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e49/11165150/4fbff67f4cf1/fnrgo-05-1375913-g0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1e49/11165150/8007f2397d30/fnrgo-05-1375913-g0006.jpg

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