The Impact of Shift Work on Mind-Wandering and Neurocognitive Mechanisms in Drilling Crews

The Impact of Shift Work on Mind-Wandering and Neurocognitive Mechanisms in Drilling Crews | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Impact of Shift Work on Mind-Wandering and Neurocognitive Mechanisms in Drilling Crews Qing Xin, Su Hao, Cai Hongbin, Wang Xiaoqin, Wang Jian, Liu Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7839905/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The stability of cognitive functioning among frontline personnel plays a pivotal role in ensuring operational safety within high-risk industries; nevertheless, the neurocognitive mechanisms through which shift work disrupts attentional control and induces mind-wandering (MW) remain insufficiently understood, particularly under conditions involving prolonged mental load and circadian misalignment. This study utilized a lab-in-the-field experiment with the Sustained Attention to Response Task (SART) and wearable electroencephalography (EEG) technology to investigate the effects of shift work on mind-wandering. The results revealed a significant coupling between behavioral performance and EEG signals. Time-domain analysis revealed that the pre-shift group did not show a distinct N2 component during mind-wandering periods, while the post-shift group displayed a notable increase in N2, indicating enhanced conflict monitoring and cognitive resource allocation efficiency following shift work. Time-domain analysis showed that the pre-shift group lacked a distinct N2 component during mind-wandering periods, whereas the post-shift group demonstrated a noticeable increase in N2, indicating enhanced conflict monitoring and cognitive resource allocation efficiency following shift work. These findings uncover the neurocognitive pathway through which shift work induces mind-wandering, highlighting the N2 component as a key marker of impaired attentional regulation, and offer empirical evidence to support neurophysiological risk monitoring in high-risk operational settings. High-risk industries Shift Work Mind-Wandering Time-Domain Analysis Time-Frequency Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Operational safety in high-risk industries is critically dependent on the stability of workers’ cognitive states. Sectors such as mining, chemical processing, nuclear energy, and oil and gas drilling—characterized by environmental complexity and operational hazards—have long faced persistent challenges posed by human error (Benson et al., 2021). Shift work, which often disrupts circadian rhythms and leads to the accumulation of cognitive load, further exacerbates these risks. Among these industries, oil and gas drilling stands out due to its dynamic work environments, complex equipment systems, and severe consequences in the event of accidents. According to statistical reports, the industry has experienced multiple major incidents, resulting in hundreds of fatalities, extensive environmental damage, and billions of dollars in economic losses (Tamim et al., 2019). Accident investigations indicate that such events are typically the result of a confluence of multiple contributing factors. For instance, both the 2010 Deepwater Horizon disaster in the Gulf of Mexico and the 2015 Gunashli oil platform fire in Azerbaijan revealed a cascading chain of systemic management failures and human operational errors (Tamim et al., 2019). In most cases, human factors play a decisive role. Heinrich's classical "domino theory" posits that unsafe behaviors are the initiating triggers of accidents, capable of inducing equipment failures or exacerbating hazardous conditions. These events form a negative feedback loop—cognitive lapses, equipment malfunction, and environmental degradation—that systematically amplify operational risks. Within this context, attentional lapses have emerged as a key causal factor in unsafe behavior, thereby becoming a critical leverage point in safety management across high-risk domains (Thomson & Smilek, 2022). In cognitive science, the concept of "Mind-Wandering"(MW) was first systematically introduced by Smallwood et al. (2006) to describe task-unrelated thoughts or mental imagery, which typically result in varying degrees of attentional disengagement. This conceptual framework laid the theoretical foundation for subsequent research on attentional control mechanisms. Empirical studies by Seli et al. (2016) confirmed that frequent mind-wandering significantly impairs task performance, as evidenced by prolonged reaction times and decreased accuracy—marking one of the earliest demonstrations of the behavioral consequences of mind-wandering. Subsequent studies by Leszczynski (2017) and by Walker and Trick (2018) further advanced the field by identifying neurophysiological correlates of mind-wandering. However, two critical limitations remain in the current literature. First, the predominant reliance on laboratory-based paradigms limits the ecological validity of findings, as these studies often fail to capture the complexity of cognitive states in real-world operational settings. Second, although prior studies have confirmed associations between mind-wandering and factors such as fatigue and task load, the dynamic mechanisms through which shift work influences mind-wandering remain underexplored. Fatigue and work duration have been consistently identified as primary drivers of mind-wandering. Research by Cárdenas-Egúsquiza and Berntsen (2022) demonstrated that self-reported sleep disturbances—including poor sleep quality, heightened insomnia symptoms, excessive daytime sleepiness, and evening chronotypes—as well as experimentally induced sleep deprivation, all significantly increase the propensity for mind-wandering. Neurophysiological evidence further suggests that mind-wandering shares overlapping cognitive pathways with drowsiness, as indicated by decreased blink rates and increased variability in pupil diameter (Stawarczyk et al., 2020). The duration of task engagement also plays a crucial role. In continuous task paradigms, Walker and Trick (2018) observed that individuals’ capacity to sustain attention deteriorates over time, resulting in a higher frequency of mind-wandering. Electroencephalography (EEG), with its millisecond-level temporal resolution, provides a powerful tool for objectively capturing fluctuations in attentional states during task performance, thereby offering a technical advantage in the measurement of mind-wandering (Simor et al., 2025). The advancement of portable EEG technology has further enhanced the ecological validity of neuroscience research by enabling real-time monitoring of cognitive activity under complex, real-world working conditions. Existing literature indicates that mind-wandering can be characterized through both event-related potentials (ERP) and oscillatory features observed in time-frequency analysis. Evidence from ERP-based investigations has shown that specific components are reliably associated with mind-wandering. For instance, Gonçalves et al. (2018), employing the Attention Network Task (ANT), identified significant relationships between mind-wandering and both N1 and P3 amplitudes. Building on this, Liu et al. (2021) distinguished subtypes of mind-wandering and found that self-referential episodes were accompanied by increased reaction times, heightened P2 amplitudes, and reduced cognitive control, whereas stimulus-driven mind-wandering was marked by a significant decrease in P300 amplitude, indicating deeper disruptions in higher-order processing. Moreover, enhanced N2 amplitudes have been interpreted as a sign of increased sensitivity to task-irrelevant stimuli, reflecting a diversion of attentional resources from goal-relevant information (Zickerick et al., 2020). Complementary findings from spectral analyses further underscore the neural underpinnings of mind-wandering. Decreases in alpha (α), theta (θ), and beta (β) power, along with reduced phase synchrony in the prefrontal cortex, have been consistently observed during episodes of mind-wandering, suggesting a decoupling of task-relevant neural systems (Bozhilova et al., 2022). Nevertheless, some research has reported that theta power may also increase under high cognitive load, potentially reflecting compensatory recruitment of cognitive resources (Liegel et al., 2022; Griggs et al., 2023). Reductions in beta activity—often associated with cognitive control and attentional engagement—have been linked to attentional disengagement or transition into a relaxed state, both of which are conducive to the onset of mind-wandering (Chaddad et al., 2023). Given the increasingly recognized role of attentional lapses in industrial accidents and the limited ecological validity of prior laboratory-based studies, this research aims to explore the neurocognitive mechanisms by which shift work contributes to mind-wandering in real-world, high-risk operational settings. Building on empirical and theoretical foundations, we hypothesize that shift work—through its disruption of circadian regulation and accumulation of mental fatigue—modulates both behavioral indicators and electrophysiological correlates of mind-wandering. Specifically, we anticipate that workers undergoing shift rotations will exhibit altered event-related potentials, particularly in the N2 component associated with conflict monitoring, as well as frequency-specific changes in theta, alpha, and beta oscillations indicative of cognitive control and attentional resource allocation. To empirically test these hypotheses, we implemented a lab-in-the-field experimental protocol that integrates the Sustained Attention to Response Task (SART) with portable EEG recordings among professional drilling crews in active operational contexts. This study thus seeks to advance current understanding of how shift-related neurocognitive fluctuations can be quantified and leveraged to inform cognitive safety monitoring and risk mitigation strategies in high-risk industries. 2. Experimental Design 2.1 Equipment and Participants Electroencephalographic (EEG) data were collected using the wireless Emotiv Epoc + system (14 channels, 128 Hz sampling rate), with electrode impedance maintained below 10 kΩ to ensure signal quality (Ke et al., 2021). The experimental task was programmed using E-Prime 3.0, enabling the synchronized recording of behavioral data (reaction time and accuracy) and EEG signals. Error trials were used as behavioral markers of mind-wandering. Fifty male drilling operators (age range: 26–57 years, M ± SD = 38.6 ± 8.2) were recruited to participate in a Sustained Attention to Response Task (SART) (Robertson et al., 1997) conducted in an actual drilling environment. All participants were employed at a deep shale gas drilling platform in southwest China, with at least three years of industry experience and active involvement in rotational shift work over the preceding six months. Eligibility criteria included right-handedness, normal or corrected-to-normal vision, and no self-reported history of neurological or psychiatric disorders or substance dependence. 2.2 Experimental Procedure High-risk industries, characterized by complex operational environments, high-density risk factors, and severe accident consequences, have long been a critical focus of safety management. Accidents in these sectors often involve large-scale economic loss, irreversible ecological impact, and sudden casualties. As a representative high-risk industry, the oil and gas industry exhibits particularly pronounced risk features. During routine drilling operations, workers are frequently required to identify unprotected openings at the worksite—an essential behavior for preventing incidents such as falls from height and struck-by-object injuries. Therefore, the present experiment was designed to simulate realistic operational scenarios and assess workers’ ability to detect such hazards under cognitively demanding conditions, thereby identifying potential cognitive biases and latent safety risks. The experimental paradigm was based on the Sustained Attention to Response Task (SART), initially developed by Robertson et al. (1997). Due to its monotonous structure and repetitive nature, this task is well-suited to elicit task-unrelated thoughts, which can induce boredom, diminished motivation, and reduced perceived accomplishment—thus making it a widely adopted paradigm in the study of mind-wandering. Experimental stimuli consisted of 30 high-resolution images (1920×1080) derived from actual oil and gas drilling environments. Standard stimuli (no hazard present) and deviant stimuli (images containing unprotected openings) were presented at a 7:3 ratio. The deviant stimuli were further categorized into three risk types: ( 1 ) derrick-edge fall hazards (40%), ( 2 ) passageway fall hazards (35%), and ( 3 ) dropped-object hazards at the wellbore (25%). Participants were instructed to respond within 500 ms after image onset by pressing a key to indicate the presence and type of risk (keys 1–3 corresponded to the three hazard categories, while key “0” was used for no-risk images). To increase the likelihood of eliciting mind-wandering, interstimulus intervals were set at 1500 ± 200 ms, as suggested by Alanazi et al. (2021). The overall task procedure is illustrated in Fig. 1 . Before the formal experiment, participants watched a video tutorial that provided detailed task instructions. This was followed by five rounds of practice trials to ensure that they fully understood the task requirements. During the main session, each participant completed 30 image-based trials and received monetary compensation upon completion to reinforce task engagement and enhance ecological validity. For the purpose of maintaining scientific rigor and reliability in subsequent data analyses, the study utilized a behavioral criterion to identify episodes of mind-wandering. Based on the findings of Ke et al. (2021), error periods in the SART task were treated as behavioral markers of mind-wandering, as participants typically fail to effectively extract and process task-relevant information during such lapses. Therefore, the present research focused primarily on these error trials as indicators of mind-wandering states, and used them to explore the potential mechanisms through which shift work may influence cognitive stability, providing behavioral-level evidence for the underlying effects. 2.3 EEG Data Acquisition and Preprocessing Raw EEG data were preprocessed using EEGLAB 2021 running on the MATLAB R2021a platform. Given that the EMOTIV Epoc + system employs a common-mode reference design, re-referencing of the raw signals was not required. The preprocessing pipeline included channel localization, application of a 0.05–40 Hz band-pass filter, and artifact removal using independent component analysis (ICA) to eliminate non-neural artifacts such as ocular and muscular interference. A manual artifact rejection step was subsequently performed using a 60.0% rejection threshold to ensure data integrity. Following artifact rejection, EEG epochs were extracted using stimulus onset as the time-locking point (0 ms), with a window from − 200 ms to 800 ms. Baseline correction was performed using the − 200 to 0 ms pre-stimulus interval. Event-related potentials (ERPs) were then computed by averaging across valid trials for each participant to obtain individual-level ERP waveforms. Finally, group-level ERPs were generated for the four experimental groups by computing the grand average across individuals, thereby reducing inter-individual variability. 2.4 Event-Related Potential (ERP) Time-Domain Analysis As shown in Fig. 2 (B), event-related potential (ERP) time-domain analysis is a widely used method to capture neural activity in response to specific stimuli (e.g., visual or motor responses) within a defined temporal window. ERP enhances the stability of EEG signals by averaging across multiple trials time-locked to the stimulus, thereby isolating event-specific neural responses (Mouraux & Iannetti, 2008). The ERP signal is calculated using the following formula: $$\:ERP\left(t\right)=\frac{1}{N}\sum\:_{i=1}^{N}{EEG}_{i}\left(t\right)$$ Where where \(\:N\) denotes the total number of trials, and \(\:{EEG}_{i}\left(t\right)\) represents the EEG signal recorded at time point \(\:t\) during the n -th trial. Among various ERP components, the N2 waveform has been frequently associated with cognitive control and response inhibition. Typically emerging between 200 and 350 ms post-stimulus, N2 is particularly sensitive to attentional conflict and the need for cognitive regulation. Existing studies suggest that N2 amplitude increases significantly during episodes of mind-wandering, reflecting heightened neural demands for conflict monitoring due to attentional lapses. Sustained attention tasks that require continuous goal maintenance tend to elicit stronger N2 responses, particularly under conditions requiring suppression of incorrect or irrelevant responses. Therefore, N2 modulation has been widely recognized as a neurophysiological marker of mind-wandering. 2.5 Time–Frequency Analysis of ERP Signals Many key features of EEG signals cannot be fully captured through purely time-domain or frequency-domain analysis, as critical information is often embedded in transient oscillatory patterns within specific frequency ranges. In order to overcome this limitation, time–frequency analysis is commonly utilized to uncover the dynamic features of EEG signals. Among these methods, continuous wavelet transform (CWT) is one of the most commonly employed techniques, as it allows multiscale decomposition of the signal, enabling simultaneous examination of both temporal and spectral components. CWT achieves adaptive time–frequency resolution by dynamically adjusting the analysis window, providing high temporal resolution for high-frequency components and high frequency resolution for low-frequency components. This property makes it particularly suitable for tracking complex oscillatory dynamics and extracting features from EEG data in cognitive neuroscience studies. As shown in Fig. 2 (C), the flexible resolution and adaptability of CWT are implemented through specific wavelet basis functions. Common choices include Haar and Morlet wavelets. Among them, the Morlet wavelet is widely adopted due to its superior smoothness and balanced resolution in both time and frequency domains. It is especially effective for isolating oscillatory components across different frequency bands in EEG analysis. $$\:\psi\:\left(t\right)={e}^{-\frac{{t}^{2}}{2{\sigma\:}^{2}}}{e}^{j2\pi\:\omega\:t}$$ the parameter \(\:{\omega\:}\) represents the central frequency of the Morlet wavelet, which determines the wavelet's localization in the frequency domain. The parameter \(\:{\sigma\:}\) controls the width of the Gaussian kernel and thereby affects the time–frequency resolution trade-off. The scale parameter \(\:{\alpha\:}\) governs dilation or compression of the wavelet, enabling analysis across different frequency bands, while the translation parameter determines the wavelet’s temporal position, allowing for the extraction of instantaneous frequency characteristics at any given time point. The complete continuous wavelet transform (CWT) of a signal \(\:x\left(t\right)\) is mathematically expressed as the convolution of the signal with a set of time–frequency localized wavelet functions: $$\:X\left(t,\alpha\:\right)=CWT\left\{x\left(t\right)\right\}={\int\:}_{-\infty\:}^{+\infty\:}\frac{1}{\sqrt{\alpha\:}}x\left(\tau\:\right)\psi\:\left(\frac{1}{\alpha\:}(\tau\:-t)\right)d\tau\:$$ Here, the squared modulus \(\:{\left|X\left(t,\alpha\:\right)\right|}^{2}\) is referred to as the wavelet power spectrum, which reflects the energy distribution of the signal across time and frequency domains. In wavelet analysis, the central frequency is selected as a reference for defining the wavelet’s scale and shift parameters. In the present study, the central frequency of the Morlet wavelet was set to 1 Hz, with a corresponding temporal resolution of 3 seconds to ensure an appropriate balance between time and frequency precision. Once defined, each wavelet's frequency-dependent resolution is determined by this reference value. The temporal precision of the wavelet increases with its central frequency, while frequency resolution improves for lower frequencies. Since ERP signals are time-locked but not phase-locked to the stimulus, direct averaging in the time domain may obscure valid neural responses due to signal cancellation. Therefore, time–frequency analysis was first conducted at the individual level, followed by grand averaging to preserve key neurophysiological features. 3. Results 3.1 Behavioral Results Behavioral data from the Sustained Attention to Response Task (SART) showed that the mean reaction time for on-task periods ( M ± SD = 879.32 ± 351.54 ms) was significantly shorter compared to mind-wandering periods (1574.70 ± 1108.11 ms), suggesting that MW negatively affects response efficiency. A paired-samples t-test was conducted to further investigate the disparities in RTs between cognitive states. The results showed a significant difference between mind-wandering and on-task periods ( t ( 30 ) = 3.548, p = 0.001), suggesting that cognitive processing is significantly delayed during episodes of mind-wandering. A principal component regression (PCR) model was built to investigate the predictive link between EEG features and behavioral outcomes. Given the presence of multicollinearity among predictors, principal component analysis (PCA) was employed to extract the first eight components, accounting for 86.04% of the total variance (based on the Kaiser criterion). The regression results revealed a significant association between EEG features and RTs ( R² = 0.516), and this relationship was further confirmed by an analysis of variance ( F ( 8 , 19 ) = 2.537, p = 0.046), indicating the overall validity of the model. Furthermore, a one-way ANOVA was conducted to assess RT differences across the four shift conditions, revealing a marginally significant group effect ( p = 0.052). Post hoc comparisons using the least significant difference (LSD) method showed significant differences between the Day-Pre and Day-Post groups ( p = 0.008), as well as between the Day-Post and Night-Post groups ( p = 0.017), suggesting that both daytime fatigue and circadian disruption may contribute to variations in cognitive performance. 3.2 ERP Time-Domain Analysis We compared responses across four experimental groups (Day-Pre, Day-Post, Night-Pre, and Night-Post) to examine the effects of shift conditions on the N2 component during mind-wandering periods in the SART. As illustrated in Fig. 4 (A), no significant N2 components were observed in either the left or right frontal regions in the Day-Pre group (left: M = 1.05 ± 1.78; right: M = 10.68 ± 7.21). In contrast, the Night-Pre group exhibited a significant N2 component in the left frontal region ( M = − 1.40 ± 1.76), while no significant activity was noted in the right frontal region ( M = 0.49 ± 1.70). Following shift completion, both the Day-Post and Night-Post groups showed robust N2 responses, with notable differences in amplitude between the two groups in both the left ( M = − 6.91 ± 2.67 vs. −4.03 ± 2.18) and right frontal areas (M = − 2.07 ± 2.88 vs. −3.18 ± 2.00). A repeated-measures ANOVA was conducted to assess between-group differences during error trials, with shift condition as the between-subjects factor and electrode location as the within-subjects factor. This analysis involved a 4 (shift condition) × 12 (electrode site) design. Results indicated a significant main effect of shift condition on N2 amplitude (F = 1399.616, p < 0.001), confirming the critical influence of shift work on the neural mechanisms underlying mind-wandering. Bonferroni-corrected pairwise comparisons further clarified intergroup differences. In the left frontal region, electrode AF3 showed significant differences across all four groups (all p 0.05), nor between the Night-Pre and Night-Post groups ( p = 0.302), while all other comparisons reached significance (all p 0.05) and between Night-Pre and Night-Post ( p = 0.102) were not significant, whereas all other group comparisons were statistically significant (all p < 0.01). Electrode FC5 showed no significant difference between Day-Pre and Night-Pre groups ( p = 0.301), but significant differences were observed in all other pairings (all p 0.05), but all other comparisons were significant (all p 0.05), nor between Night-Pre and Night-Post ( p > 0.05), while all remaining comparisons were significant (all p 0.05), or between Night-Pre and Night-Post (p > 0.05), but significant differences emerged across the remaining pairs (all p < 0.01). Finally, electrode AF4 exhibited significant differences across all four groups (all p < 0.01). 3.3 ERP Time–Frequency Analysis Time–frequency characteristics in the N2 time window were examined for theta (θ), alpha (α), and beta (β) frequency bands during mind-wandering periods. Shift condition (Day-Pre, Day-Post, Night-Pre, Night-Post) was defined as the between-subjects factor, and electrode site as the within-subjects factor. For θ-band activity, a 4 (shift) × 2 (electrodes: AF3, AF4) repeated-measures ANOVA was performed. For α-band activity, a 4 × 4 ANOVA was conducted on O1, O2, P7, and P8. For β-band activity, a 4 × 6 ANOVA was applied to F3, F4, FC5, FC6, P7, and P8. Average values are illustrated in Fig. 5 . Between-subjects analysis revealed a significant main effect of shift condition on θ-band power ( F = 4950.274, p < 0.001), indicating that shift work systematically modulated frontal midline θ activity. Bonferroni-corrected pairwise comparisons further revealed four key differences: ( 1 ) θ power significantly decreased at AF3 and AF4 after the day shift compared to before (Day-Post vs. Day-Pre, p < 0.001); ( 2 ) in contrast, θ power significantly increased after the night shift compared to before (Night-Post vs. Night-Pre, p < 0.001); ( 3 ) baseline θ activity was significantly lower in the Night-Pre group compared to the Day-Pre group across both electrodes ( p < 0.001); and ( 4 ) a significant increase in θ power was observed in the Night-Post group compared to the Day-Post group at both sites ( p < 0.001). For the alpha (α) band, between-subject analysis revealed a significant main effect of shift condition ( F = 958.057, p < 0.001), indicating that shift work substantially modulated posterior α-band activity. Bonferroni-corrected pairwise comparisons identified the following significant differences: ( 1 ) In the Day-Post versus Day-Pre comparison, α power significantly increased at P7 ( p < 0.001) but decreased at O2 and P8 ( p 0.05); ( 2 ) In the Night-Post versus Night-Pre comparison, α power decreased significantly across all four electrodes (O1, O2, P7, P8; all p < 0.001); ( 3 ) Comparing Day-Pre and Night-Pre conditions, α power was significantly reduced at O1 and O2 ( p = 0.002, p = 0.026, respectively), and also decreased significantly at O2 and P8 (both p < 0.001), showing a consistent downward trend; ( 4 ) In the Day-Post versus Night-Post comparison, all four electrodes exhibited significant reductions in α power (all p < 0.001). For the beta (β) band, between-subject analysis also revealed a significant main effect of shift condition ( F = 2133.253, p < 0.001), suggesting that β-band activity was strongly modulated by shift work. Pairwise comparisons clarified the following effects: ( 1 ) In the Day-Post versus Day-Pre comparison, all electrodes except P8 showed significant changes ( p < 0.001), with β activity decreasing at F3, FC5, FC6, and F4, and increasing at P7. No significant change was observed at P8 ( p = 0.096); ( 2 ) In the Night-Post versus Night-Pre comparison, all electrodes showed significant differences, including F4 ( p = 0.023) and others ( p < 0.001). However, the direction of change varied, with most sites (F3, P8, FC6, F4) showing increased β activity; ( 3 ) Comparing Day-Pre and Night-Pre conditions, β power significantly decreased across all six electrodes (all p < 0.001); ( 4 ) In the Day-Post versus Night-Post comparison, significant differences were observed at F3 ( p = 0.031) and all remaining electrodes ( p < 0.001), with β activity decreasing at FC5, P7, FC6, and F4. 4. Discussion Previous research has suggested that shift work may elevate the frequency of mind-wandering by depleting attentional resources. However, the specific neuroregulatory pathways through which shift schedules influence mind-wandering under high-risk operational contexts remain insufficiently explored. This study recruited on-site oilfield drilling workers and employed a sustained attention to response task (SART) to systematically investigate the dynamic neural representation of mind-wandering as modulated by shift conditions. Behavioral findings revealed that shift work significantly prolonged participants' reaction times, supporting the close relationship between attention regulation and the occurrence of mind-wandering. Complementary time-domain and event-related spectral perturbation analyses identified distinctive neural signatures under different shift conditions, thereby providing novel empirical evidence to elucidate the neural regulatory mechanisms of cognitive function in high-risk occupational environments. Specifically, the behavioral results demonstrated a significant difference in reaction time between mind-wandering and non-mind-wandering periods during the SART, with the former showing substantially longer response latency. Existing studies have established that both reaction time and accuracy serve as valid behavioral markers for identifying mind-wandering (Ke et al., 2021). Our findings further confirm that prolonged reaction time reliably captures the onset of mind-wandering episodes. Moreover, principal component regression analyses revealed a significant linear relationship between EEG features and reaction time, whereas no such correlation was found with accuracy. This suggests a robust coupling between electrophysiological activity and behavioral performance during mind-wandering (Jana ༆ Aron, 2022). In the ERP time-domain analysis, the N2 component was absent during MW periods in both the pre-day-shift and pre-night-shift groups, but was clearly observed in the post-day-shift and post-night-shift groups. Notably, the N2 amplitude was significantly more negative in the post-day-shift group compared to the post-night-shift group. As a classic negative ERP component, the N2 (200–350 ms post-stimulus) is strongly associated with cognitive conflict monitoring. For example, in GO/NO-GO paradigms, enhanced N2 amplitude in the anterior cingulate cortex (ACC) reflects activation of the inhibitory control system (Haciahmet et al., 2023), while in Stroop tasks, N2 dynamics have been linked to the allocation of cognitive resources under conditions of response competition (Conte et al., 2023). From the perspective of attentional regulation, the neural generators of the N2 component are closely associated with the functional coupling of the fronto-parietal attention network (Chan et al., 2020). Empirical evidence shows that discriminating target stimuli evokes specific N2 amplitude changes in the prefrontal cortex, a finding clinically validated in studies of executive deficits in individuals with Attention Deficit Hyperactivity Disorder(ADHD) (Chen et al., 2021). The significant N2 negativity observed during post-shift mind-wandering trials likely reflects altered dynamics in attentional resource allocation under fatigue. When attention is misallocated, the brain becomes more sensitive to task-irrelevant distractors, resulting in increased N2 amplitude (Zickerick et al., 2020). This can be interpreted as requiring additional cognitive effort to suppress interference, reflecting a failure to efficiently focus on task-relevant targets (Folstein & Van Petten, 2008). Theta-band oscillations (4–7 Hz) are well established as being functionally coupled with the dynamic allocation of attentional resources during cognitive tasks (Liegel et al., 2022). Research indicates that theta activity significantly increases during tasks requiring high attention or cognitive load, reflecting dynamic resource integration and interregional information processing (Griggs et al., 2023). In this study, theta activity was analyzed at the prefrontal AF3 and AF4 electrodes (per the 10–20 system), as these sites play key roles in attentional resource distribution and executive function (Chen et al., 2023). Within the N2 time window, post-night-shift theta activity at AF3 and AF4 was significantly higher than in both the pre-night-shift and post-day-shift groups. This suggests that night-shift work disrupts circadian rhythms and necessitates additional attentional resources to meet sustained cognitive demands and restore performance balance (Vlasak et al., 2022). Conversely, post-day-shift theta activity at AF3 and AF4 was significantly lower than in the pre-day-shift group, potentially reflecting daytime fatigue and reduced cognitive workload, possibly aided by natural circadian recovery. Alpha-band activity plays a pivotal role in modulating visual attention, particularly in the activation of task-relevant regions and suppression of task-irrelevant ones (Jensen & Mazaheri, 2010). Accordingly, time–frequency analysis focused on the parieto-occipital electrodes P7, P8, O1, and O2, which cover cortical areas critically involved in visual processing and spatial attention (Thut et al., 2006). The results revealed significant reductions in alpha activity both post-shift (e.g., pre- vs. post-day shift, pre- vs. post-night shift) and across shift types (e.g., night vs. day shifts) in parietal and occipital regions. Such reductions in alpha activity are commonly interpreted as markers of increased cognitive load (Puma et al., 2018; Clements et al., 2021). The persistent alpha suppression observed even after task completion suggests that accumulated cognitive load during work may not be fully alleviated post-shift. This effect was more pronounced following night shifts, likely due to circadian misalignment, which impedes recovery and further suppresses alpha activity. These findings indicate that both shift work and circadian disruption impose additional strain on the brain’s recovery capacity, requiring prolonged recalibration of the attentional system to maintain cognitive performance. In the analysis of beta-band activity, six electrodes—F3, F4, FC5, FC6, P7, and P8—were selected to represent the prefrontal and parietal cortices, regions integral to attention regulation and cognitive processing. The prefrontal cortex is widely recognized as a central hub for attentional control (Kam et al., 2021; Paneri & Gregoriou, 2017), while the parietal regions are closely associated with perceptual integration and relaxation responses (Lee et al., 2023). Across both post-shift conditions and shift-type comparisons, significant reductions in beta activity were observed. Decreases in beta activity are typically indicative of attentional disengagement or transition into more relaxed states (Chaddad et al., 2023), both of which are conducive to increased mind-wandering episodes. Drawing on the empirical findings of this study, we offer the following implications for managerial practice in high-risk industries: ( 1 ) Optimize shift scheduling: Night shifts and prolonged work periods significantly elevate the likelihood of mind-wandering by exhausting cognitive resources and impeding attentional recovery. Managers should design shift rotations in alignment with circadian and cognitive recovery cycles, incorporating longer rest intervals to mitigate the adverse cognitive impacts of shift work. ( 2 ) Implement EEG-based safety monitoring: EEG provides real-time insights into workers’ cognitive states, enabling timely detection of mental fatigue and mind-wandering. The integration of wearable EEG devices in high-risk environments could allow for dynamic monitoring and early warning systems to prompt attentional refocusing or work suspension when neural indicators of risk are detected. ( 3 ) Adapt task design to enhance worker engagement: Mind-wandering and cognitive load are closely linked to task monotony and challenge levels. To maintain attentional engagement, managers should introduce variation and structured task-switching—especially following extended high-intensity work periods—to prevent cognitive fatigue and sustain work efficiency. Declarations Conflicts of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics approval The study protocol adhered to the ethical standards outlined in the Declaration of Helsinki and was approved by the Academic Committee of Southwest Petroleum University. Consent to participate Written informed consent was obtained from each participant. Consent for publication Consent for publication has been obtained Funding sources This work was supported by Sichuan Science and Technology Program (2024NSFSC2047), National Social Science Fund of China (23FJYB039). Author Contribution Su Hao, Qing Xin, and Cai Hongbin contributed equally to this work and are considered co-first authors. Methodology: Su Hao, Qing Xin, Cai Hongbin, Wang Xiaoqin, Wang Jian, Liu Lu; Software: Su Hao, Qing Xin, Cai Hongbin; Experiment Organization: Su Hao, Qing Xin, Cai Hongbin, Wang Xiaoqin, Wang Jian, Liu Lu; Data Curation and Formal Analysis: Su Hao, Qing Xin, Cai Hongbin; Writing – Original Draft: Su Hao, Qing Xin, Cai Hongbin; All authors have read and approved the final manuscript. Data Availability Statistical data are available from the corresponding author upon reasonable request. References Alanazi FI, Al-Ozzi TM, Kalia SK et al (2021) Neurophysiological responses of globus pallidus internus during the auditory oddball task in Parkinson's disease[J]. Neurobiol Dis 159:105490 Benson C, Argyropoulos CD, Dimopoulos C, Mikellidou CV, Boustras G (2021) Safety and risk analysis in digitalized process operations warning of possible deviating conditions in the process environment. 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Occup Environ Med 79(6):365–372 Walker HE, K, Trick LM (2018) Mind-wandering while driving: The impact of fatigue, task length, and sustained attention abilities[J]. Transp Res part F: traffic Psychol Behav 59:81–97 Zickerick B, Thönes S, Kobald SO et al (2020) Differential effects of interruptions and distractions on working memory processes in an ERP study[J]. Front Hum Neurosci 14:84 Additional Declarations No competing interests reported. Supplementary Files data.rar Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7839905","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":532512857,"identity":"80ba28b9-fe0b-4a57-a4df-a36b86cd5c75","order_by":0,"name":"Qing Xin","email":"","orcid":"","institution":"Southwest Petroleum University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Xin","suffix":""},{"id":532512858,"identity":"7199cde5-b8e6-4cc4-b115-9d3e03ac7329","order_by":1,"name":"Su 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2","display":"","copyAsset":false,"role":"figure","size":532550,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExperimental procedures across groups.\u003c/strong\u003e (A) Preprocessed EEG data. (B) ERP analysis procedure. (C) Time–frequency analysis procedure.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7839905/v1/9532a12377335aebd623e3e7.png"},{"id":94589866,"identity":"bd041745-9b7a-421b-876f-0d642218388e","added_by":"auto","created_at":"2025-10-28 18:20:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":279663,"visible":true,"origin":"","legend":"\u003cp\u003e(A) PCA prior to linear regression, with components selected based on a cumulative variance explanation exceeding 85%. (B) Comparison of reaction times between correct and error phases.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7839905/v1/2ab992f0f28f6f7f712e3c7c.png"},{"id":94589047,"identity":"bb74c3d9-9d98-4803-8568-0ebb7246bc74","added_by":"auto","created_at":"2025-10-28 18:20:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":891474,"visible":true,"origin":"","legend":"\u003cp\u003e(A) N2 waveforms for the four shift groups, showing no significant N2 amplitude in the Night-Pre group. (B) Boxplot comparison of mean amplitudes within the N2 time window.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7839905/v1/cf2045ff7673ec93087109d3.png"},{"id":94590249,"identity":"05816eb2-6720-491b-9286-39dae195f1f6","added_by":"auto","created_at":"2025-10-28 18:21:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":347338,"visible":true,"origin":"","legend":"\u003cp\u003eMean decibel (dB) values of EEG activity in the θ, α, and β frequency bands across their corresponding electrodes, along with the associated topographic maps.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7839905/v1/6edbb967f77a9eed89121034.png"},{"id":95526291,"identity":"b72b7434-cd50-43b0-86d7-933b2991f27a","added_by":"auto","created_at":"2025-11-10 10:06:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3220820,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7839905/v1/3b3cba0e-d293-449c-b5b9-f68533e19624.pdf"},{"id":94590281,"identity":"d0d69612-6e62-4b22-a72a-546094b771da","added_by":"auto","created_at":"2025-10-28 18:21:03","extension":"rar","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2791525,"visible":true,"origin":"","legend":"","description":"","filename":"data.rar","url":"https://assets-eu.researchsquare.com/files/rs-7839905/v1/2efd9bafc39aa89a487714fc.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Impact of Shift Work on Mind-Wandering and Neurocognitive Mechanisms in Drilling Crews","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOperational safety in high-risk industries is critically dependent on the stability of workers\u0026rsquo; cognitive states. Sectors such as mining, chemical processing, nuclear energy, and oil and gas drilling\u0026mdash;characterized by environmental complexity and operational hazards\u0026mdash;have long faced persistent challenges posed by human error (Benson et al., 2021). Shift work, which often disrupts circadian rhythms and leads to the accumulation of cognitive load, further exacerbates these risks. Among these industries, oil and gas drilling stands out due to its dynamic work environments, complex equipment systems, and severe consequences in the event of accidents. According to statistical reports, the industry has experienced multiple major incidents, resulting in hundreds of fatalities, extensive environmental damage, and billions of dollars in economic losses (Tamim et al., 2019). Accident investigations indicate that such events are typically the result of a confluence of multiple contributing factors. For instance, both the 2010 Deepwater Horizon disaster in the Gulf of Mexico and the 2015 Gunashli oil platform fire in Azerbaijan revealed a cascading chain of systemic management failures and human operational errors (Tamim et al., 2019). In most cases, human factors play a decisive role. Heinrich's classical \"domino theory\" posits that unsafe behaviors are the initiating triggers of accidents, capable of inducing equipment failures or exacerbating hazardous conditions. These events form a negative feedback loop\u0026mdash;cognitive lapses, equipment malfunction, and environmental degradation\u0026mdash;that systematically amplify operational risks. Within this context, attentional lapses have emerged as a key causal factor in unsafe behavior, thereby becoming a critical leverage point in safety management across high-risk domains (Thomson \u0026amp; Smilek, 2022).\u003c/p\u003e\u003cp\u003eIn cognitive science, the concept of \"Mind-Wandering\"(MW) was first systematically introduced by Smallwood et al. (2006) to describe task-unrelated thoughts or mental imagery, which typically result in varying degrees of attentional disengagement. This conceptual framework laid the theoretical foundation for subsequent research on attentional control mechanisms. Empirical studies by Seli et al. (2016) confirmed that frequent mind-wandering significantly impairs task performance, as evidenced by prolonged reaction times and decreased accuracy\u0026mdash;marking one of the earliest demonstrations of the behavioral consequences of mind-wandering. Subsequent studies by Leszczynski (2017) and by Walker and Trick (2018) further advanced the field by identifying neurophysiological correlates of mind-wandering. However, two critical limitations remain in the current literature. First, the predominant reliance on laboratory-based paradigms limits the ecological validity of findings, as these studies often fail to capture the complexity of cognitive states in real-world operational settings. Second, although prior studies have confirmed associations between mind-wandering and factors such as fatigue and task load, the dynamic mechanisms through which shift work influences mind-wandering remain underexplored.\u003c/p\u003e\u003cp\u003eFatigue and work duration have been consistently identified as primary drivers of mind-wandering. Research by C\u0026aacute;rdenas-Eg\u0026uacute;squiza and Berntsen (2022) demonstrated that self-reported sleep disturbances\u0026mdash;including poor sleep quality, heightened insomnia symptoms, excessive daytime sleepiness, and evening chronotypes\u0026mdash;as well as experimentally induced sleep deprivation, all significantly increase the propensity for mind-wandering. Neurophysiological evidence further suggests that mind-wandering shares overlapping cognitive pathways with drowsiness, as indicated by decreased blink rates and increased variability in pupil diameter (Stawarczyk et al., 2020). The duration of task engagement also plays a crucial role. In continuous task paradigms, Walker and Trick (2018) observed that individuals\u0026rsquo; capacity to sustain attention deteriorates over time, resulting in a higher frequency of mind-wandering.\u003c/p\u003e\u003cp\u003eElectroencephalography (EEG), with its millisecond-level temporal resolution, provides a powerful tool for objectively capturing fluctuations in attentional states during task performance, thereby offering a technical advantage in the measurement of mind-wandering (Simor et al., 2025). The advancement of portable EEG technology has further enhanced the ecological validity of neuroscience research by enabling real-time monitoring of cognitive activity under complex, real-world working conditions. Existing literature indicates that mind-wandering can be characterized through both event-related potentials (ERP) and oscillatory features observed in time-frequency analysis.\u003c/p\u003e\u003cp\u003eEvidence from ERP-based investigations has shown that specific components are reliably associated with mind-wandering. For instance, Gon\u0026ccedil;alves et al. (2018), employing the Attention Network Task (ANT), identified significant relationships between mind-wandering and both N1 and P3 amplitudes. Building on this, Liu et al. (2021) distinguished subtypes of mind-wandering and found that self-referential episodes were accompanied by increased reaction times, heightened P2 amplitudes, and reduced cognitive control, whereas stimulus-driven mind-wandering was marked by a significant decrease in P300 amplitude, indicating deeper disruptions in higher-order processing. Moreover, enhanced N2 amplitudes have been interpreted as a sign of increased sensitivity to task-irrelevant stimuli, reflecting a diversion of attentional resources from goal-relevant information (Zickerick et al., 2020).\u003c/p\u003e\u003cp\u003eComplementary findings from spectral analyses further underscore the neural underpinnings of mind-wandering. Decreases in alpha (α), theta (θ), and beta (β) power, along with reduced phase synchrony in the prefrontal cortex, have been consistently observed during episodes of mind-wandering, suggesting a decoupling of task-relevant neural systems (Bozhilova et al., 2022). Nevertheless, some research has reported that theta power may also increase under high cognitive load, potentially reflecting compensatory recruitment of cognitive resources (Liegel et al., 2022; Griggs et al., 2023). Reductions in beta activity\u0026mdash;often associated with cognitive control and attentional engagement\u0026mdash;have been linked to attentional disengagement or transition into a relaxed state, both of which are conducive to the onset of mind-wandering (Chaddad et al., 2023).\u003c/p\u003e\u003cp\u003eGiven the increasingly recognized role of attentional lapses in industrial accidents and the limited ecological validity of prior laboratory-based studies, this research aims to explore the neurocognitive mechanisms by which shift work contributes to mind-wandering in real-world, high-risk operational settings. Building on empirical and theoretical foundations, we hypothesize that shift work\u0026mdash;through its disruption of circadian regulation and accumulation of mental fatigue\u0026mdash;modulates both behavioral indicators and electrophysiological correlates of mind-wandering. Specifically, we anticipate that workers undergoing shift rotations will exhibit altered event-related potentials, particularly in the N2 component associated with conflict monitoring, as well as frequency-specific changes in theta, alpha, and beta oscillations indicative of cognitive control and attentional resource allocation. To empirically test these hypotheses, we implemented a lab-in-the-field experimental protocol that integrates the Sustained Attention to Response Task (SART) with portable EEG recordings among professional drilling crews in active operational contexts. This study thus seeks to advance current understanding of how shift-related neurocognitive fluctuations can be quantified and leveraged to inform cognitive safety monitoring and risk mitigation strategies in high-risk industries.\u003c/p\u003e"},{"header":"2. Experimental Design","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Equipment and Participants\u003c/h2\u003e\u003cp\u003eElectroencephalographic (EEG) data were collected using the wireless Emotiv Epoc\u0026thinsp;+\u0026thinsp;system (14 channels, 128 Hz sampling rate), with electrode impedance maintained below 10 kΩ to ensure signal quality (Ke et al., 2021). The experimental task was programmed using E-Prime 3.0, enabling the synchronized recording of behavioral data (reaction time and accuracy) and EEG signals. Error trials were used as behavioral markers of mind-wandering.\u003c/p\u003e\u003cp\u003eFifty male drilling operators (age range: 26\u0026ndash;57 years, M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u0026thinsp;=\u0026thinsp;38.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2) were recruited to participate in a Sustained Attention to Response Task (SART) (Robertson et al., 1997) conducted in an actual drilling environment. All participants were employed at a deep shale gas drilling platform in southwest China, with at least three years of industry experience and active involvement in rotational shift work over the preceding six months. Eligibility criteria included right-handedness, normal or corrected-to-normal vision, and no self-reported history of neurological or psychiatric disorders or substance dependence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Experimental Procedure\u003c/h2\u003e\u003cp\u003eHigh-risk industries, characterized by complex operational environments, high-density risk factors, and severe accident consequences, have long been a critical focus of safety management. Accidents in these sectors often involve large-scale economic loss, irreversible ecological impact, and sudden casualties. As a representative high-risk industry, the oil and gas industry exhibits particularly pronounced risk features. During routine drilling operations, workers are frequently required to identify unprotected openings at the worksite\u0026mdash;an essential behavior for preventing incidents such as falls from height and struck-by-object injuries. Therefore, the present experiment was designed to simulate realistic operational scenarios and assess workers\u0026rsquo; ability to detect such hazards under cognitively demanding conditions, thereby identifying potential cognitive biases and latent safety risks.\u003c/p\u003e\u003cp\u003eThe experimental paradigm was based on the Sustained Attention to Response Task (SART), initially developed by Robertson et al. (1997). Due to its monotonous structure and repetitive nature, this task is well-suited to elicit task-unrelated thoughts, which can induce boredom, diminished motivation, and reduced perceived accomplishment\u0026mdash;thus making it a widely adopted paradigm in the study of mind-wandering.\u003c/p\u003e\u003cp\u003eExperimental stimuli consisted of 30 high-resolution images (1920\u0026times;1080) derived from actual oil and gas drilling environments. Standard stimuli (no hazard present) and deviant stimuli (images containing unprotected openings) were presented at a 7:3 ratio. The deviant stimuli were further categorized into three risk types: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) derrick-edge fall hazards (40%), (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) passageway fall hazards (35%), and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) dropped-object hazards at the wellbore (25%). Participants were instructed to respond within 500 ms after image onset by pressing a key to indicate the presence and type of risk (keys 1\u0026ndash;3 corresponded to the three hazard categories, while key \u0026ldquo;0\u0026rdquo; was used for no-risk images). To increase the likelihood of eliciting mind-wandering, interstimulus intervals were set at 1500\u0026thinsp;\u0026plusmn;\u0026thinsp;200 ms, as suggested by Alanazi et al. (2021). The overall task procedure is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eBefore the formal experiment, participants watched a video tutorial that provided detailed task instructions. This was followed by five rounds of practice trials to ensure that they fully understood the task requirements. During the main session, each participant completed 30 image-based trials and received monetary compensation upon completion to reinforce task engagement and enhance ecological validity.\u003c/p\u003e\u003cp\u003eFor the purpose of maintaining scientific rigor and reliability in subsequent data analyses, the study utilized a behavioral criterion to identify episodes of mind-wandering. Based on the findings of Ke et al. (2021), error periods in the SART task were treated as behavioral markers of mind-wandering, as participants typically fail to effectively extract and process task-relevant information during such lapses. Therefore, the present research focused primarily on these error trials as indicators of mind-wandering states, and used them to explore the potential mechanisms through which shift work may influence cognitive stability, providing behavioral-level evidence for the underlying effects.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 EEG Data Acquisition and Preprocessing\u003c/h2\u003e\u003cp\u003eRaw EEG data were preprocessed using EEGLAB 2021 running on the MATLAB R2021a platform. Given that the EMOTIV Epoc\u0026thinsp;+\u0026thinsp;system employs a common-mode reference design, re-referencing of the raw signals was not required. The preprocessing pipeline included channel localization, application of a 0.05\u0026ndash;40 Hz band-pass filter, and artifact removal using independent component analysis (ICA) to eliminate non-neural artifacts such as ocular and muscular interference. A manual artifact rejection step was subsequently performed using a 60.0% rejection threshold to ensure data integrity.\u003c/p\u003e\u003cp\u003eFollowing artifact rejection, EEG epochs were extracted using stimulus onset as the time-locking point (0 ms), with a window from \u0026minus;\u0026thinsp;200 ms to 800 ms. Baseline correction was performed using the \u0026minus;\u0026thinsp;200 to 0 ms pre-stimulus interval. Event-related potentials (ERPs) were then computed by averaging across valid trials for each participant to obtain individual-level ERP waveforms. Finally, group-level ERPs were generated for the four experimental groups by computing the grand average across individuals, thereby reducing inter-individual variability.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Event-Related Potential (ERP) Time-Domain Analysis\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(B), event-related potential (ERP) time-domain analysis is a widely used method to capture neural activity in response to specific stimuli (e.g., visual or motor responses) within a defined temporal window. ERP enhances the stability of EEG signals by averaging across multiple trials time-locked to the stimulus, thereby isolating event-specific neural responses (Mouraux \u0026amp; Iannetti, 2008). The ERP signal is calculated using the following formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:ERP\\left(t\\right)=\\frac{1}{N}\\sum\\:_{i=1}^{N}{EEG}_{i}\\left(t\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:N\\)\u003c/span\u003e\u003c/span\u003e denotes the total number of trials, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{EEG}_{i}\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e represents the EEG signal recorded at time point \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e during the \u003cem\u003en\u003c/em\u003e-th trial. Among various ERP components, the N2 waveform has been frequently associated with cognitive control and response inhibition. Typically emerging between 200 and 350 ms post-stimulus, N2 is particularly sensitive to attentional conflict and the need for cognitive regulation. Existing studies suggest that N2 amplitude increases significantly during episodes of mind-wandering, reflecting heightened neural demands for conflict monitoring due to attentional lapses. Sustained attention tasks that require continuous goal maintenance tend to elicit stronger N2 responses, particularly under conditions requiring suppression of incorrect or irrelevant responses. Therefore, N2 modulation has been widely recognized as a neurophysiological marker of mind-wandering.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Time\u0026ndash;Frequency Analysis of ERP Signals\u003c/h2\u003e\u003cp\u003eMany key features of EEG signals cannot be fully captured through purely time-domain or frequency-domain analysis, as critical information is often embedded in transient oscillatory patterns within specific frequency ranges. In order to overcome this limitation, time\u0026ndash;frequency analysis is commonly utilized to uncover the dynamic features of EEG signals. Among these methods, continuous wavelet transform (CWT) is one of the most commonly employed techniques, as it allows multiscale decomposition of the signal, enabling simultaneous examination of both temporal and spectral components.\u003c/p\u003e\u003cp\u003eCWT achieves adaptive time\u0026ndash;frequency resolution by dynamically adjusting the analysis window, providing high temporal resolution for high-frequency components and high frequency resolution for low-frequency components. This property makes it particularly suitable for tracking complex oscillatory dynamics and extracting features from EEG data in cognitive neuroscience studies.\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e(C), the flexible resolution and adaptability of CWT are implemented through specific wavelet basis functions. Common choices include Haar and Morlet wavelets. Among them, the Morlet wavelet is widely adopted due to its superior smoothness and balanced resolution in both time and frequency domains. It is especially effective for isolating oscillatory components across different frequency bands in EEG analysis.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\psi\\:\\left(t\\right)={e}^{-\\frac{{t}^{2}}{2{\\sigma\\:}^{2}}}{e}^{j2\\pi\\:\\omega\\:t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ethe parameter \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\omega\\:}\\)\u003c/span\u003e\u003c/span\u003e represents the central frequency of the Morlet wavelet, which determines the wavelet's localization in the frequency domain. The parameter \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}\\)\u003c/span\u003e\u003c/span\u003e controls the width of the Gaussian kernel and thereby affects the time\u0026ndash;frequency resolution trade-off. The scale parameter \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}\\)\u003c/span\u003e\u003c/span\u003e governs dilation or compression of the wavelet, enabling analysis across different frequency bands, while the translation parameter determines the wavelet\u0026rsquo;s temporal position, allowing for the extraction of instantaneous frequency characteristics at any given time point. The complete continuous wavelet transform (CWT) of a signal \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:x\\left(t\\right)\\)\u003c/span\u003e\u003c/span\u003e is mathematically expressed as the convolution of the signal with a set of time\u0026ndash;frequency localized wavelet functions:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:X\\left(t,\\alpha\\:\\right)=CWT\\left\\{x\\left(t\\right)\\right\\}={\\int\\:}_{-\\infty\\:}^{+\\infty\\:}\\frac{1}{\\sqrt{\\alpha\\:}}x\\left(\\tau\\:\\right)\\psi\\:\\left(\\frac{1}{\\alpha\\:}(\\tau\\:-t)\\right)d\\tau\\:$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eHere, the squared modulus \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left|X\\left(t,\\alpha\\:\\right)\\right|}^{2}\\)\u003c/span\u003e\u003c/span\u003e is referred to as the wavelet power spectrum, which reflects the energy distribution of the signal across time and frequency domains. In wavelet analysis, the central frequency is selected as a reference for defining the wavelet\u0026rsquo;s scale and shift parameters. In the present study, the central frequency of the Morlet wavelet was set to 1 Hz, with a corresponding temporal resolution of 3 seconds to ensure an appropriate balance between time and frequency precision. Once defined, each wavelet's frequency-dependent resolution is determined by this reference value. The temporal precision of the wavelet increases with its central frequency, while frequency resolution improves for lower frequencies. Since ERP signals are time-locked but not phase-locked to the stimulus, direct averaging in the time domain may obscure valid neural responses due to signal cancellation. Therefore, time\u0026ndash;frequency analysis was first conducted at the individual level, followed by grand averaging to preserve key neurophysiological features.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Behavioral Results\u003c/h2\u003e\u003cp\u003eBehavioral data from the Sustained Attention to Response Task (SART) showed that the mean reaction time for on-task periods (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cem\u003eSD\u003c/em\u003e\u0026thinsp;=\u0026thinsp;879.32\u0026thinsp;\u0026plusmn;\u0026thinsp;351.54 ms) was significantly shorter compared to mind-wandering periods (1574.70\u0026thinsp;\u0026plusmn;\u0026thinsp;1108.11 ms), suggesting that MW negatively affects response efficiency. A paired-samples t-test was conducted to further investigate the disparities in RTs between cognitive states. The results showed a significant difference between mind-wandering and on-task periods (\u003cem\u003et\u003c/em\u003e(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;3.548, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), suggesting that cognitive processing is significantly delayed during episodes of mind-wandering.\u003c/p\u003e\u003cp\u003eA principal component regression (PCR) model was built to investigate the predictive link between EEG features and behavioral outcomes. Given the presence of multicollinearity among predictors, principal component analysis (PCA) was employed to extract the first eight components, accounting for 86.04% of the total variance (based on the Kaiser criterion). The regression results revealed a significant association between EEG features and RTs (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.516), and this relationship was further confirmed by an analysis of variance (\u003cem\u003eF\u003c/em\u003e(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u0026thinsp;=\u0026thinsp;2.537, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046), indicating the overall validity of the model.\u003c/p\u003e\u003cp\u003eFurthermore, a one-way ANOVA was conducted to assess RT differences across the four shift conditions, revealing a marginally significant group effect (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.052). Post hoc comparisons using the least significant difference (LSD) method showed significant differences between the Day-Pre and Day-Post groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008), as well as between the Day-Post and Night-Post groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.017), suggesting that both daytime fatigue and circadian disruption may contribute to variations in cognitive performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.2 ERP Time-Domain Analysis\u003c/h2\u003e\u003cp\u003eWe compared responses across four experimental groups (Day-Pre, Day-Post, Night-Pre, and Night-Post) to examine the effects of shift conditions on the N2 component during mind-wandering periods in the SART. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e(A), no significant N2 components were observed in either the left or right frontal regions in the Day-Pre group (left: \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.05\u0026thinsp;\u0026plusmn;\u0026thinsp;1.78; right: \u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;10.68\u0026thinsp;\u0026plusmn;\u0026thinsp;7.21). In contrast, the Night-Pre group exhibited a significant N2 component in the left frontal region (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;1.76), while no significant activity was noted in the right frontal region (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.70). Following shift completion, both the Day-Post and Night-Post groups showed robust N2 responses, with notable differences in amplitude between the two groups in both the left (\u003cem\u003eM\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;6.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.67 vs. \u0026minus;4.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.18) and right frontal areas (M\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;2.88 vs. \u0026minus;3.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.00).\u003c/p\u003e\u003cp\u003eA repeated-measures ANOVA was conducted to assess between-group differences during error trials, with shift condition as the between-subjects factor and electrode location as the within-subjects factor. This analysis involved a 4 (shift condition) \u0026times; 12 (electrode site) design. Results indicated a significant main effect of shift condition on N2 amplitude (F\u0026thinsp;=\u0026thinsp;1399.616, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), confirming the critical influence of shift work on the neural mechanisms underlying mind-wandering.\u003c/p\u003e\u003cp\u003eBonferroni-corrected pairwise comparisons further clarified intergroup differences. In the left frontal region, electrode AF3 showed significant differences across all four groups (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). For electrode F7, no significant differences were observed between the Day-Pre and Night-Pre groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), nor between the Night-Pre and Night-Post groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.302), while all other comparisons reached significance (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Similarly, for electrode F3, comparisons between Day-Pre and Night-Pre (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and between Night-Pre and Night-Post (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.102) were not significant, whereas all other group comparisons were statistically significant (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Electrode FC5 showed no significant difference between Day-Pre and Night-Pre groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.301), but significant differences were observed in all other pairings (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003eIn the right frontal region, electrode FC6 showed no significant difference between Day-Post and Night-Post groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but all other comparisons were significant (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Electrode F4 did not differ significantly between Day-Pre and Night-Pre groups (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), nor between Night-Pre and Night-Post (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), while all remaining comparisons were significant (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). For electrode F8, no significant differences were found between Day-Post and Night-Post (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), or between Night-Pre and Night-Post (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), but significant differences emerged across the remaining pairs (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Finally, electrode AF4 exhibited significant differences across all four groups (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.3 ERP Time\u0026ndash;Frequency Analysis\u003c/h2\u003e\u003cp\u003eTime\u0026ndash;frequency characteristics in the N2 time window were examined for theta (θ), alpha (α), and beta (β) frequency bands during mind-wandering periods. Shift condition (Day-Pre, Day-Post, Night-Pre, Night-Post) was defined as the between-subjects factor, and electrode site as the within-subjects factor. For θ-band activity, a 4 (shift) \u0026times; 2 (electrodes: AF3, AF4) repeated-measures ANOVA was performed. For α-band activity, a 4 \u0026times; 4 ANOVA was conducted on O1, O2, P7, and P8. For β-band activity, a 4 \u0026times; 6 ANOVA was applied to F3, F4, FC5, FC6, P7, and P8. Average values are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eBetween-subjects analysis revealed a significant main effect of shift condition on θ-band power (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4950.274, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that shift work systematically modulated frontal midline θ activity. Bonferroni-corrected pairwise comparisons further revealed four key differences: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) θ power significantly decreased at AF3 and AF4 after the day shift compared to before (Day-Post vs. Day-Pre, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) in contrast, θ power significantly increased after the night shift compared to before (Night-Post vs. Night-Pre, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) baseline θ activity was significantly lower in the Night-Pre group compared to the Day-Pre group across both electrodes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) a significant increase in θ power was observed in the Night-Post group compared to the Day-Post group at both sites (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eFor the alpha (α) band, between-subject analysis revealed a significant main effect of shift condition (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;958.057, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that shift work substantially modulated posterior α-band activity. Bonferroni-corrected pairwise comparisons identified the following significant differences: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) In the Day-Post versus Day-Pre comparison, α power significantly increased at P7 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but decreased at O2 and P8 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with no significant change at O1 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) In the Night-Post versus Night-Pre comparison, α power decreased significantly across all four electrodes (O1, O2, P7, P8; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Comparing Day-Pre and Night-Pre conditions, α power was significantly reduced at O1 and O2 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, respectively), and also decreased significantly at O2 and P8 (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), showing a consistent downward trend; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) In the Day-Post versus Night-Post comparison, all four electrodes exhibited significant reductions in α power (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eFor the beta (β) band, between-subject analysis also revealed a significant main effect of shift condition (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2133.253, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that β-band activity was strongly modulated by shift work. Pairwise comparisons clarified the following effects: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) In the Day-Post versus Day-Pre comparison, all electrodes except P8 showed significant changes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with β activity decreasing at F3, FC5, FC6, and F4, and increasing at P7. No significant change was observed at P8 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.096); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) In the Night-Post versus Night-Pre comparison, all electrodes showed significant differences, including F4 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) and others (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the direction of change varied, with most sites (F3, P8, FC6, F4) showing increased β activity; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Comparing Day-Pre and Night-Pre conditions, β power significantly decreased across all six electrodes (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) In the Day-Post versus Night-Post comparison, significant differences were observed at F3 (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031) and all remaining electrodes (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with β activity decreasing at FC5, P7, FC6, and F4.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePrevious research has suggested that shift work may elevate the frequency of mind-wandering by depleting attentional resources. However, the specific neuroregulatory pathways through which shift schedules influence mind-wandering under high-risk operational contexts remain insufficiently explored. This study recruited on-site oilfield drilling workers and employed a sustained attention to response task (SART) to systematically investigate the dynamic neural representation of mind-wandering as modulated by shift conditions. Behavioral findings revealed that shift work significantly prolonged participants' reaction times, supporting the close relationship between attention regulation and the occurrence of mind-wandering. Complementary time-domain and event-related spectral perturbation analyses identified distinctive neural signatures under different shift conditions, thereby providing novel empirical evidence to elucidate the neural regulatory mechanisms of cognitive function in high-risk occupational environments.\u003c/p\u003e\u003cp\u003eSpecifically, the behavioral results demonstrated a significant difference in reaction time between mind-wandering and non-mind-wandering periods during the SART, with the former showing substantially longer response latency. Existing studies have established that both reaction time and accuracy serve as valid behavioral markers for identifying mind-wandering (Ke et al., 2021). Our findings further confirm that prolonged reaction time reliably captures the onset of mind-wandering episodes. Moreover, principal component regression analyses revealed a significant linear relationship between EEG features and reaction time, whereas no such correlation was found with accuracy. This suggests a robust coupling between electrophysiological activity and behavioral performance during mind-wandering (Jana ༆ Aron, 2022).\u003c/p\u003e\u003cp\u003eIn the ERP time-domain analysis, the N2 component was absent during MW periods in both the pre-day-shift and pre-night-shift groups, but was clearly observed in the post-day-shift and post-night-shift groups. Notably, the N2 amplitude was significantly more negative in the post-day-shift group compared to the post-night-shift group. As a classic negative ERP component, the N2 (200\u0026ndash;350 ms post-stimulus) is strongly associated with cognitive conflict monitoring. For example, in GO/NO-GO paradigms, enhanced N2 amplitude in the anterior cingulate cortex (ACC) reflects activation of the inhibitory control system (Haciahmet et al., 2023), while in Stroop tasks, N2 dynamics have been linked to the allocation of cognitive resources under conditions of response competition (Conte et al., 2023).\u003c/p\u003e\u003cp\u003eFrom the perspective of attentional regulation, the neural generators of the N2 component are closely associated with the functional coupling of the fronto-parietal attention network (Chan et al., 2020). Empirical evidence shows that discriminating target stimuli evokes specific N2 amplitude changes in the prefrontal cortex, a finding clinically validated in studies of executive deficits in individuals with Attention Deficit Hyperactivity Disorder(ADHD) (Chen et al., 2021). The significant N2 negativity observed during post-shift mind-wandering trials likely reflects altered dynamics in attentional resource allocation under fatigue. When attention is misallocated, the brain becomes more sensitive to task-irrelevant distractors, resulting in increased N2 amplitude (Zickerick et al., 2020). This can be interpreted as requiring additional cognitive effort to suppress interference, reflecting a failure to efficiently focus on task-relevant targets (Folstein \u0026amp; Van Petten, 2008).\u003c/p\u003e\u003cp\u003eTheta-band oscillations (4\u0026ndash;7 Hz) are well established as being functionally coupled with the dynamic allocation of attentional resources during cognitive tasks (Liegel et al., 2022). Research indicates that theta activity significantly increases during tasks requiring high attention or cognitive load, reflecting dynamic resource integration and interregional information processing (Griggs et al., 2023). In this study, theta activity was analyzed at the prefrontal AF3 and AF4 electrodes (per the 10\u0026ndash;20 system), as these sites play key roles in attentional resource distribution and executive function (Chen et al., 2023). Within the N2 time window, post-night-shift theta activity at AF3 and AF4 was significantly higher than in both the pre-night-shift and post-day-shift groups. This suggests that night-shift work disrupts circadian rhythms and necessitates additional attentional resources to meet sustained cognitive demands and restore performance balance (Vlasak et al., 2022). Conversely, post-day-shift theta activity at AF3 and AF4 was significantly lower than in the pre-day-shift group, potentially reflecting daytime fatigue and reduced cognitive workload, possibly aided by natural circadian recovery.\u003c/p\u003e\u003cp\u003eAlpha-band activity plays a pivotal role in modulating visual attention, particularly in the activation of task-relevant regions and suppression of task-irrelevant ones (Jensen \u0026amp; Mazaheri, 2010). Accordingly, time\u0026ndash;frequency analysis focused on the parieto-occipital electrodes P7, P8, O1, and O2, which cover cortical areas critically involved in visual processing and spatial attention (Thut et al., 2006). The results revealed significant reductions in alpha activity both post-shift (e.g., pre- vs. post-day shift, pre- vs. post-night shift) and across shift types (e.g., night vs. day shifts) in parietal and occipital regions. Such reductions in alpha activity are commonly interpreted as markers of increased cognitive load (Puma et al., 2018; Clements et al., 2021). The persistent alpha suppression observed even after task completion suggests that accumulated cognitive load during work may not be fully alleviated post-shift. This effect was more pronounced following night shifts, likely due to circadian misalignment, which impedes recovery and further suppresses alpha activity. These findings indicate that both shift work and circadian disruption impose additional strain on the brain\u0026rsquo;s recovery capacity, requiring prolonged recalibration of the attentional system to maintain cognitive performance.\u003c/p\u003e\u003cp\u003eIn the analysis of beta-band activity, six electrodes\u0026mdash;F3, F4, FC5, FC6, P7, and P8\u0026mdash;were selected to represent the prefrontal and parietal cortices, regions integral to attention regulation and cognitive processing. The prefrontal cortex is widely recognized as a central hub for attentional control (Kam et al., 2021; Paneri \u0026amp; Gregoriou, 2017), while the parietal regions are closely associated with perceptual integration and relaxation responses (Lee et al., 2023). Across both post-shift conditions and shift-type comparisons, significant reductions in beta activity were observed. Decreases in beta activity are typically indicative of attentional disengagement or transition into more relaxed states (Chaddad et al., 2023), both of which are conducive to increased mind-wandering episodes.\u003c/p\u003e\u003cp\u003eDrawing on the empirical findings of this study, we offer the following implications for managerial practice in high-risk industries: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Optimize shift scheduling: Night shifts and prolonged work periods significantly elevate the likelihood of mind-wandering by exhausting cognitive resources and impeding attentional recovery. Managers should design shift rotations in alignment with circadian and cognitive recovery cycles, incorporating longer rest intervals to mitigate the adverse cognitive impacts of shift work. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Implement EEG-based safety monitoring: EEG provides real-time insights into workers\u0026rsquo; cognitive states, enabling timely detection of mental fatigue and mind-wandering. The integration of wearable EEG devices in high-risk environments could allow for dynamic monitoring and early warning systems to prompt attentional refocusing or work suspension when neural indicators of risk are detected. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) Adapt task design to enhance worker engagement: Mind-wandering and cognitive load are closely linked to task monotony and challenge levels. To maintain attentional engagement, managers should introduce variation and structured task-switching\u0026mdash;especially following extended high-intensity work periods\u0026mdash;to prevent cognitive fatigue and sustain work efficiency.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflicts of interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cp\u003e The study protocol adhered to the ethical standards outlined in the Declaration of Helsinki and was approved by the Academic Committee of Southwest Petroleum University.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cp\u003e Written informed consent was obtained from each participant.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003e Consent for publication has been obtained\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding sources\u003c/h2\u003e\u003cp\u003eThis work was supported by Sichuan Science and Technology Program (2024NSFSC2047), National Social Science Fund of China (23FJYB039).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSu Hao, Qing Xin, and Cai Hongbin contributed equally to this work and are considered co-first authors. Methodology: Su Hao, Qing Xin, Cai Hongbin, Wang Xiaoqin, Wang Jian, Liu Lu; Software: Su Hao, Qing Xin, Cai Hongbin; Experiment Organization: Su Hao, Qing Xin, Cai Hongbin, Wang Xiaoqin, Wang Jian, Liu Lu; Data Curation and Formal Analysis: Su Hao, Qing Xin, Cai Hongbin; Writing \u0026ndash; Original Draft: Su Hao, Qing Xin, Cai Hongbin; All authors have read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eStatistical data are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlanazi FI, Al-Ozzi TM, Kalia SK et al (2021) Neurophysiological responses of globus pallidus internus during the auditory oddball task in Parkinson's disease[J]. 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Front Hum Neurosci 14:84\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"High-risk industries, Shift Work, Mind-Wandering, Time-Domain Analysis, Time-Frequency Analysis","lastPublishedDoi":"10.21203/rs.3.rs-7839905/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7839905/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe stability of cognitive functioning among frontline personnel plays a pivotal role in ensuring operational safety within high-risk industries; nevertheless, the neurocognitive mechanisms through which shift work disrupts attentional control and induces mind-wandering (MW) remain insufficiently understood, particularly under conditions involving prolonged mental load and circadian misalignment. This study utilized a lab-in-the-field experiment with the Sustained Attention to Response Task (SART) and wearable electroencephalography (EEG) technology to investigate the effects of shift work on mind-wandering. The results revealed a significant coupling between behavioral performance and EEG signals. Time-domain analysis revealed that the pre-shift group did not show a distinct N2 component during mind-wandering periods, while the post-shift group displayed a notable increase in N2, indicating enhanced conflict monitoring and cognitive resource allocation efficiency following shift work. Time-domain analysis showed that the pre-shift group lacked a distinct N2 component during mind-wandering periods, whereas the post-shift group demonstrated a noticeable increase in N2, indicating enhanced conflict monitoring and cognitive resource allocation efficiency following shift work. These findings uncover the neurocognitive pathway through which shift work induces mind-wandering, highlighting the N2 component as a key marker of impaired attentional regulation, and offer empirical evidence to support neurophysiological risk monitoring in high-risk operational settings.\u003c/p\u003e","manuscriptTitle":"The Impact of Shift Work on Mind-Wandering and Neurocognitive Mechanisms in Drilling Crews","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-28 16:42:30","doi":"10.21203/rs.3.rs-7839905/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28db821a-264e-4187-9830-a754ff1851d6","owner":[],"postedDate":"October 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-07T12:09:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-28 16:42:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7839905","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7839905","identity":"rs-7839905","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}