Error Monitoring Research Articles

The balance N1 and the ERN correlate in amplitude across individuals in small samples of younger and older adults.

The error-related negativity (ERN) is a neural correlate of error monitoring often used to investigate individual differences in developmental, mental health, and adaptive contexts. However, limited experimental control over errors presents several confounds to its measurement. An experimentally controlled disturbance to standing balance evokes the balance N1, which we previously suggested may share underlying mechanisms with the ERN based on a number of shared features and factors. We now measure whether the balance N1 and ERN are correlated across individuals within two small groups (N = 21 young adults and N = 20 older adults). ERNs were measured in arrow flanker tasks using hand and foot response modalities (ERN-hand and ERN-foot). The balance N1 was evoked by sudden slip-like movements of the floor while standing. The ERNs and the balance N1 showed good and excellent internal consistency, respectively, and were correlated in amplitude in both groups. One principal component strongly loaded on all three evoked potentials, suggesting that the majority of individual differences are shared across the three ERPs. However, there remains a significant component of variance shared between the ERN-hand and ERN-foot beyond what they share with the balance N1. It is unclear whether this component of variance is specific to the arrow flanker task, or something fundamentally related to error processing that is not evoked by a sudden balance disturbance. If the balance N1 were to reflect error processing mechanisms indexed by the ERN, balance paradigms offer several advantages in terms of experimental control over errors.

Cortico-Cerebellar Monitoring of Speech Sequence Production.

In a functional magnetic resonance imaging study, we examined speech error monitoring in a cortico-cerebellar network for two contrasts: (a) correct trials with high versus low articulatory error probability and (b) overtly committed errors versus correct trials. Engagement of the cognitive cerebellar region Crus I in both contrasts suggests that this region is involved in overarching performance monitoring. The activation of cerebellar motor regions (superior medial cerebellum, lobules VI and VIII) indicates the additional presence of a sensorimotor driven implementation of control. The combined pattern of pre-supplementary motor area (active across contrasts) and anterior cingulate cortex (only active in the contrast involving overt errors) activations suggests sensorimotor driven feedback monitoring in the medial frontal cortex, making use of proprioception and auditory feedback through overt errors. Differential temporal and parietal cortex activation across contrasts indicates involvement beyond sensorimotor driven feedback in line with speech production models that link these regions to auditory target processing and internal modeling-like mechanisms. These results highlight the presence of multiple, possibly hierarchically interdependent, mechanisms that support the optimizing of speech production.

A correction algorithm for improved magnetic field monitoring with distal field probes.

A significant source of artifacts in MRI are field fluctuations. Field monitoring is a new technology that allows measurement of field dynamics during a scan via "field probes," which can be used to improve image reconstruction. Ideally, probes are located within the volume where gradients produce nominally linear field patterns. However, in some situations probes must be located far from isocenter where rapid field variation can arise, leading to erroneous field-monitoring characterizations and images. This work aimed to develop an algorithm that improves the robustness of field dynamics in these situations. The algorithm is split into three components. Component 1 calculates field dynamics one spatial order at a time, whereas the second implements a weighted least squares solution based on probe distance. Component 3 then calculates phase residuals and removes the residual phase for distant probes before recalculation. Two volunteers and a phantom were scanned on a 7T MRI using diffusion-weighted sequences, and field monitoring was performed. Image reconstructions were informed with field dynamics calculated conventionally, and with the correction algorithm, after which in vivo images were compared qualitatively and phantom image error was quantitatively assessed. The algorithm was able to correct corrupted field dynamics, resulting in image-quality improvements. Significant artifact reduction was observed when correcting higher-order fits. Stepwise fitting provided the most correction benefit, which was marginally improved when adding the other correction strategies. The proposed algorithm can mitigate effects of phase errors in field monitoring, providing improved characterization of field dynamics.

Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling

When sampling for Bayesian inference, one popular approach in the computational field is to use Hamiltonian Monte Carlo (HMC) and specifically the No-U-Turn Sampler (NUTS), which automatically decides the end time of the Hamiltonian trajectory. However, HMC and NUTS can require numerous numerical gradients of the target density, and can prove slow in practice when relying on computationally expensive forward models. We propose Latent Hamiltonian neural networks (L-HNNs) with HMC and NUTS for solving Bayesian inference problems. Once trained, L-HNNs do not require numerical gradients of the target density during sampling, and hence numerous evaluations of the forward computational model. Moreover, L-HNNs satisfy important properties such as perfect time reversibility and Hamiltonian conservation, making them well-suited for use within HMC and NUTS because stationarity can be shown. We also propose the integration of L-HNNs in an online error monitoring scheme, in which numerical gradients of the target density are used for a few samples whenever the L-HNNs prediction errors are large. This online error monitor scheme prevents sample degeneracy in regions of low probability density and ensures robust uncertainty quantification. We demonstrate L-HNNs in NUTS with online error monitoring on several analytical examples involving complex, heavy-tailed, and high-local-curvature probability densities. We then demonstrate the applicability of L-HNNs in NUTS to two computational case studies, namely the Allen-Cahn stochastic partial differential equation and an elliptic partial differential equation with 25 and 50 inference parameters, respectively. Overall, the L-HNNs in NUTS with online error monitoring satisfactorily inferred these probability densities. Compared to traditional NUTS, L-HNNs in NUTS with online error monitoring required 1–2 orders of magnitude fewer numerical gradients of the target density and improved the effective sample size (ESS) per gradient (which is a measure of both the sampling quality and the computational expense) by an order of magnitude.

Attack-Resistant, Energy-Adaptive Monitoring for Smart Farms: Uncertainty-Aware Deep Reinforcement Learning Approach

This work proposes an energy-adaptive monitoring system for a smart farm using solar sensors attached to cows. The proposed system aims to achieve a high monitoring quality in the smart farm under fluctuating energy and cyber attacks disrupting the collection of sensed data from solar sensors, such as protocol non-compliance, false data injection, denial-of-service, and state manipulation. We adopt Subjective Logic, a belief model, to consider multidimensional uncertainty in sensed data. We employ Deep Reinforcement Learning (DRL) for agents on gateways to collect high-quality sensed data from the solar sensors. The DRL agents aim to collect high-quality sensed data with low uncertainty and high freshness under fluctuating energy levels in solar sensors. We analyze the performance of the proposed energy-adaptive smart farm system in accumulated reward, monitoring error rate, and system overload. We conduct a comparative performance analysis of the uncertainty-aware DRL algorithms against their counterparts in choosing the number of sensed data to be updated to collect high-quality sensed data to achieve high resilience against attacks. Our results prove that Multi-Agent Proximal Policy Optimization (MAPPO) using the uncertainty maximization technique outperforms other counterparts, showing about 4% lower monitoring error rate and the system overload.

Nanogenerator based self-powered motion monitoring for flapping wings of bio-inspired flying robots

Under the constraints of weight and power, achieving airborne monitoring of wings' flexible motion toward bio-inspired flying robots is an arduous challenge. Herein, we present a self-powered motion monitoring method based on nanogenerators to tackle this issue. First, a locally adaptable integration structure of triboelectric nanogenerator (TENG) integrated wings is proposed for the design of airborne devices. Second, a theoretical output model is developed to dynamically monitor the flapping motion of TENG-integrated wings. The proposed approach is a multi-target monitoring technique that enables the sensing of parameters, such as the flapping frequency and the flapping angles with stability. After validation, the monitoring error of the wing plane's pitch angle affected by device stability is less than 0.7°. Likewise, the maximum observed error rate for flapping frequency monitoring is about 0.1%. This technique will further enhance the intelligent airborne wing state perception for bio-inspired flying robots.

A serious web game for children with attentive disorders: design and experiences from two trials

ABSTRACT Cognitive developmental disorders are common in children and can affect various abilities. Attention Deficit/Hyperactivity Disorder (ADHD) is the most prevalent childhood psychiatric condition. This work presents PlayToPickUp, a serious game that aims to stimulate children in relevant cognitive domains (attention and error monitoring). A multidisciplinary team of experts and caregivers from two different centers that support therapeutic activities with such children participated from the games’s inception to the design and the evaluation of the game. Depending on the characteristics and abilities of the player, therapists can customize the game to provide training that best fits the skills and the needs of the child while maintaining the player’s motivation. After its development, the game was used over 2 months “in the wild” by children recruited by the two centers. In one case the children played with it within the regular activities offered by the training center. In the other one, the parents of the children were instructed by caregivers to have the children play the game at home. In the paper, we describe the experience gathered from such two studies run in parallel, discussing the aspects that worked better, those that represented difficulties, and the lesson learnt for future studies.

Cognitive correlates of auditory hallucinations in schizophrenia spectrum disorders

Auditory hallucinations (AHs) are one of the central symptoms of schizophrenia spectrum disorders (SSD). Current cognitive models of AH implicate source monitoring, top-down processes, and inhibitory control. However, research combining these processes is limited. Our study aimed to examine how source monitoring bias, top-down processes, and inhibitory control contribute to AHs in individuals with SSD. Eighty seven patients (aged 18–45 years) with SSD were included in the analyses. Participants completed cognitive tasks assessing source monitoring (Action Memory Task), top-down processes (False Perception Task; FPT), and inhibitory control (Auditory Go/NoGo task). AH was positively associated with response bias on the FPT. Correlations between AH and the other cognitive tasks were nonsignificant. Source monitoring errors correlated positively with response bias measures and negatively with Hits on the FPT. PANSS total score was positively correlated with source monitoring bias and False Alarms on the Go/NoGo task. The severity of disorganized symptoms was related to Source Monitoring Errors and False Alarms in the Go/NoGo task. Negative symptoms were associated with Hits and False Alarms in the Go/NoGo task.Future studies are necessary to further elucidate the relationships between different cognitive processes that may be related to clinical symptoms of psychosis.

Design and Performance Analysis of Monitoring System for Seed Metering and Fertilization of Precision Seeder Based on Photoelectric Sensor

In order to realize accurate monitoring of wheat sowing and fertilization process and ensure efficient and reliable sowing operation, an integrated monitoring system of wheat sowing and fertilization based on variable-distance photoelectric sensor is designed. The monitoring system includes the hardware circuit of STM32F103 lower computer and the human-machine interface of the upper computer touch screen. The hardware circuit of the lower computer is composed of OH-1021 photoelectric sensor, signal shaping and amplifying circuit, encoder speed acquisition module, communication module, central processor, and peripheral circuits. The information of seed fertilizer flow and seed fertilizer shaft rotation is obtained through the reflective photoelectric sensor and rotary encoder respectively to judge the running state of sowing. The information is transmitted to the human-machine interface of the upper computer touch screen of MCGS by means of Modbus communication protocol. In the experimental species, the single seed monitoring accuracy of the monitoring system reaches 98.6%. Compared with the test results of the performance test bench of the seed metering device, the monitoring error of the monitoring system is less than 0.3%, the monitoring error of the replay rate is less than 0.6%, and the monitoring accuracy of the seeding amount is greater than 94%. The simulation results of the seed metering monitoring circuit of the lower computer show that the detection distance of the amplifier circuit to the photoelectric sensor of the seed tube changes by [4 mm, 7 mm]. The communication test results of the upper and lower computers show that the accuracy of data transmission reaches 100%, the monitoring system shows that the accuracy of fault detection exceeds 90%, and the response time of lacking, blocking, and leaking is less than 0.3 s. The monitoring system realizes high-precision monitoring of wheat seed metering and fertilization, which is helpful to improve the quality of wheat planting.

Electrocortical activity correlated with locomotor adaptation during split-belt treadmill walking.

Locomotor adaptation is crucial for daily gait adjustments to changing environmental demands and obstacle avoidance. Mobile brain imaging with high-density electroencephalography (EEG) now permits quantification of electrocortical dynamics during human locomotion. To determine the brain areas involved in human locomotor adaptation, we recorded high-density EEG from healthy, young adults during split-belt treadmill walking. We incorporated a dual-electrode EEG system and neck electromyography to decrease motion and muscle artefacts. Voluntary movement preparation and execution have been linked to alpha (8-13Hz) and beta band (13-30Hz) desynchronizations in the sensorimotor and posterior parietal cortices, whereas theta band (4-7Hz) modulations in the anterior cingulate have been correlated with movement error monitoring. We hypothesized that relative to normal walking, split-belt walking would elicit: (1) decreases in alpha and beta band power in sensorimotor and posterior parietal cortices, reflecting enhanced motor flexibility; and (2) increases in theta band power in anterior cingulate cortex, reflecting instability and balance errors that will diminish with practice. We found electrocortical activity in multiple regions that was associated with stages of gait adaptation. Data indicated that sensorimotor and posterior parietal cortices had decreased alpha and beta band spectral power during early adaptation to split-belt treadmill walking that gradually returned to pre-adaptation levels by the end of the adaptation period. Our findings emphasize that multiple brain areas are involved in adjusting gait under changing environmental demands during human walking. Future studies could use these findings on healthy, young participants to identify dysfunctional supraspinal mechanisms that may be impairing gait adaptation. KEY POINTS: Identifying the location and time course of electrical changes in the brain correlating with gait adaptation increases our understanding of brain function and provides targets for brain stimulation interventions. Using high-density EEG in combination with 3D biomechanics, we found changes in neural oscillations localized near the sensorimotor, posterior parietal and cingulate cortices during split-belt treadmill adaptation. These findings suggest that multiple cortical mechanisms may be associated with locomotor adaptation, and their temporal dynamics can be quantified using mobile EEG. Results from this study can serve as a reference model to examine brain dynamics in individuals with movement disorders that cause gait asymmetry and reduced gait adaptation.

Vierordt’s Law for Space and Time: Hollingworth (1909) and “The Law of Central Tendency”

Abstract This article discusses material in a 1909 monograph, The Inaccuracy of Movement with Special Reference to Constant Errors, by H. L. Hollingworth, which is mostly concerned with demonstrating and accounting for what we would now call Vierordt’s Law, for judgements of both length and time. Hollingworth demonstrates Vierordt’s Law for a length reproduction task, and shows that the length which is reproduced most accurately (the indifference point) varies as a function of the range of the lengths presented. Hollingworth attributes this effect to the influence of the central tendency of the stimuli presented. He notes that this effect could be found in previous studies of time interval reproduction, but was not remarked upon. A second study also introduced error monitoring for length and time judgements. Hollingworth’s ideas echo much more recent approaches to Vierordt’s Law, some of which also use the idea that the Vierordt effect is based on the central tendency of the stimulus range from which the stimuli to be judged come.

Effect of near-surface winds on the measurement of forest soil CO2 fluxes using closed air chambers

Forest soil CO2 flux measurements are important for studying global climate change. Current monitoring methods are based on closed gas chambers, which block the wind pumping effect of near-surface winds in the measurements, resulting in biased values. Therefore, in this study, the effects of near-surface winds on chamber-monitored fluxes were investigated. The CO2 flux was quantified using a designed flux reference system with different CO2 concentrations, and the monitoring performance of the closed chamber was studied. Wavelet coherence was used to analyze the response relationship between near-surface winds and soil gas, and was combined with a flux calculation model to explore the relevant factors influencing gas chamber measurement-produced bias. The data indicate that at near-surface wind speeds greater than 0.8 m·s−1, gas transport enhancement was significant and further increased the deviation of the gas chamber-monitored CO2 fluxes. The monitoring error of the flow chamber (NSF) increased from 7% to 30% in soils with low carbon content, but did not vary significantly (3–7%) in soils with high CO2 concentrations. The flux measurement bias of the non-flow chamber (NSNF) was positively correlated with the soil carbon content, with the measurement error expanding by 16–24% with increasing soil CO2 concentrations. The measurement errors of the exponential and linear models in a windless environment were 9.8% (Exp) and 18.7% (Lin), respectively. The estimation errors of both models were positively correlated with both the time of a single monitoring event and the wind-induced coefficient Dw. Therefore, flux calculation models should be improved by considering environments with wind disturbances to reduce the effect of wind on measured values, which will help improve the accuracy of ecosystem carbon budgets.

To err is human- to understand error-processing is divine: Contributions of working memory and anxiety to error-related brain and pupil responses.

Both anxiety and working memory capacity appear to predict increased (more negative) error-related negativity (ERN) amplitudes, despite being inversely related to one another. Until the interactive effects of these variables on the ERN are clarified, there may be challenges posed to our ability to use the ERN as an endophenotype for anxiety, as some have suggested. The compensatory error monitoring hypothesis suggests that high trait-anxiety individuals have larger ERN amplitudes because they must employ extra, compensatory efforts to override the working memory demands of their anxiety. Yet, to our knowledge, no ERN study has employed direct manipulation of working memory demands in conjunction with direct manipulations of induced (state) anxiety. Furthermore, little is known about how these manipulations affect other measures of error processing, such as the error-related pupil dilation response and post-error behavioral adjustments. Therefore, we manipulate working memory load and anxiety in a 2 × 2 within-subjects design to examine the interactive effects of working memory load and anxiety on ERN amplitude, error-related pupil dilation response amplitude, and post-error behavior. There were no effects of our manipulations on ERN amplitude, suggesting a strong interpretation of compensatory error-processing theory. However, our worry manipulation affected post-error behavior, such that worry caused a reduction in post-error accuracy. Additionally, our working memory manipulation affected error-related PDR magnitude and the amplitude of the error-related positivity (Pe), such that increased working memory load decreased the amplitude of these responses. Implications of these results within the context of the compensatory error processing framework are discussed.

A high-precision strain seeding spacing monitoring system based on a combined bionic strain sensor and strain peak recognition algorithm

This study has discovered a novel monitoring quantity suitable for seeding spacing monitoring, which is the strain signal generated by the seed flow through the seed discharge port during the operation of the finger-clip seed metering device. The strain sensor output a dataset of approximately 30 electrical signals for each flow of the seed pick-up clip of the finger-clip seed metering device through the seed discharge port. The number of peak points in this data set is equivalent to the number of seeds sown, enabling accurate judgment of missed seeding or reseeding. This study addressed the problem that field vibrations tend to cause displacement of seed, which in turn triggers a change in strain direction. Through a combined bionic design, a scorpion body surface V-shaped crack structure (ultrasensitive sensing function) and a spider-web annular topology (omnidirectional equalization sensing function) were selected as bionic prototypes to develop a combined bionic strain sensor (CBSS). The test results showed that the CBSS combines the functional characteristics of both bionic structures and was more suitable for seed spacing monitoring. Compared to the conventional straight-line V-shaped crack strain sensor (CSS), CBSS improved the omnidirectional sensitivity by 21.54–35.57%. To mitigate the impact of ground frequency noise in the system causing fluctuations in the CBSS output signal, a strain peak recognition algorithm (SPRA) was developed using image processing techniques and Python. The SPRA effectively removed ground frequency noise using a gray-scale erosion algorithm and accurately recognized peak points in the electrical signal dataset through the Suzuki and CEV algorithms. Results showed that the SPRA reduced monitoring errors by 20.52% compared to the conventional direct data processing method (DDPM). Based on the CBSS and SPRA, a strain seeding spacing monitoring system (S-SMS) was developed. Bench tests revealed that the parameters of the combined bionic annular crack structure (crack depth h) of the CBSS significantly affected the monitoring error of the S-SMS, with optimal performance achieved at the h value of 82 μm. Field tests demonstrated that the CBSS accurately monitored the strain signal triggered by each seed with 98.51% accuracy, while the SPRA recognized missed seeding and reseeding with 98.64% and 98.42% accuracy, respectively. Compared to the conventional photoelectric diffuse reflection seeding monitoring system (P-SMS), the S-SMS reduced seeding monitoring errors in the field by 3.51%. This research can provide a new research path for the development of seeding spacing monitoring systems.

Temporal Error Monitoring Does Not Depend on Working Memory.

Working memory (WM) and metacognition has been documented to be in a reciprocal relationship. This study aims to address if temporal error monitoring performance can be diminished with increased working memory load. We hypothesized that if temporal error monitoring has commonalities with perceptual error monitoring, temporal error monitoring performance should be diminished by increased working memory load. Participants completed a temporal error monitoring task in a dual task design in which the secondary task was a letter alphabetization task. Results revealed no disrupting effect of WM load on either confidence or short-long judgments as being different metrics of temporal error monitoring ability. These results demonstrate that unlike perceptual error monitoring, WM and temporal error monitoring have distinct processing mechanisms. With this result, the current study suggests that temporal and perceptual error monitoring may partially rely on different mechanisms. Results are discussed within A Theory of Magnitude (ATOM), pacemaker-accumulator model and temporal error monitoring frameworks.

Listening to Yourself and Watching Your Tongue: Distinct Abilities and Brain Regions for Monitoring Semantic and Phonological Speech Errors.

Despite the many mistakes we make while speaking, people can effectively communicate because we monitor our speech errors. However, the cognitive abilities and brain structures that support speech error monitoring are unclear. There may be different abilities and brain regions that support monitoring phonological speech errors versus monitoring semantic speech errors. We investigated speech, language, and cognitive control abilities that relate to detecting phonological and semantic speech errors in 41 individuals with aphasia who underwent detailed cognitive testing. Then, we used support vector regression lesion symptom mapping to identify brain regions supporting detection of phonological versus semantic errors in a group of 76 individuals with aphasia. The results revealed that motor speech deficits as well as lesions to the ventral motor cortex were related to reduced detection of phonological errors relative to semantic errors. Detection of semantic errors selectively related to auditory word comprehension deficits. Across all error types, poor cognitive control related to reduced detection. We conclude that monitoring of phonological and semantic errors relies on distinct cognitive abilities and brain regions. Furthermore, we identified cognitive control as a shared cognitive basis for monitoring all types of speech errors. These findings refine and expand our understanding of the neurocognitive basis of speech error monitoring.

Implementasi Sistem Pengelolaan Rekap Jual Beli pada Toko Busana

The buying and selling recap management system is a system that can help manage buying and selling transactions in a business. The aim of implementing a sales and purchase recap management system at the Fashion Store is to increase store management efficiency, facilitate financial management, monitor inventory, and reduce errors in recording transactions. The method used is in the form of observation, interviews, and data analysis to collect the necessary data. Furthermore, designing and implementing a buying and selling recap management system at the Fashion Store. This system can record buying and selling transactions automatically and generate reports on sales, purchases, and profits easily and quickly. The results of the implementation of the sales and purchase recap management system show that this system can increase store management efficiency, reduce errors in transactions, facilitate stock and financial monitoring. This system also makes it easy for users to access buying and selling transaction data and generate reports automatically. In conclusion, the implementation of a sales and purchase recap management system at Clothing Stores provides significant benefits for store management and can be applied to various types of businesses to increase management efficiency and save time and costs required in store management.

Prestress monitoring for prestress tendons based on the resonance-enhanced magnetoelastic method considering the construction process

Monitoring the prestress of prestressed concrete structures is hard but helpful. The resonance-enhanced magnetoelastic (REME) sensor could measure the stress of steel, but the measurement is influenced by the stress history. Thus, after analyzing the principle of the REME method according to the electric circuit theorem, the construction process and stress history of different prestress tendons were discussed. The prestress monitoring experiment showed that the induced voltage–prestress relationship was influenced by the stress history. To evaluate the prestress, a prestress evaluation method suitable for prestress steel strands was proposed. Using the precise calibration method, the monitoring errors in the construction stage and the prestress loss stage were less than 13.26% and 5.97%. The simplified calibration method reduced the workload of the calibration by 50% while the monitoring error increased by less than 4%. In addition, using the self-calibration method can avoid the influence of the differences in steel strands, leading to higher monitoring accuracy.

Active Risk Management Program in Operating Rooms of Hospitals in Different Countries: A Scoping Review Study

Context: Risk management in hospitals is essential for improving healthcare quality. This study analyzed the characteristics of risk management programs implemented in operating rooms of hospitals across countries worldwide. Evidence Acquisition: This study was a scoping review of online database studies, including Web of Knowledge, PubMed, Scopus, Cochrane, Springer, ProQuest, Iranian SID, and Magiran databases and the Google Scholar search engine. Three people independently performed the study selection, quality assessment, data extraction, and analysis among studies that reviewed risk management programs in health systems and those conducted outside the health system. Articles in non-English languages (including Persian) were excluded. Results: A total of 21 studies with similar purposes and data collection methods were included. The characteristics of risk management programs were classified into six main factors: Objectives, components, steps, results, prerequisites, facilitators of risk management programs, and 35 sub-factors. Conclusions: The conceptual framework of any risk management program should include at least the objectives: Risk eradication, safety promotion, quality improvement and prevention and reduction of risks, component: Communication and monitoring; steps: (1) preoperative evaluation during (logging); (2) evaluation during surgery; (3) post-surgery evaluation (logout); and the results: Achieving effective methods in reducing errors; prerequisites: Human resource, knowledge and information, and facilitators such as the use of monitoring technologies and error detection and reporting in the operating room.

Cognitive control, bedtime patterns, and testing time in female adolescent students: behavioral and neuro-electrophysiological correlates.

Shorter and/or disrupted sleep during adolescence is associated with cognitive and mental health risks, particularly in females. We explored the relationship between bedtime behavior patterns co-varying with Social Jet Lag (SJL) and School Start Times (SST) and neurocognitive performance in adolescent female students. To investigate whether time of day (morning vs. afternoon), early SSTs and days of the school week can be correlated with neurocognitive correlates of sleep insufficiency, we recruited 24 female students aged 16-18 to report sleep logs, and undergo event-related electroencephalographic recordings on Monday, Wednesday, mornings, and afternoons. Using a Stroop task paradigm, we analyzed correlations between reaction times (RTs), accuracy, time of day, day of week, electroencephalographic data, and sleep log data to understand what relationships may exist. Participants reported a 2-h sleep phase delay and SJL. Stroop interference influenced accuracy on Monday and Wednesday similarly, with better performance in the afternoon. For RTs, the afternoon advantage was much larger on Monday than Wednesday. Midline Event-Related Potentials (ERPs) yielded higher amplitudes and shorter latencies on Wednesday morning and Monday afternoon, in time windows related to attention or response execution. A notable exception were delayed ERP latencies on Wednesday afternoon. The latter could be explained by the fact that delta EEG waves tended to be the most prominent, suggesting heightened error monitoring due to accumulating mental fatigue. These findings provide insights into the interaction between SJL and SST and suggest evidence-based criteria for planning when female adolescents should engage in cognitive-heavy school activities such as tests or exams.