Error Monitoring Research Articles

Fluency shaping increases integration of the command-to-execution and the auditory-to-motor pathways in persistent developmental stuttering

Fluency-shaping enhances the speech fluency of persons who stutter, yet underlying conditions and neuroplasticity-related mechanisms are largely unknown. While speech production-related brain activity in stuttering is well studied, it is unclear whether therapy repairs networks of altered sensorimotor integration, imprecise neural timing and sequencing, faulty error monitoring, or insufficient speech planning. Here, we tested the impact of one-year fluency-shaping therapy on resting-state fMRI connectivity within sets of brain regions subserving these speech functions. We analyzed resting-state data of 22 patients who participated in a fluency-shaping program, 18 patients not participating in therapy, and 28 fluent control participants, measured one year apart. Improved fluency was accompanied by an increased connectivity within the sensorimotor integration network. Specifically, two connections were strengthened; the left inferior frontal gyrus showed increased connectivity with the precentral gyrus at the representation of the left laryngeal motor cortex, and the left inferior frontal gyrus showed increased connectivity with the right superior temporal gyrus. Thus, therapy-associated neural remediation was based on a strengthened integration of the command-to-execution pathway together with an increased auditory-to-motor coupling. Since we investigated task-free brain activity, we assume that our findings are not biased to network activity involved in compensation but represent long-term focal neuroplasticity effects.

Successful Outpatient Administration of Blinatumomab Infusion in Pediatric Patients with Acute Lymphoblastic Leukemia

Introduction:Acute lymphoblastic leukemia (ALL) is the most common type of pediatric cancer. Unfortunately, approximately 15% of children with high-risk B-cell ALL (B-ALL) relapse after frontline chemotherapy. Treatment of relapsed/refractory B-ALL is still challenging, and more effective novel therapies are urgently warranted. Blinatumomab, a first in class bispecific antibody therapeutic, has demonstrated superiority compared to standard chemotherapy in patients with B-ALL and has a manageable toxicity profile. Blinatumomab functions by binding to CD19 expressed on B-cells and CD3 expressed on T-cells, resulting in T-cell-mediated killing of CD19-positive cells common in B-cell malignancies.Despite remarkable efficacy and a manageable toxicity profile compared to standard-of-care chemotherapy, blinatumomab poses unique healthcare system challenges related to preparation, administration, toxicity monitoring, and medication error prevention. The drug's success in helping patients achieve complete remission relies on its continuous and uninterrupted administration. In order to ensure that it is delivered in the safest and most effective manner, education on its unique logistical and administration challenges is imperative.Objectives:The primary objective of this study is to describe and share the 6 years of institutional experience on the outpatient delivery of blinatumomab for the management of pediatric patients with B-ALL as per Children's Oncology Group protocols, as well as to retrospectively analyze the safety of this novel 28-day home-based therapy.Methods:A multidisciplinary team composed of physicians, nurses, and pharmacists was created to address administration challenges associated with blinatumomab infusions. Although blinatumomab requires a 28-day continuous infusion, it is not necessary for patients to remain hospitalized for the entire cycle. To ensure tolerability prior to discharge, patients are monitored closely during the first 3 days of Cycle 1 and 2 days of Cycle 2 for signs of cytokine release syndrome and neurological toxicities. Once discharged, they are seen every 96/72 hours for bag changes in either an outpatient hematology/oncology unit or by home health for those off study.Results:A total of 16 patients were treated with blinatumomab between May 2015 and June 2021; 10 were newly diagnosed and 6 were in first relapse. Of the 26 total infusions, 24 were successfully completed without significant adverse reactions. Two patients treated for relapsed disease had to discontinue therapy; one experienced neurotoxicity within 72 hours of blinatumomab infusion initiation and the other developed refractory disease and was switched to another protocol. No adverse events were observed in the home setting.Discussion:The team was successfully able to transform the original inpatient-only blinatumomab protocol to the outpatient setting. Retrospective analysis over 6 years demonstrates a clinically significant reduced rate of complications of blinatumomab administration in comparison to previous reports (Amicucci et al. 2021), which can be attributed to careful multidisciplinary team planning and delivery. This study confirms the feasibility of a home-based continuous blinatumomab infusion without adverse effects on safety. Additionally, this outpatient protocol leads to cost savings associated with reduced length of stay and an overall improved quality of life for pediatric patients able to receive therapy at home with their caregivers. DisclosuresNo relevant conflicts of interest to declare.

Centralized on-line monitoring system for pipeline and cable environment and fresh air of air conditioning

Aiming at the problem of on-line centralized monitoring error of pipeline and cable environment and fresh air of air conditioning, a utility model is designed, a centralized monitoring system of pipeline and cable environment and fresh air of air conditioning is disclosed, the system structure configuration is optimized, and the system performance is improved by combining multiple central air conditioning controllers, data processors, acquisition servers and other equipment, combined with LabVIEW technology, the system software function and operation process are improved. Finally, the experiment proves that the centralized online monitoring system for pipeline and cable environment and air conditioning fresh air can better reduce the monitoring error and ensure the real-time monitoring quality of pipeline and cable environment and air conditioning fresh air.

Motion Artifact Resilient Cuff-Less Blood Pressure Monitoring Using a Fusion of Multi-Dimensional Seismocardiograms.

Blood pressure (BP) monitoring is critical to raise awareness of hypertension and hypotension, yet the commonly used techniques require the person staying still along with a cuff around the arm. Some cuff-less approaches have been researched, but all hinder the person from moving around. To address the challenge, we propose using a fusion of accelerometers to achieve motion artifact resilient blood pressure monitoring. Such technique is accomplished with the motion artifact removal process and feature extraction from multi-dimensional seismocardiograms. The efficacy of our BP monitoring models is validated in 19 young healthy adults. Both the diastolic and systolic BP monitoring models fulfill the AAMI standard and British Hypertension Society protocol. For sitting still BP monitoring, the mean and standard deviation of diastolic and systolic difference errors (DE) are 0.09 ± 4.10 and -0.25 ± 5.45 mmHg; moreover, the mean absolute difference errors (MADE) are 3.62 and 4.73 mmHg. In walking motions, the DE are 1.15 ±4.47 mmHg for diastolic BP and -0.38 ± 6.67 for systolic BP; furthermore, the MADE are 3.36 and 5.07 mmHg, respectively. The motion artifact resilient cuff-less BP monitoring reveals the potential of portable BP monitoring in healthcare environments.

Graph Theoretic Analysis of Multilayer EEG Connectivity Networks.

Over the past twenty years, functional connectivity of the human brain has been studied in detail using tools from complex network theory. These methods include graph theoretic metrics ranging from the micro-scale such as the degree of a node to the macro-scale such as the small worldness of the brain network. However, most of these network models focus on average activity within a time window of interest and given frequency band. Therefore, they cannot capture the changes in network connectivity across time and different frequency bands. Recently, multilayer brain networks have attracted a lot of attention as they can capture the full view of neuronal connectivity. In this paper, we introduce a multilayer view of the functional connectivity network of the brain, where each layer corresponds to a different frequency band. We construct multi-frequency connectivity networks from electroencephalogram data where the intra-layer edges are quantified by phase synchrony while the inter-layer edges are quantified by phase-amplitude coupling. We then introduce multilayer degree, participation coefficient and clustering coefficient to quantify the centrality of nodes across frequency layers and to identify the importance of different frequency bands. The proposed framework is applied to electroencephalogram data collected during a study of error monitoring in the human brain.

Distributed communication power monitoring system based on big data technology

Communication system has high quality requirements for power supply. Realizing the effective monitoring of power supply system is an important measure to improve power quality. In order to better and effectively manage massive power information in complex environment and avoid communication power failure, a design method of distributed communication power monitoring system based on big data technology is proposed. The system operation performance is proposed, and the software function and operation process of the communication power monitoring system are simplified by using big data technology to improve the system monitoring accuracy. Finally, the experiment shows that the distributed communication power monitoring system based on big data technology has significantly higher response speed and relatively lower monitoring error in the actual application process.

Spectacle wear and refractive errors in Dutch children

Abstract Background Myopia is a refractive error that is increasing dramatically all over the world. Early onset is associated with a significant visual burden later in life, but little is known about refractive errors in preschool children. The aim of this study was to assess prevalence of spectacle wear, visual acuity and refractive errors in young Dutch children and to make global comparisons. Methods We analyzed data of three prospective population-based studies: 99,660 3- to 5-year-olds undergoing vision screening at preventive child healthcare organizations, 6,934 6-year-olds from the Generation R study, and 2,974 7-year-olds from the RAMSES study. Visual acuity was measured with Landolt-C or LEA charts, spectacle wear was assessed, and refractive errors at age 6 and 7 were measured with cycloplegic refraction. Spectacle wear was compared with international studies. Results The prevalence of spectacle wear was 1.5%, 2.3%, 6.6%, 8.2% and 11.8% at 36, 45, 60, 72 and 84 months, respectively, with no major sex differences. Among children with spectacle wear at 72 months (N = 583) and 84 months (N = 351) 29.8% and 34.6% had myopia respectively, of which 21.1% and 21.6% combined with astigmatism, 19.6% and 6.8% had hyperopia, 37.2% and 11.1% hyperopia and astigmatism, and 12.5% and 33.3% astigmatism only. The prevalence of spectacle wear globally varied between 1.5% to 21%. Conclusions Spectacle wear in these European children started early in preschool and increased to substantial figures at school age. Among children with spectacle wear, >30% were already myopic, illustrating the urgency to implement myopia prevention strategies in child health centers. Key messages Early onset myopia is a public health issue. Of the 6- to 7-year-olds with spectacles 30-34% were already myopic. Monitoring of refractive errors and preventive lifestyle interventions are warranted.

Human Motion Representation and Motion Pattern Recognition Based on Complex Fuzzy Theory

With the development of science and technology, the introduction of virtual reality technology has pushed the development of human-computer interaction technology to a new height. The combination of virtual reality and human-computer interaction technology has been applied more and more in military simulation, medical rehabilitation, game creation, and other fields. Action is the basis of human behavior. Among them, human behavior and action analysis is an important research direction. In human behavior and action, recognition research based on behavior and action has the characteristics of convenience, intuition, strong interaction, rich expression information, and so on. It has become the first choice of many researchers for human behavior analysis. However, human motion and motion pictures are complex objects with many ambiguous factors, which are difficult to express and process. Traditional motion recognition is usually based on two-dimensional color images, while two-dimensional RGB images are vulnerable to background disturbance, light, environment, and other factors that interfere with human target detection. In recent years, more and more researchers have begun to use fuzzy mathematics theory to identify human behaviors. The plantar pressure data under different motion modes were collected through experiments, and the current gait information was analyzed. The key gait events including toe-off and heel touch were identified by dynamic baseline monitoring. For the error monitoring of key gait events, the screen window is used to filter the repeated recognition events in a certain period of time, which greatly improves the recognition accuracy and provides important gait information for motion pattern recognition. The similarity matching is performed on each template, the correct rate of motion feature extraction is 90.2%, and the correct rate of motion pattern recognition is 96.3%, which verifies the feasibility and effectiveness of human motion recognition based on fuzzy theory. It is hoped to provide processing techniques and application examples for artificial intelligence recognition applications.

Neurocognitive development of novelty and error monitoring in children and adolescents

The abilities to monitor one’s actions and novel information in the environment are crucial for behavioural and cognitive control. This study investigated the development of error and novelty monitoring and their electrophysiological correlates by using a combined flanker with novelty-oddball task in children (7–12 years) and adolescents (14–18 years). Potential moderating influences of prenatal perturbation of steroid hormones on these performance monitoring processes were explored by comparing individuals who were prenatally exposed and who were not prenatally exposed to synthetic glucocorticoids (sGC). Generally, adolescents performed more accurately and faster than children. However, behavioural adaptations to error or novelty, as reflected in post-error or post-novelty slowing, showed different developmental patterns. Whereas post-novelty slowing could be observed in children and adolescents, error-related slowing was absent in children and was marginally significant in adolescents. Furthermore, the amplitude of error-related negativity was larger in adolescents, whereas the amplitude of novelty-related N2 was larger in children. These age differences suggest that processes involving top-down processing of task-relevant information (for instance, error monitoring) mature later than processes implicating bottom-up processing of salient novel stimuli (for instance, novelty monitoring). Prenatal exposure to sGC did not directly affect performance monitoring but initial findings suggest that it might alter brain-behaviour relation, especially for novelty monitoring.

Improvements in error-monitoring and symptoms following low-frequency rTMS of dorsal anterior cingulate cortex in obsessive compulsive disorder; a randomized, sham-controlled study

Action monitoring deficit is a core underlying characteristic and endophenotype of Obsessive Compulsive Disorder (OCD). Dorsal anterior cingulate cortex (dACC) is heavily involved in error monitoring and cognitive control, and the hyperactivity in this region is associated with OCD symptom severity. This study aimed to test whether low frequency (LF) repetitive transcranial magnetic stimulation (rTMS) targeting dACC improves both error-monitoring performance and OCD symptoms in a randomized, sham-controlled, double-blind trial design. 20 OCD patients were randomly assigned to receive 20 sessions of Active (n = 10) or Sham (n = 10) rTMS administered twice-daily. Error-monitoring performance and symptom severity were measured pre- and post-treatment using Erikson Flanker tasks and the Yale-Brown Obsessive Compulsive Scale (Y-BOCS), with three month symptom follow-up. Following active—but not sham—rTMS, patients showed improved response time for incongruent stimuli, trials following a correct response, and for reporting and correcting errors. Significant OCD symptom improvement was observed at one-month follow-up for patients who received Active (28.0% reduction) but not Sham (11.7% reduction) stimulation. In OCD patients, LF rTMS of the dACC can simultaneously improve on-line adjustment of behaviour—by enhancing the capacity for rapid error monitoring—and clinical symptoms, suggesting a link between error monitoring impairment and OCD pathophysiology.

μPMU-Based Temporal Decoupling of Parameter and Measurement Gross Error Processing in DSSE

Simultaneous real-time monitoring of measurement and parameter gross errors poses a great challenge to distribution system state estimation due to usually low measurement redundancy. This paper presents a gross error analysis framework, employing μPMUs to decouple the error analysis of measurements and parameters. When a recent measurement scan from SCADA RTUs and smart meters is available, gross error analysis of measurements is performed as a post-processing step of non-linear DSSE (NLSE). In between scans of SCADA and AMI measurements, a linear state estimator (LSE) using μPMU measurements and linearized SCADA and AMI measurements is used to detect parameter data changes caused by the operation of Volt/Var controls. For every execution of the LSE, the variance of the unsynchronized measurements is updated according to the uncertainty introduced by load dynamics, which are modeled as an Ornstein–Uhlenbeck random process. The update of variance of unsynchronized measurements can avoid the wrong detection of errors and can model the trustworthiness of outdated or obsolete data. When new SCADA and AMI measurements arrive, the LSE provides added redundancy to the NLSE through synthetic measurements. The presented framework was tested on a 13-bus test system. Test results highlight that the LSE and NLSE processes successfully work together to analyze bad data for both measurements and parameters.

Measuring the Deflection of the Vertical via Local Reference Point Surveying and Pointing Calibration of a VLBI Telescope: A Case Study at the Urumqi Station

AbstractAccording to Newton's law of universal gravitation, the movement of matter and the change in density cause a gravitational variation. Due to the anisotropic distribution of matter, the vertical and the normal directions at a point on the ground are probably inconsistent, which is referred to as the deflection of the vertical (DOV) and is indispensable to astrogeodetic and geophysics research. Two new non‐optical methods, including small network transformation and azimuth‐axis inclination inversion, are proposed to determine the DOV at a very long baseline interferometry (VLBI) telescope at the Urumqi station. A generalized expression of the pointing calibration model considering axis‐related errors is also presented. Therefore, the pointing calibration model and indirect model used for reference point (RP) determination are unified by redefining their coordinate system, angle direction, and axis‐related errors. The DOV given by the small network transformation agrees well with the DOVs determined by the real surveying and other different models. Due to the possible asymmetric coverage of calibrating sources in the north‐south direction in antenna pointing calibration, the north‐south DOV component based on azimuth‐axis inclination inversion differs from the surveyed value. However, the west‐east DOV component fits well in both direction and magnitude. The proposed methods enable the VLBI telescope to sense the direction of the local plumb line by introducing local leveling. We also establish the connections among VLBI delay observation, RP determination, and telescope pointing calibration, which has a benefit to multi‐tech systematic error identification, monitoring, and correction. The research indicates that telescope point calibration can also be taken as a regular technique to monitor systematic error.

Error analysis and experimental research of joint fiber-optic displacement sensor based on shear lag model

Landslide displacement monitoring is an efficient method to mitigate casualties and economic losses caused by landslide disasters. In recent years, distributed fiber-optic sensing technology, due to distributed and long-distance advantages, was widely used in subsurface displacement monitoring of landslides. However, existing fiber-optic displacement sensors has a large monitoring error when the landslide has multi-sliding surfaces. Aiming at solving this issue, a new joint fiber-optic displacement sensor which can achieve accurate displacement monitoring, was designed. Its measurement error was analyzed and the feasibility was demonstrated by theory and laboratory experiment. Based on the shear lag model, the error caused by strain transfer was studied by fiber-optic cable pull-out experiment. Finally, the feasibility of the sensor was verified by calibration experiment and soil shear experiment. The results show that the displacement sensor has high sensitivity and accuracy, and can realize distributed displacement monitoring inside the slope with multi-sliding surfaces.

Developmental changes in external and internal performance monitoring across middle childhood: An ERP study

Performance monitoring is critical for learning and behavioral adaption and is supported by both externally and internally sourced information. Cross-sectional studies indicate an increase in internal error processing across childhood, suggesting a potential developmental transition from reliance on external information to reliance on internally developed models. However, little research has examined the association between these constructs longitudinally. Data from 339 children assessed annually from kindergarten to 2nd grade were examined to determine the developmental trajectory of ERP indices of performance monitoring, and whether the association between these indices changes across time. EEG data were recorded during an incentivized Go/No-Go task and ERP component amplitudes were extracted as peak measures at Fz. Despite small increases in magnitude, no significant changes were observed in any of the ERPs. Multi-level regression analyses indicated that in kindergarten a more negative feedback-related negativity (FRN) was associated with a more negative error-related negativity (ERN) and a more negative error positivity (Pe). Further, the association between the FRN and Pe changed over time, such that in 2nd grade the FRN and Pe decoupled from one another and were no longer associated. These results suggest that the development of performance monitoring through middle childhood may be a phasic process. More specifically, matured external feedback monitoring processes may first facilitate the development of conscious error recognition, and then the development of internal error monitoring processes. Once internal models of error monitoring are well-established, children may then reduce their utilization of external feedback.

Precision seeding parameter monitoring system based on laser sensor and wireless serial port communication

With the development of agricultural intelligence, the degree of integration of computer technology, wireless communication technology, sensor technology, and agricultural machinery technology is getting higher and higher. Among them, the seeding parameter monitoring technology of the seeder has been more and more applied. However, the current seeding monitoring technology still has some problems, including the low accuracy of parameter monitoring, the monitoring parameters are mainly the seeding quantity, the data is communicated by wire connection, the communication distance is short, the wiring is complicated, and the scalability is poor. Aiming at the problems existing in the current seeding parameter monitoring technology, this study designed and developed a precision seeding parameter monitoring system based on laser sensors and wireless serial port communication for precision seeders. The monitoring system included the design of laser sensor, the design of controller parameter capture and calculation, the design of upper computer human–computer interaction software, the design of wireless communication methods, the design of fault alarm, and speed measurement function. Bench tests had shown that the monitoring system designed in this study had high monitoring accuracy. When the simulated seeder was at a forward speed of 5–10 km/h, the average monitoring error of the seeding quantity was less than 1.2%, and the average monitoring error of the seeding qualified rate was less than 0.5%. The monitoring system could accurately and timely alarm the fault, which the success rate of alarm reached 100%, and the average response time of the fault alarm was less than 0.5 s. The GPS speed measurement value was consistent with the simulation software value, and the speed measurement frequency was 5 Hz. Field tests had shown that the average monitoring error of the seeding quantity was less than 1%, and the average monitoring error of the seeding qualified rate was less than 1.5%. The monitoring system could alarm in time when the seeder had a missing seeds fault. Through test verification, the monitoring system designed in this study could realize the online real-time monitoring function of the precision seeder.

Executive Functions and Сonscious Self-Regulation as Predictors of Native Language Learning Success in Russian Middle School Children

Mastering the mother tongue at school is essential for both academic and life success. It depends on many factors, including self-regulation, i. e. the ability to control one’s thoughts and behaviour. In this study, we assess the influence of conscious self-regulation, executive functions (inhibition, switching, working memory updating, and error monitoring and control) and intelligence on Russian language competences and Russian language annual grade in a group of Russian middle school children. Through structural equations modelling, we found that both categories of regulatory predictors, i. e. conscious self-regulation and executive functions, are related to academic performance in the native language. Both predictors make an indirect contribution to the annual grade in the Russian language. In the case of self-regulation, fluid intelligence acts as a mediating variable. Executive functions influence the annual grade through language competences, which also significantly contribute to the general performance. Executive functions also have an impact on self-regulation, confirming their status as the neurocognitive basis of self-regulation. Of the self-regulatory functions, Goal Planning, Modelling, Results Evaluation, and regulatory personality traits of Flexibility and Initiative have the greatest impact on the native language academic performance. We revealed the effect of «excessive flexibility», that is, an increase in number of errors at high values of the executive function Switching attention. Links to recent findings as well as directions for future research are discussed. The results can be used to improve academic performance in native language and emphasize the importance of developing conscious self-regulation at school

Automated Alert Failures and their Impact on Operator Performance and Trust

Despite its rapid advancement, automation remains vulnerable to system failures. The reliability of automation may impact users’ trust and how they interact with it. Additionally, the type of error can uniquely redirect user behavior. This study investigated how reliability and error type impact operator trust and monitoring performance. Participants completed a monitoring task at either 50% or 90% reliability, experiencing either misses or false alarms from an automated alert system. It was hypothesized that automation reliability would impact trust, while error type would also impact reliance and compliance behaviors. Results indicated that misses had a greater impact on monitoring performance than false alarms, while reliability did not influence performance. Trust was not influenced by reliability or error type and showed no relationship with performance measures. These results can help further clarify the way automation failures shape how humans interact with automation and inform the design of future automated systems.

Reduction of Errors in Hydrological Drought Monitoring – A Novel Statistical Framework for Spatio-Temporal Assessment of Drought

Reduction of Errors in Hydrological Drought Monitoring – A Novel Statistical Framework for Spatio-Temporal Assessment of Drought

Intrapartum-related Perinatal Deaths in Births Planned in Midwifery-led Settings in Great Britain: Findings and Recommendations From the ESMiE Confidential Enquiry

(BJOG. 2020;127:1665–1675) The UK has higher rates of stillbirth and neonatal mortality than similar countries. A 2015 UK-wide confidential inquiry, Mothers and Babies: Reducing Risk through Audits and Confidential Enquiries across the UK (MBRRACE-UK), identified key issues contributing to perinatal death, including inadequate maternal monitoring, errors in fetal monitoring, and failure to recognize the transition from the latent phase of labor to the active phase. The majority of deaths included in MBRRACE-UK occurred in hospital obstetric units (OUs), but the proportion of births occurring in midwifery-led settings in England is increasing. While there is no evidence that midwifery-led settings experience higher rates of intrapartum-related perinatal deaths compared with OU, contributing factors to perinatal deaths in midwifery settings may differ. Enhancing the safety of Midwifery-led births Enquiry (ESMiE) reviewed the quality of care in pregnancy, labor, birth, and postpartum in births resulting in intrapartum-related deaths in midwifery-led settings and compared the findings with those of MBRRACE-UK.

Reading and Calculation Neural Systems and Their Weighted Adaptive Use for Programming Skills.

Software programming is a modern activity that poses strong challenges to the human brain. The neural mechanisms that support this novel cognitive faculty are still unknown. On the other hand, reading and calculation abilities represent slightly less recent human activities, in which neural correlates are relatively well understood. We hypothesize that calculus and reading brain networks provide joint underpinnings with distinctly weighted contributions which concern programming tasks, in particular concerning error identification. Based on a meta-analysis of the core regions involved in both reading and math and recent experimental evidence on the neural basis of programming tasks, we provide a theoretical account that integrates the role of these networks in program understanding. In this connectivity-based framework, error-monitoring processing regions in the frontal cortex influence the insula, which is a pivotal hub within the salience network, leading into efficient causal modulation of parietal networks involved in reading and mathematical operations. The core role of the anterior insula and anterior midcingulate cortex is illuminated by their relation to performance in error processing and novelty. The larger similarity that we observed between the networks underlying calculus and programming skills does not exclude a more limited but clear overlap with the reading network, albeit with differences in hemispheric lateralization when compared with prose reading. Future work should further elucidate whether other features of computer program understanding also use distinct weights of phylogenetically “older systems” for this recent human activity, based on the adjusting influence of fronto-insular networks. By unraveling the neural correlates of program understanding and bug detection, this work provides a framework to understand error monitoring in this novel complex faculty.