The editorial, authored by Juan Lupiáñez of the University of Granada, Fernando Gabriel Luna of the National University of Córdoba, and Daniel Smilek of the University of Waterloo, frames a Research Topic that gathers historical analyses, conceptual reviews, empirical studies, theoretical proposals, and methodological innovations. The editors argue that no single theoretical framework proposed so far fully accounts for the complex reality of the vigilance decrement. Instead, contemporary research is characterized by a productive debate among resource-depletion accounts, resource-control models, opportunity-cost frameworks, motivational approaches, and theories emphasizing mind wandering and attentional disengagement. Progress, they contend, increasingly depends on pairing theoretical innovation with novel analytical and methodological approaches.

The first major theme emerging from the collection concerns the nature of vigilance itself. The term is often used interchangeably with sustained attention, alertness, and arousal, sometimes referring to the same cognitive process and other times to different capacities or behaviors. Hemmerich and colleagues provide a comprehensive review that revisits these conceptual boundaries and synthesizes the theoretical, contextual, and neural foundations of vigilance. Their analysis complements a historical perspective from Klein and Feltmate, who trace how explanations of the vigilance decrement have evolved since Mackworth’s seminal studies during the Second World War era. Together, these contributions remind researchers that theoretical progress depends not only on new data but also on conceptual clarity about the cognitive processes under investigation.

The second theme concerns the mechanisms underlying the decrement. Traditional resource theories attribute performance decline to the depletion of limited attentional resources, as though the brain were a battery draining with use. Newer frameworks reject simple depletion. Resource-control models argue that a progressive failure of executive control allows attention to drift toward task-unrelated thoughts, while opportunity-cost and motivational theories view the decrement as a cost-benefit decision in which individuals continually evaluate the value of task engagement against alternative mental activities. The evidence increasingly favors these dynamic accounts over static depletion.

Studies by Drody and colleagues and by Schwartzman and colleagues provide direct evidence bearing on this debate. In sustained attention tasks, increasing motivation and interest attenuated time-related increases in media multitasking and mind wandering while also mitigating time-related decreases in performance. Rather than reflecting depleted cognitive capacity, these findings underscore the role of motivational factors and attentional priorities in sustaining prolonged engagement. Schwartzman’s team further showed that performance decline is accompanied by more frequent task-unrelated thoughts, even during short intervals, suggesting that the mind begins disengaging almost immediately after a task starts.

Extending this work into more ecological contexts, Zheng and colleagues identified distinct EEG synchronization signatures that differentiate on-task states from mind wandering during continuous performance. These neural markers demonstrate the feasibility of tracking attentional states in real time, an approach with considerable promise for both theoretical investigation and practical intervention in safety-critical settings. Shelat and colleagues showed that response-time patterns can identify impending attentional lapses and episodes of mind wandering before they fully manifest, opening the door to predictive systems that could alert operators before dangerous lapses occur.

Importantly, the editors argue that future research should expand explanations of vigilance failures to include an understanding of why the mind disengages from the task at hand. Task-unrelated thought may not simply impose performance costs; it may also serve adaptive functions, including memory consolidation and creative thinking. Recent studies suggest that mind wandering during implicit learning is associated with improved extraction of hidden probabilistic patterns, and that self-generated thought contributes to creativity. Understanding both the costs and benefits of mind wandering may therefore be essential for explaining why the vigilance decrement is so pervasive in everyday life and so hard to avoid.

Theoretical innovation in the collection extends beyond the resource versus motivation debate. In a provocative contribution, Jagacinski proposes an Adjustable Wave Model in which attentional deployment is conceptualized as a spatio-temporal sampling process operating at adjustable frequencies. Curley and colleagues combined electrophysiological measures with computational simulations to investigate interactions among working memory, cognitive control, and performance monitoring, while Matthews and colleagues highlighted challenges in measuring temporal changes in attentional networks at the individual level. These studies reflect a broader shift toward multilevel accounts linking behavior, neural activity, computational mechanisms, and individual differences.

The third major theme concerns methodological innovation. Many traditional vigilance paradigms rely on overall detection rates or aggregate performance measures that provide a general index of attention span but limited insight into the mechanisms responsible for decline. Skinner and Giesbrecht advocate broader application of signal detection theory to distinguish declines in perceptual sensitivity from shifts in decision criterion, a distinction that resonates with longstanding debates revisited by Klein and Feltmate. The field is also witnessing a recent shift from classic signal detection metrics toward psychometric curves as indices of vigilance loss, alongside ex-Gaussian modeling of reaction-time distributions. Notably, McCarley and colleagues deconstructed the vigilance decrement and found changes in bias, lapse rate, and guess rate over time, but not in perceptual sensitivity, suggesting that what looks like fading perception may actually be shifting decisions and increasing lapses.

Taken together, the contributions portray a field undergoing both conceptual refinement and methodological transformation. Rather than supporting a single explanatory framework, the collection suggests that vigilance decrements arise from the interplay of attentional control, motivational dynamics, opportunity-cost evaluations, mind wandering, environmental influences, and neurocognitive constraints. Future theoretical progress will likely depend less on identifying a single dominant explanation than on developing integrative models that capture how these factors interact across tasks, individuals, and contexts. For a phenomenon that touches nearly every prolonged human endeavor, from monitoring automated transit networks to sustaining focus in digital education, that integrated understanding cannot come soon enough.

Subject of Research: Editorial: Advances in vigilance research: exploring novel theoretical models and analytical approaches on the analysis of the vigilance decrement

Article Title: Editorial: Advances in vigilance research: exploring novel theoretical models and analytical approaches on the analysis of the vigilance decrement

Article References: Lupiáñez, J., Luna, F. G., & Smilek, D. (2026). Editorial: Advances in vigilance research: exploring novel theoretical models and analytical approaches on the analysis of the vigilance decrement. Frontiers in Cognition, 5, Article 1948924. https://doi.org/10.3389/fcogn.2026.1948924

Image Credits: AI Generated

DOI: 10.3389/fcogn.2026.1948924

Keywords: Editorial, Advances, vigilance, research, exploring, novel, theoretical, models, analytical, approaches, analysis, decrement

The research, conducted by Stefania C. Ficarella of ONERA and Aix-Marseille Université, Andrea Desantis of ONERA, and colleagues in collaboration with Airbus Operations, tackled a question that has long confounded human-factors scientists. Vigilance, the ability to stay alert and sustain attention over time, is known to decline as time-on-task increases. Task disengagement, the reduced allocation of processing resources to the task at hand, emerges when operators are not frequently solicited. Both contribute to out-of-the-loop effects, but in most real-world settings the two are confounded. A pilot who has been flying for six hours is simultaneously fatigued and under-stimulated. Which factor is driving the degradation? Until now, no objective metric could tell them apart.

To disentangle the two, the team designed an elegant supervisory paradigm. Twelve healthy volunteers with normal or corrected-to-normal vision watched a simplified aircraft interface displaying four flight parameters: altitude, speed, flap position, and heading. Their job was to monitor an autopilot and validate or reject its decisions. The rule was deceptively simple. Whenever a parameter deviated by more than 20 units (or 1 unit for flaps), participants compared the direction of that change to the most recent significant change they had observed. Same direction, regardless of parameter, meant the autopilot was correct; opposite direction meant it was wrong. Participants pressed designated arrow keys to report their judgment. Background noise—small sinusoidal fluctuations in all four parameters—made detection genuinely challenging, and the central display remained fixed to encourage eye movements.

The critical manipulation was temporal. The researchers varied the time delay between significant parameter changes across fifteen intervals ranging from 8 to 92 seconds, with the frequency of each interval following an exponential function so that short delays appeared most often. The logic was that the longer an operator waits before taking a relevant action, the more likely disengagement becomes—through task withdrawal or mind-wandering. Vigilance, by contrast, was manipulated through block repetition: the same task was divided into ten blocks, with the first block corresponding to the first 15 responses and the last to the final 15. The full session lasted roughly 90 minutes. Throughout, a Smart Eye Pro 3.0 system with two infrared illuminators and three cameras sampled gaze at 120 Hz, recording fixations, saccades, blinks, and eyelid opening.

The behavioral results confirmed the performance cost of disengagement. Using hierarchical Bayesian mixed-effects models, the team found strong evidence that reaction times increased with longer delays: the longer participants had to wait before intervening, the slower they responded when the moment finally arrived. Accuracy, by contrast, remained high overall—around 90 percent—and showed no credible effect of delay or block. Correct responses were slightly slower than incorrect ones, consistent with a small speed–accuracy trade-off in which more careful judgments took a bit longer. The performance problem, in other words, was not about making mistakes. It was about losing the readiness to act quickly.

The eye-tracking data revealed something far more interesting: two dissociable oculomotor signatures. Under disengagement—longer delays—the number of saccades decreased, the number of fixations decreased, and fixation durations increased. Visual exploration of the interface shrank; participants scanned less and dwelled longer at each location, a pattern the authors suggest is consistent with episodes of mind-wandering or attentional drift during extended intervals without stimulation. Under declining vigilance—as blocks progressed—the picture changed. Blink frequency increased, blink durations increased, and eyelid opening decreased, all classic markers of reduced arousal and fatigue. Fixation durations, remarkably, moved in opposite directions for the two states: they lengthened with increasing delay but shortened across blocks. The number of saccades and fixations remained stable across blocks, indicating that vigilance decline did not reduce visual exploration per se but altered how sustained visual processing was maintained.

This dissociation matters because it suggests different underlying mechanisms. Longer delays between task-relevant events may encourage transient disengagement from active monitoring, reducing motor and cognitive readiness so that participants are less prepared to detect and evaluate system changes when they occur. The shortening of fixation durations across blocks, occurring alongside increased blinking and narrowed eyelids, reflects less sustained visual processing rather than reduced scanning. The authors note that the mid-experiment break may have partially restored vigilance, attenuating time-on-task effects on behavior—yet clear oculomotor patterns associated with time spent on task still emerged. They also acknowledge that fatigue or boredom cannot be fully excluded over a 90-minute session, though a slight decrease in reaction times across blocks argues against boredom or fatigue being the dominant factor, since those would be expected to slow responses as time-on-task increases.

The most forward-looking result came from the classification analyses. The team trained logistic regression classifiers to predict, from oculomotor features alone, whether a participant would respond slowly or quickly on each upcoming trial. Features included the number and duration of saccades, fixations, and blinks, plus pupil size and eyelid opening, computed across three time windows preceding each parameter change. Using Monte Carlo cross-validation repeated 1,000 times, with training on 80 percent of trials and matched numbers of fast and slow responses, the classifiers performed reliably above chance. The highest classification accuracy reached 56 percent, with area-under-the-curve values between 0.54 and 0.58 across time windows, and the entire time cluster was statistically significant. Neighborhood component analysis identified the three most informative features: the number of saccades, the duration of fixations, and the duration of blinks.

Those numbers are modest, but their implications are substantial. They demonstrate that an operator’s behavioral state—specifically, whether they are about to react sluggishly—can be anticipated from eye movements recorded seconds before the critical event. In a cockpit, that means an eye-tracking system could in principle flag a pilot who is drifting out of the loop before the autopilot fails, opening the door to adaptive interfaces that re-engage attention proactively rather than reactively. The authors emphasize that the findings are relevant not only for monitoring piloting activities but also for the broader science of how task disengagement shapes human cognition.

Limitations remain. The sample of twelve participants is small, which may constrain generalizability, and the laboratory paradigm cannot fully capture the vigilance and engagement decrements pilots experience during long flights. The authors call for future studies to replicate these effects in larger and more diverse samples, including pilots during flight simulations, and note that the limited ecological validity of the cognitive task requires validation using cockpit simulators. Still, the study delivers what the field has needed: proof that disengagement and vigilance decline, long conflated in the out-of-the-loop literature, leave distinct fingerprints in the eyes—and that those fingerprints can predict performance before it falters.

Subject of Research: Distinct oculomotor signatures for task disengagement and reduction in vigilance during a supervisory task

Article Title: Distinct oculomotor signatures for task disengagement and reduction in vigilance during a supervisory task

Article References: Ficarella, S. C., Maille, N., Lantos, N., Le Goff, K., Sciabica, J.-F., Sarrazin, J.-C., & Desantis, A. (2026). Distinct oculomotor signatures for task disengagement and reduction in vigilance during a supervisory task. Frontiers in Neuroergonomics, 7, Article 1706491. https://doi.org/10.3389/fnrgo.2026.1706491

Image Credits: AI Generated

DOI: 10.3389/fnrgo.2026.1706491

Keywords: Distinct, oculomotor, signatures, task, disengagement, reduction, vigilance, during, supervisory, scientific research

The review, led by Manal Aich, Jamal El Aoufi, and Ahmed Moussa from Abdelmalek Essaâdi University and the Mohammed VI International Academy of Civil Aviation in Morocco, systematically analysed 34 peer-reviewed studies drawn from PubMed, Scopus, and Web of Science. Following PRISMA 2020 guidelines, the team screened over 850 records and distilled them into a rigorous synthesis of how vigilance decrements in air traffic control are measured, modelled, and — crucially — predicted. Their conclusion is stark: while machine learning models can achieve impressive classification accuracies under controlled conditions, no study to date has validated a vigilance detection system under real shift conditions with a sample large enough to support operational deployment.

Vigilance, in the context of air traffic control, refers to the sustained ability to detect rare but safety-critical events against a continuous backdrop of routine traffic. A controller might spend hours watching radar screens where nothing unusual happens, and then suddenly be required to spot a conflict — two aircraft on converging trajectories — within seconds. The cognitive demands are enormous: simultaneous tracking of multiple aircraft, management of radio communications, integration of meteorological data, and coordination with adjacent sectors. Yet the physical demands are minimal. Controllers sit, watch, and talk. This combination — low physical activity paired with relentless attentional requirements — is precisely the profile that decades of vigilance research have shown to produce reliable and predictable decrements in detection performance.

What makes vigilance failure particularly insidious from a safety perspective is that it leaves no trace. Unlike a mechanical fault or a communication error, a lapse in sustained attention produces no log entry, no anomaly in the data record, and no recoverable artifact after the fact. Incident reports document what happened; they cannot reconstruct where a controller’s attention was in the seconds before a near-miss. This absence of evidence makes vigilance decrements one of the most underreported and underinvestigated contributors to aviation safety events, despite their consistent appearance across incident and accident records worldwide.

The review distinguishes carefully between three related but distinct constructs: vigilance, workload, and fatigue. Vigilance concerns sustained signal detection over time. Workload is the momentary cognitive demand imposed by the task. Fatigue is a cumulative reduction in physiological or cognitive capacity that degrades both. Treating these as interchangeable — as much of the older literature has done — produces models that cannot identify the actual source of a performance decrement. The authors argue that vigilance is the most tractable target for real-time physiological monitoring because declining vigilance produces measurable neural and ocular signatures: increases in theta-band (4–8 Hz) and alpha-band (8–13 Hz) EEG power, changes in blink rate, and shifts in gaze patterns that can, in principle, be detected before a performance error occurs.

The measurement landscape revealed by the review is dominated by two modalities. Electroencephalography (EEG) appears in nearly 59 percent of the included studies, capitalising on well-documented spectral signatures of cognitive state. Eye-tracking features in about half of all studies, capturing fixation duration, saccade dynamics, blink rate, pupil diameter, and gaze entropy — a measure of how randomly a controller scans the display. Heart rate variability (HRV) appears in roughly 29 percent of studies. Increasingly, researchers are combining these modalities into multimodal fusion systems, which accounted for about 44 percent of the reviewed work. The logic is compelling: no single sensor captures the full picture of cognitive state, but combining EEG, ocular, and cardiac signals can produce a more robust and reliable estimate of vigilance than any individual stream.

The machine learning results are, on the surface, impressive. Classification accuracies across the 20 modelling studies ranged from 64 percent to a remarkable 100 percent, with several studies employing sophisticated architectures — convolutional neural networks paired with long short-term memory (CNN-LSTM), spatio-temporal graph networks with centre loss functions, and dual-stream fusion of facial video with radiotelephony audio — reporting accuracies exceeding 90 percent. One study using a spatio-temporal graph network on EEG data for remote tower controllers achieved 96.73 percent accuracy in fatigue detection. Another, combining eye-tracking and heart rate variability through a gradient-boosted model, predicted situational awareness with an R² of 0.78. These are not trivial results; they demonstrate that the physiological signatures of declining vigilance are real, learnable, and exploitable.

But the review’s most important contribution may be its unflinching assessment of what those numbers actually mean. The authors introduce a two-tier classification framework. Tier 1 encompasses proof-of-concept studies: small samples, within-subject validation, simulator-based settings. The overwhelming majority of the reviewed literature falls into this tier. Tier 2, representing stronger external validity, requires cross-subject validation, field data from operational shifts, or operationally relevant metrics such as sensitivity and false alarm rate. Only two studies in the entire review met Tier 2 criteria — and even those relied on dataset or simulation data rather than live operations. No study in the review combined field validation with cross-subject generalisation. The gap between a 97 percent accuracy figure achieved in a laboratory simulator with 15 participants and a system that can reliably protect a controller on a real midnight shift is, the authors make clear, enormous.

The practical constraints on deployment are formidable. High-density EEG arrays and precision eye trackers produce the best laboratory results but are not the sensors that can be worn comfortably during a live six-hour shift. Dry-electrode EEG systems, which eliminate conductive gel, suffer from higher contact impedance and greater susceptibility to motion artefact — particularly problematic for the theta-band signals most central to vigilance detection. Camera-based computer vision systems offer a contact-free alternative, and recent work using improved YOLO-based architectures and vision-language models has shown promise, but these approaches raise their own questions about individual variability, lighting conditions, and data governance. Beyond the sensors themselves, operational deployment requires regulatory clearance for non-interference with certified ATC equipment, hygiene protocols for shared workstations, controller consent frameworks, and data protection measures for continuous biometric monitoring of certified personnel.

Perhaps the most forward-looking contribution of the review is its argument that detection alone is insufficient. The authors call for closed-loop vigilance monitoring systems — ones in which a real-time classifier does not merely log a state label but triggers an automated or supervisory response: a supervisor alert, an adjustment in automation support, a redirection of incoming traffic, or a structured handover protocol. Such systems have been demonstrated in adjacent domains, notably EEG-based driver fatigue detection that issues adaptive alerts, but remain largely absent from air traffic control. The authors also stress the need for explainable AI — systems whose outputs supervisors can actually interpret — and for individual calibration, since vigilance profiles differ substantially across controllers in circadian pattern, workload tolerance, and physiological response. Bridging the gap between proof of concept and operational reality, they conclude, requires individual calibration, closed-loop intervention design, interpretable outputs, field validation at scale, and early regulatory engagement — all pursued in parallel, not as a sequential checklist. The silent threat in the control tower is real, measurable, and increasingly predictable. What remains is the harder work of turning prediction into protection.

Subject of Research: Vigilance decrement detection and prediction in air traffic control using physiological monitoring and machine learning

Article Title: Vigilance decrements in air traffic control as an aviation safety risk: a systematic review of modeling and prediction approaches

Article References: Aich, M., El Aoufi, J., & Moussa, A. (2026). Vigilance decrements in air traffic control as an aviation safety risk: a systematic review of modeling and prediction approaches. Frontiers in Neuroergonomics, 7, Article 1874304. https://doi.org/10.3389/fnrgo.2026.1874304

Image Credits: AI Generated

DOI: 10.3389/fnrgo.2026.1874304

Keywords: air traffic control, vigilance, neuroergonomics, EEG, machine learning, aviation safety, fatigue detection, systematic review, human factors, attention, decrements, traffic

The review, conducted by a single investigator using PubMed and Google Scholar, began with 1,608 publications and, after rigorous screening against modified PRISMA 2020 guidelines, distilled the evidence down to 34 peer-reviewed studies, comprising 31 research papers and three case studies. Nearly all of the included work was published between 2014 and 2025, underscoring how recently the topic has captured scientific attention. The United Kingdom and the United States together produced almost two-thirds of the studies, with additional contributions from Australia, Brazil, New Zealand, China, Pakistan, Portugal and Singapore. Mammals dominated the species roster, with non-human primates studied most often, but birds were also well represented, including flamingos, penguins, hornbills, kiwi and free-flight aviary collections, alongside a handful of reptiles such as an American alligator, a Siamese crocodilian and semi-aquatic turtles. Notably, no study examined teleost fish, and half of all investigations evaluated two or fewer individual animals, a small sample size that limits how broadly the results can be generalized.

Behavioural observation was by far the most common assessment tool, deployed in 28 of the studies through species-specific ethograms that catalogue activities such as resting, foraging, pacing and social interaction. The single most frequently documented response was increased vigilance, reported in ten studies across macaques, gibbons, capuchins, peccaries, orangutans, giraffes, hornbills, cheetahs and mixed aviary species. Vigilance, the scanning behaviour animals use to monitor their environment for threats, is widely interpreted as a sign of heightened arousal or anxiety. Other documented responses included retreating away from visitors or out of sight, decreased rest, increased self- or social grooming, increased pacing, reduced appetite, increased swimming, altered circadian activity patterns, greater time spent underwater in a Siamese crocodile, and even increased protectiveness of infants in orangutans. Importantly, six studies found no statistically significant behavioural change linked to noise, and no publication reported overt abnormalities such as self-inflicted trauma or serious aggression.

Physiological measurement, by contrast, was rare. Only six studies assessed glucocorticoid concentrations, the hormonal fingerprints of activity in the hypothalamic-pituitary-adrenal axis, with five pairing faecal or urinary hormone analysis with behavioural observation. Three of the six reported elevated glucocorticoids: one in okapis attributed to visitor presence, one in orangutans linked to ventilation system noise, and one in a giant panda later attributed to normal physiological variation unrelated to noise. The remaining studies found no elevation or no correlation with behaviour. The review’s authors note that the broader literature increasingly cautions against interpreting glucocorticoids as stand-alone welfare indicators, since levels can fluctuate with reproductive status, circadian rhythms and sampling methods, and argue that pairing hormone data with complementary measures, such as the cognitive bias testing used in one primate study, would produce far more robust welfare conclusions.

Perhaps the most surprising finding concerns the sources of the noise itself. Visitor noise was the focus of 27 studies, but construction noise appeared in five and concerts in two. Yet in two publications, researchers suspected that ventilation systems, not visitors, were the real acoustic culprit, because decibel levels did not rise appreciably when crowds grew. A third study found that behavioural changes tracked a habitat’s waterfall feature more closely than visitor noise. One investigation concluded that reducing visitor numbers or limiting opening hours did not guarantee a quieter environment, highlighting the persistent hum of mechanical infrastructure in zoo soundscapes. At the same time, enclosure acoustics can work in animals’ favour: one study showed that a habitat’s soundscape substantially buffered short-term fluctuations in external noise, suggesting that thoughtful acoustic design can be a protective factor rather than a hazard.

The objective acoustic data gathered across the studies spanned an extraordinary range, from 51.5 decibels to a peak of 122.2 decibels recorded near a sloth habitat in a walk-through enclosure. Concert noise reached a maximum of 88 decibels and construction noise 76 decibels. One study measured construction and non-construction days at different frequencies in hertz, while another compared how A-weighted and C-weighted decibel filters affected penguin species, finding that only southern rockhoppers responded positively to pool use under elevated C-weighted levels. These technical details matter because sound meters positioned on the public-facing side of a habitat may not accurately represent what an animal actually perceives, a limitation the review flags as a significant obstacle to drawing welfare conclusions from sound pressure levels alone.

When it came to characterizing welfare outcomes, the literature revealed a striking inconsistency. Eleven studies suspected a negative welfare impact from anthropogenic noise based on observed behavioural changes, nine could not determine whether welfare was affected, seven reported no substantial impact, and seven did not address welfare as an outcome at all. Welfare assessment protocols were rarely described in a standardized way, and many studies acknowledged confounding factors, including the visual presence of visitors, the sight of construction workers, species-level and individual differences in noise sensitivity, and even the acoustic interference of precipitation. Under the Five Domains model of animal welfare, which integrates nutrition, physical environment, health, behavioural interaction and mental state, these complications underscore that noise cannot be evaluated in isolation. The ventilation system example illustrates the dilemma: mechanical noise may be stressful, yet reducing ventilation could compromise thermal comfort and air quality, producing a net welfare loss across multiple domains.

Fourteen publications offered mitigation recommendations, clustering around three practical strategies. Public education, delivered through signage or trained staff, was the most frequently cited, and one study found that ‘watching eyes’ signage requesting quietness measurably reduced noise levels. The second strategy centred on giving animals choice and control over their location, an established contributor to positive welfare, with several studies documenting animals retreating to indoor or off-exhibit spaces to escape noise. Additional suggestions included controlling visitor numbers and timing, habituation training, adding soothing background sound, increasing vegetation, redesigning enclosures to dampen sound and continuing behavioural monitoring during disruptive periods. Operational planning, such as relocating noise-sensitive species during extended construction, could preserve visitor engagement while protecting animal welfare.

The review concludes that anthropogenic noise alone may not harm welfare in every case, since some studies could not disentangle acoustic from visual disturbances, and that species and individuals differ markedly in their responses. Individual-level welfare assessments, the authors argue, allow institutions to plan more appropriately and may prevent costly habitat reconstruction by identifying minor, targeted modifications before welfare is seriously compromised. As zoo accreditation standards evolve, the authors suggest that noise deserves more formal inclusion in welfare assessment frameworks, and that future research should explore how animals respond to different human-generated frequencies, whether reactions differ between chronic and acute exposure, and which documented behaviours genuinely reflect negative welfare states. For a field barely a decade old, the message is clear: the soundscape of the modern zoo is not just background noise, but a variable that zoological institutions can measure, design around and ultimately manage to the benefit of the animals in their care.

Subject of Research: The effects of anthropogenic noise on animal welfare in zoological settings

Article Title: A Scoping Review: The Effect of Anthropogenic Noise on Animal Welfare in Zoological Settings

Article References: Kruse, T. N., & Flint, M. (2026). A Scoping Review: The Effect of Anthropogenic Noise on Animal Welfare in Zoological Settings. Veterinary Medicine and Science, 12(5), Article e71197. https://doi.org/10.1002/vms3.71197

Image Credits: AI Generated

DOI: 10.1002/vms3.71197

Keywords: anthropogenic noise, zoo animal welfare, animal behaviour, soundscape, glucocorticoids, visitor effects, Five Domains model, noise mitigation, scoping review, captive animals, vigilance, zoo acoustics