Background A learning task recurrently perceived as easy (or hard) may cause poor learning results. Gamer data such as errors, attempts, or time to finish a challenge are widely used to estimate the perceived difficulty level. In other contexts, pupillometry is widely used to measure cognitive load (mental effort); hence, this may describe the perceived task difficulty. Objective This study aims to assess the use of task-evoked pupillary responses to measure the cognitive load measure for describing the difficulty levels in a video game. In addition, it proposes an image filter to better estimate baseline pupil size and to reduce the screen luminescence effect. Methods We conducted an experiment that compares the baseline estimated from our filter against that estimated from common approaches. Then, a classifier with different pupil features was used to classify the difficulty of a data set containing information from students playing a video game for practicing math fractions. Results We observed that the proposed filter better estimates a baseline. Mauchly’s test of sphericity indicated that the assumption of sphericity had been violated (χ214=0.05; P=.001); therefore, a Greenhouse-Geisser correction was used (ε=0.47). There was a significant difference in mean pupil diameter change (MPDC) estimated from different baseline images with the scramble filter (F5,78=30.965; P<.001). Moreover, according to the Wilcoxon signed rank test, pupillary response features that better describe the difficulty level were MPDC (z=−2.15; P=.03) and peak dilation (z=−3.58; P<.001). A random forest classifier for easy and hard levels of difficulty showed an accuracy of 75% when the gamer data were used, but the accuracy increased to 87.5% when pupillary measurements were included. Conclusions The screen luminescence effect on pupil size is reduced with a scrambled filter on the background video game image. Finally, pupillary response data can improve classifier accuracy for the perceived difficulty of levels in educational video games.

The adoption of Industry 4.0 technologies in manufacturing systems has accelerated in recent years, with a shift towards understanding operators’ well-being and resilience within the context of creating a human-centric manufacturing environment. In addition to measuring physical workload, monitoring operators’ cognitive workload is becoming a key element in maintaining a healthy and high-performing working environment in future digitalized manufacturing systems. The current approaches to the measurement of cognitive workload may be inadequate when human operators are faced with a series of new digitalized technologies, where their impact on operators’ mental workload and performance needs to be better understood. Therefore, a new method for measuring and determining the cognitive workload is required. Here, we propose a new method for determining cognitive-workload indices in a human-centric environment. The approach provides a method to define and verify the relationships between the factors of task complexity, cognitive workload, operators’ level of expertise, and indirectly, the operator performance level in a highly digitalized manufacturing environment. Our strategy is tested in a series of experiments where operators perform assembly tasks on a Wankel Engine block. The physiological signals from heart-rate variability and pupillometry bio-markers of 17 operators were captured and analysed using eye-tracking and electrocardiogram sensors. The experimental results demonstrate statistically significant differences in both cardiac and pupillometry-based cognitive load indices across the four task complexity levels (rest, low, medium, and high). Notably, these developed indices also provide better indications of cognitive load responding to changes in complexity compared to other measures. Additionally, while experts appear to exhibit lower cognitive loads across all complexity levels, further analysis is required to confirm statistically significant differences. In conclusion, the results from both measurement sensors are found to be compatible and in support of the proposed new approach. Our strategy should be useful for designing and optimizing workplace environments based on the cognitive load experienced by operators.

<h4>Introduction</h4>Pupillometry, the measurement of eye pupil diameter, is a well-established and objective modality correlated with cognitive workload. In this paper, we analyse the pupillary response of ultrasound imaging operators to assess their cognitive workload, captured while they undertake routine fetal ultrasound examinations. Our experiments and analysis are performed on real-world datasets obtained using remote eye-tracking under natural clinical environmental conditions.<h4>Methods</h4>Our analysis pipeline involves careful temporal sequence (time-series) extraction by retrospectively matching the pupil diameter data with tasks captured in the corresponding ultrasound scan video in a multi-modal data acquisition setup. This is followed by the pupil diameter pre-processing and the calculation of pupillary response sequences. Exploratory statistical analysis of the operator pupillary responses and comparisons of the distributions between ultrasonographic tasks (fetal heart versus fetal brain) and operator expertise (newly-qualified versus experienced operators) are performed. Machine learning is explored to automatically classify the temporal sequences into the corresponding ultrasonographic tasks and operator experience using temporal, spectral, and time-frequency features with classical (shallow) models, and convolutional neural networks as deep learning models.<h4>Results</h4>Preliminary statistical analysis of the extracted pupillary response shows a significant variation for different ultrasonographic tasks and operator expertise, suggesting different extents of cognitive workload in each case, as measured by pupillometry. The best-performing machine learning models achieve receiver operating characteristic (ROC) area under curve (AUC) values of 0.98 and 0.80, for ultrasonographic task classification and operator experience classification, respectively.<h4>Conclusion</h4>We conclude that we can successfully assess cognitive workload from pupil diameter changes measured while ultrasound operators perform routine scans. The machine learning allows the discrimination of the undertaken ultrasonographic tasks and scanning expertise using the pupillary response sequences as an index of the operators' cognitive workload. A high cognitive workload can reduce operator efficiency and constrain their decision-making, hence, the ability to objectively assess cognitive workload is a first step towards understanding these effects on operator performance in biomedical applications such as medical imaging.

About one-third of all recently published studies on listening effort have used at least one physiological measure, providing evidence of the popularity of such measures in listening effort research. However, the specific measures employed, as well as the rationales used to justify their inclusion, vary greatly between studies, leading to a literature that is fragmented and difficult to integrate. A unified approach that assesses multiple psychophysiological measures justified by a single rationale would be preferable because it would advance our understanding of listening effort. However, such an approach comes with a number of challenges, including the need to develop a clear definition of listening effort that links to specific physiological measures, customized equipment that enables the simultaneous assessment of multiple measures, awareness of problems caused by the different timescales on which the measures operate, and statistical approaches that minimize the risk of type-I error inflation. This article discusses in detail the various obstacles for combining multiple physiological measures in listening effort research and provides recommendations on how to overcome them.

Abstract The ability to sustain attention on a task-relevant sound-source whilst avoiding distraction from other concurrent sounds is fundamental to listening in crowded environments. To isolate this aspect of hearing we designed a paradigm that continuously measured behavioural and pupillometry responses during 25-second-long trials in young (18-35 yo) and older (63-79 yo) participants. The auditory stimuli consisted of a number (1, 2 or 3) of concurrent, spectrally distinct tone streams. On each trial, participants detected brief silent gaps in one of the streams whilst resisting distraction from the others. Behavioural performance demonstrated increasing difficulty with time-on-task and with number/proximity of distractor streams. In young listeners (N=20), pupillometry revealed that pupil diameter (on the group and individual level) was dynamically modulated by instantaneous task difficulty such that periods where behavioural performance revealed a strain on sustained attention, were also accompanied by increased pupil diameter. Only trials on which participants performed successfully were included in the pupillometry analysis. Therefore, the observed effects reflect consequences of task demands as opposed to failure to attend. In line with existing reports, we observed global changes to pupil dynamics in the older group, including decreased pupil diameter, a limited dilation range, and reduced temporal variability. However, despite these changes, the older group showed similar effects of attentive tracking to those observed in the younger listeners. Overall, our results demonstrate that pupillometry can be a reliable and time-sensitive measure of the effort associated with attentive tracking over long durations in both young and (with some caveats) older listeners.

<h4>Background</h4>Mental fatigue significantly impairs surgeons' cognitive performance, compromising patient safety. However, surgical practice lacks an integrated framework to understand and mitigate this cognitive strain effectively.<h4>Conceptual model</h4>We propose adapting the Flush model, initially developed for endurance sports, to surgical settings. This model conceptualizes mental fatigue through a dynamic analogy of a water tank composed of 4 main components: perceived fatigue (ballcock), fatigue accumulation (filling rate), fatigue recovery (drain rate), and a safety margin (security reserve). We detail how intrinsic cognitive load, extraneous stressors, physiological and psychological factors, and circadian influences collectively drive mental fatigue accumulation.<h4>Clinical implications</h4>The Flush model clarifies how mental fatigue fluctuates during surgical procedures and highlights practical recovery methods such as brief mindfulness interventions, microbreaks, cognitive offloading, and ergonomics adjustments. It emphasizes maintaining a cognitive safety reserve to safeguard against errors during critical surgical phases, providing surgeons with actionable strategies to manage fatigue in real time.<h4>Future directions</h4>We recommend empirical validation through real-time monitoring using physiological measures (eg, heart-rate variability, pupillometry) coupled with subjective assessments (eg, NASA Task Load Index, Surgery Task Load Index). Integrating Flush principles into surgical training, simulation programs, and institutional policies could foster a culture prioritizing cognitive performance and patient safety.<h4>Conclusions</h4>The Flush model provides a comprehensive, intuitive framework for understanding and addressing surgeons' mental fatigue. Its implementation promises to enhance cognitive resilience, reduce surgical errors, and improve both patient outcomes and surgeon well-being.

Background/Objectives: This narrative review aims to evaluate the use of pupillometry as an objective measure of auditory perception and listening effort across the lifespan. Specifically, it synthesizes research examining pupillary responses in individuals with and without hearing impairment across pediatric, adult, and older adult populations. The review addresses methodological practices and clinical implications for integrating pupillometry into routine audiological assessment. Methods: 12 peer-reviewed studies published between 2010 and 2025 were selected through a systematic search of databases including PubMed, Scopus, Web of Science, and Google Scholar. Inclusion criteria required empirical use of pupillometry in auditory tasks involving human participants with normal hearing or hearing impairment. Studies were analyzed for population characteristics, experimental paradigms, pupillometric metrics (e.g., peak pupil dilation), level of evidence, and relevance to clinical audiology. This article uses a narrative review approach to organize and interpret findings. Results: Across age groups and hearing conditions, pupillometry consistently demonstrated sensitivity to cognitive load and listening effort, particularly in noisy environments or during complex auditory tasks. Pediatric studies revealed its potential as a non-invasive tool for preverbal children. Adult and older adult studies confirmed that pupillary responses reflect device performance (e.g., hearing aids, cochlear implants) and cognitive–linguistic demands. Methodological variability and individual differences in pupil response patterns were noted as limitations. Conclusions: The findings support the use of pupillometry as a valuable adjunct to behavioral audiometry, offering objective insight into auditory–cognitive load. Its application holds promise for pediatric diagnostics, hearing technology evaluation, and geriatric audiology. Standardization of measurement protocols and development of normative data are necessary to enhance clinical applicability and generalizability.

Background/Objectives: This narrative review aims to evaluate the use of pupillometry as an objective measure of auditory perception and listening effort across the lifespan. Specifically, it synthesizes research examining pupillary responses in indi-viduals with and without hearing impairment across pediatric, adult, and older adult populations. The review addresses methodological practices and clinical implications for integrating pupillometry into routine audiological assessment. Methods: Eleven peer-reviewed studies published between 2010 and 2025 were selected through a sys-tematic search of databases including PubMed, Scopus, Web of Science, and Google Scholar. Inclusion criteria required empirical use of pupillometry in auditory tasks in-volving human participants with normal hearing or hearing impairment. Studies were analyzed for population characteristics, experimental paradigms, pupillometric metrics (e.g., peak pupil dilation), level of evidence, and relevance to clinical audiology. This article uses a narrative review approach to organize and interpret findings. Results: Across age groups and hearing conditions, pupillometry consistently demonstrated sensitivity to cognitive load and listening effort, particularly in noisy environments or during complex auditory tasks. Pediatric studies revealed its potential as a non-invasive tool for preverbal children. Adult and older adult studies confirmed that pupillary re-sponses reflect device performance (e.g., hearing aids, cochlear implants) and cogni-tive-linguistic demands. Methodological variability and individual differences in pupil response patterns were noted as limitations. Conclusions: The findings support the use of pupillometry as a valuable adjunct to behavioral audiometry, offering objective insight into auditory-cognitive load. Its application holds promise for pediatric diagnostics, hearing technology evaluation, and geriatric audiology. Standardization of measurement protocols and development of normative data are necessary to enhance clinical applica-bility and generalizability.

Recent advances in eye-tracking technologies have fostered growing interest in their integration with acoustic research for investigating auditory perception and human behavioral responses. This study presents a structured literature review of recent developments at the intersection of eye tracking and acoustics, with the aim of analyzing how eye-movement data can support the interpretation of auditory events, spatial listening behaviors, and multimodal human-environment interactions. The reviewed studies were organized into four main research areas focusing on the application of eye-tracking in acoustics: sound source localization and identification, sound event detection and classification, acoustic sensing and multimodal systems, and soundscape and perceptual acoustic studies. The analysis indicates that eye-movement patterns can provide useful indicators of auditory attention and perceptual processes, particularly when combined with complementary physiological, visual, and acoustic sensing modalities. Furthermore, recent methodological advances, including real-time processing, machine learning algorithms, and sensor fusion techniques, have contributed to improving the robustness and accuracy of multimodal data analysis. Nevertheless, the review also highlights several limitations in current research, such as the lack of standardized experimental protocols, inter-individual variability, and susceptibility to environmental noise and external interference. Finally, future research perspectives are discussed, emphasizing the development of standardized and adaptive multimodal frameworks for human behavior modeling and intelligent acoustic monitoring systems.