Behavior Research Methods

Museum visits typically follow curator-defined routes that constrain how visitors shape their own experience, yet choice is widely held to heighten engagement, autonomy, and enjoyment. Virtual reality (VR) offers a setting in which to study these processes because it combines ecological immersion with precise, continuous behavioral measurement. We investigated (i) whether VR- derived behavioral signals are associated with self-reported enjoyment during a virtual museum tour, and (ii) whether the level of agency afforded to visitors influences enjoyment. Forty-eight adults completed a room-scale, life-size VR tour (8 * 4 m) of seven paintings from the Tel Aviv Museum of Art, each accompanied by a synchronized audio guide. Synchronized gaze and head- position streams were logged continuously (50 Hz) and segmented into painting-level viewing episodes using a trial-and-tile pipeline that intersects each painting's trial interval with an empirically defined spatial window in front of the canvas. Participants were randomly assigned to one of three agency conditions, Active (choice before every artwork), Semi-Active (choice for the first three), or Passive (fixed route),while the artwork sequence was held identical. Self- reported enjoyment at the tour and painting levels did not differ reliably across agency conditions. Among VR-derived measures, gaze engagement during the audio guide showed the clearest (though modest) association with painting-level liking, whereas locomotion and pacing measures were weak and inconsistent predictors. Agency nonetheless reliably modulated several gaze- and time-based viewing measures. The findings reveal a dissociation between subjective enjoyment and the micro-structure of viewing, and establish a reusable framework for full-tour, painting-level behavioral analysis in immersive settings.

Recognizing others emotions is central to social interaction. Traditional biological psychology infers emotional responding via laboratory measures, whereas contemporary computer vision algorithms claim to identify emotions unobtrusively from facial video. However, the validity of such algorithms for classifying spontaneous emotional responses occurring without explicit communicative intent remains debated. We compared established psychophysiological measures (EEG, facial EMG, EDA activity) with the open-source facial behavior toolkit OpenFace for classifying participants spontaneous responses during free viewing of happiness-inducing, disgust-inducing, and neutral pictures. Participants provided valence and arousal ratings and later selected the basic emotion that best matched their reaction which served as the classification criterion. Using within-participants single-trial support vector machine (SVM) classification, EEG achieved the highest accuracy (40%), followed by facial EMG (37%); OpenFace reached 36%. All methods except EDA exceeded chance performance (33.3%) and were lower compared to human raters (48%). Predictions declined slightly for across-participants SVMs, being at chance for OpenFace and EDA. The results indicate that in principle both, psychophysiological measures and video-derived facial action units, can capture diagnostically relevant aspects of emotional responding during picture viewing, but that their performance is limited when expressions are spontaneous and not produced for communicative purposes. Inter-individual variability in expressivity and physiological responding likely contributes to these limitations and should be considered when deploying automatic emotion recognition in research or applied settings.

Natural-sound research requires stimulus sets that combine acoustic standardization with detailed behavioural characterization. We present 1,377 two-second sounds representing 240 expert-defined source--action classes. We call this database "MaMa Sounds", as it resulted from the collaborative effort of two academic teams in Maastricht and Marseille. The sounds were manually curated, segmented, sampled at 16 kHz, and labelled with a noun identifying the source and a verb identifying the action. We release deidentified trial-level identification and familiarity data together with multiple per-sound norms (e.g., identification accuracy, confidence and agreement; familiarity), along with overall norms derived with principal component analysis. Noun, verb, and joint noun--verb norms are provided as direct means and medians with the number of contributing observations. This battery preserves process-specific information, while two principal-component scores provide compact overall behavioural-identifiability measures derived from response ease, semantic correspondence, agreement, and familiarity. The repository also contains deterministic response-cleaning code, participant and reference Word2Vec representations, and code reproducing the public sound-level tables. The resource supports stimulus selection, matching, and continuous modelling in auditory cognition and neuroscience.

PurposePupil diameter in daily life depends on both the light reaching the eye and the observers age, but established prediction formulas require laboratory quantities that are rarely measured in natural environments. We developed a compact age-corrected model that predicts pupil diameter from melanopic equivalent daylight illuminance (mEDI). MethodsWe used an existing field dataset in which binocular pupil diameter and near-corneal spectral irradiance were recorded while 83 adults aged 18-87 years moved through indoor and outdoor environments. The analysis included 10,082 valid paired observations. We fitted a bounded sigmoid relating pupil diameter to mEDI and age, with each participant given equal influence, and assessed prediction in participants excluded from model fitting. Performance was compared with simpler models, a flexible generalised additive model (GAM), and Watson-Yellott predictions based on assumed field geometry. ResultsPupil diameter decreased smoothly as mEDI increased. Age primarily reduced the difference between pupils in dim and bright conditions, by 0.768 mm per decade, while the predicted bright-light diameter changed little with age. In held-out participants, the bounded model had a participant-balanced root mean squared error (RMSE) of 0.630 mm and mean absolute error of 0.537 mm. The GAM had a slightly lower point-estimate RMSE of 0.610 mm, but the difference was small and uncertain. The bounded model outperformed the tested log-linear, reduced, age-only, and Watson-Yellott alternatives. ConclusionAge and mEDI are sufficient to provide useful population-average pupil predictions across the observed adult age and real-world light range. The model is transparent, physiologically bounded, and nearly as accurate as a flexible GAM, but predictions approaching darkness remain uncertain because valid mEDI measurements were not available in that range. Key pointsO_LIA compact equation predicts population-average pupil diameter from age and mEDI alone. C_LIO_LIAge mainly compresses the pupils response range by reducing pupil diameter under dimmer conditions. C_LIO_LIPrediction error in unseen participants was close to that of a flexible GAM, without requiring a fitted smooth object. C_LIO_LIThe model is intended for the observed adult age and field-light range, not for extrapolation into darkness. C_LI

Wayfinding in hospitals is often hindered by ineffective signage; however, the cognitive mechanisms of healthcare wayfinding symbols comprehension remain under-researched. This study utilized eye-tracking and spatial gaze mapping to examine how visual complexity, abstraction, and human figuration modulate perception in 40 healthy adults viewing 24 hospital-related healthcare wayfinding symbols. Results indicate that pupil size is a sensitive physiological marker of cognitive load, significantly influenced by visual complexity ({chi}2 = 11.32, p = .022) and abstraction ({chi}2 = 7.49, p = .027). Human figuration reduced fixation duration and increased saccade amplitude, facilitating efficient semantic integration. Furthermore, human-centric healthcare wayfinding symbols elicited streamlined gaze trajectories, whereas abstract/complex designs induced chaotic scanpaths. These findings suggest that human figuration acts as a cognitive scaffold, reducing mental effort. We provide evidence-based guidelines for optimizing healthcare wayfinding symbols by prioritizing human body representations and balancing abstraction levels. HighlightO_LIPupil size indexes cognitive load during symbol comprehension. C_LIO_LIHuman figuration cuts fixation duration, boosting wayfinding efficiency. C_LIO_LIAbstract symbols increase pupil dilation, raising cognitive load. C_LI

The measurement of light received at the cornea of the eye is a paramount consideration for the understanding of the relation between environmental illumination and the non-image-forming effects of light. The field of view (FOV) at the cornea is less than a full hemisphere, because it is partially occluded by human facial morphology. The International Commission on Illumination (CIE) has defined a standard model of human FOV. A suitably designed physical occluder attached to the sensor (of a light meter) has been proposed as a means of incorporating the effect of human FOV when taking measurements. Similarly, when using simulation to predict light received at the cornea, a geometrical description of the occluder at the eye point(s) can be added to the 3D model of the scene. The first occluder model proposed to represent CIE human FOV was enumerated in terms of: the CIE definition; the radius of the occluder; and, the radius of the light sensor disc. We present a simpler model based only on the CIE definition and the occluder radius. Both models were tested using a virtual goniophotometer. Various sensor response functions describing the spatial sensitivity across the sensor disc, including several we characterized through laboratory measurements, were included in the test. For all functions considered, the performance of the simpler occluder model was equivalent to or better than the model first proposed.

Traditionally, complex behavioral tasks in mice have relied upon water restriction as an external motivator to increase task engagement. However, citric acid (CA) water, the technique whereby water is given ad libitum but made sour by the addition of CA, has emerged as an alternative to typical water restriction. Evidence suggests CA water can effectively motivate task performance while improving animal welfare in alignment with the 3Rs of animal research and reducing experimenter labor. While promising, the applicability of CA water in mice remains incompletely characterized with higher concentrations only tested in rats and "ramping" schedules, where mice progress to increasingly higher concentrations, indirectly examined. Here, we evaluate four CA concentration/schedule combinations for their effects on mouse health (weight changes, home cage behaviors, fecal counts) and motivation to drink regular water (lick counts, drinking behaviors) in female and male C57BL/6J mice. We find that a schedule ramping from 1% to 2% CA after one week is the easiest for mice to adapt to and sustained 2% CA use maintains robust lick counts for at least 5 weeks. Additionally, CA has been directly characterized for wheel-turning and touchscreen tasks, but not virtual reality (VR) tasks, an increasingly popular class of behavioral experiments. Therefore, we also assess how 2% CA affects motivation and task performance in two VR treadmill tasks (running and stopping task). We find that CA water does not improve task performance above that of mice given regular water, but does limit competing motivations and produce more uniform, reward-motivated behavior.

Planning husbandry tasks and experiments with Drosophila melanogaster requires converting a target date into development times that depend on the rearing temperature. This calculation needs to be done for each cross, genotype, and temperature, and the risk of error grows quickly. Available laboratory management tools let users register stocks, crosses, and track them, but they do not create schedules based on a clear, adjustable thermal model. To fill that gap, we developed DrosoTracker, a self-contained web application that works offline and predicts Drosophila development with a thermal summation model recalibrated through regression on data from Powsner (1935) (T0 = 11.78 {degrees}C, DD = 116.38 {degrees}C{middle dot}days, R{superscript 2} = 0.997). The model offers an optional two-level calibration driven by user observations. A wild-type strain first adjusts the model to the laboratorys own conditions. Then each genotype is calibrated against that reference using a random-effects shrinkage estimator that accounts for measurement error and between-batch variability. The model creates schedules for husbandry tasks, evaluates adult cohort survival with the Kaplan-Meier estimator and the log-rank test, and calculates sample size for lifespan studies using Schoenfelds formula. The quantitative components were checked against independent references, including Rs survival package and manual calculations. Ongoing work is focused on validating the calibrated model using cohorts specifically bred for this purpose. DrosoTracker runs entirely in the browser, stores data locally, and is available in English and Spanish.

Advances in camera technology and computer vision techniques have allowed researchers to track animals in 3D in ways which previously were difficult or impossible. Many such 3D tracking tools make use of multiple cameras, but unfamiliarity with the principles and technology involved can make it difficult to employ such techniques. In this protocol, we describe Braid, open-source software for live, multi-camera 3D tracking of insects. Using background-subtraction, Braid performs detection of objects without requiring the use of physical markers affixed to the insect. Braid constructs low-latency 3D position estimates using Kalman filtering and nearest neighbor data association. We document in detail the process of tracking freely flying bees within a flight arena using Braid. This protocol includes instructions on installation, configuration of cameras, setup, calibration, and operation. Within the system described here, we demonstrate that Braid can achieve position estimates accurate to <1 millimeter (within a 0.3 cubic meter volume). These factors make Braid suitable for tracking small, fast-flying animals like insects. Braid's low latency allows live tracking, removing the necessity to collect large video files and making it suitable for integration in closed loop systems such as virtual reality. Code is available at https://github.com/strawlab/strand-braid

Rigorously tracking eye and head behavior in space is key to building realistic models of the stimulus that reaches our retina. The motion structure of this stimulus or retinal flow - the substrate for self and object motion processing - is created by the relative movement of the eyes with respect to the world. Characterizing this stimulus requires tracking the eyes three degrees of freedom in the head and the heads six degrees of freedom in the world. While vertical and horizontal eye rotations have been described during locomotion in the context of gaze stabilization (Moore et al, 2001), the component around the line of sight - torsion - has remained difficult to quantify, and how all three rotational components jointly contribute to retinal flow during self-motion remains largely unexplored. Here, we leveraged head-mounted technology to estimate eye torsion in ten subjects as they walked towards a distant target in a fast and slow condition (from 14 to 4 meters away from the target, see Fig. 1A). More specifically, we combined automatic feature tracking with gaze-constrained simulations of eye rotations and camera projection to recover torsion from image data. We then estimated flow curl in head and retina centered frames in two scenarios: torsion as estimated from our data and with no torsion. We show that the eyes torsional component compensates for the roll component of heads angular displacement, altering the incoming visual flow in ways that are relevant for the extraction of self-motion parameters from retinal flow. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/743586v1_fig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@391b9eorg.highwire.dtl.DTLVardef@1444510org.highwire.dtl.DTLVardef@1121e16org.highwire.dtl.DTLVardef@754b6a_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig 1.C_FLOATNO A. Top. Custom-made head-mounted device combining the Neon eye tracker (Pupil Labs), an RGB camera and a dimmable light. Bottom. Four 3D frames of reference (FoR) are relevant for this study, two static (world and locomotion) and two subject centered (head and eye). The Z axis of the locomotion, head and eye FoRs are approximately aligned throughout the trial. For the locomotion FoR the Z axis is fixed in the world and points forward (towards the target). The heads Z axis moves with the head but - as subjects are fixating a target along their path -, it also points approximately forward. The eyes Z axis also moves with the head and its exact forward orientation will depend on compensatory eye movements. B. Left. Blue dots represent the Z component of the heads orientation vector (on the locomotion frame) on the X axis, and the sum of all three components on the Y axis; for each frame for all corpus data. Blue contour is the 75th percentile 2D density distribution of the blue dots. Red and violet contours represent the 75th percentile for the X and Y components of head orientation, respectively. Right. Same logic but applied to the heads velocity vector. C. Left. Two examples showing the mean rotation of iris features over the course of a slow (top) and fast (bottom) trial. Colored lines show each of the 561 simulated cameras for a given scenario (one color per scenario); the black line shows the camera from the empirical data. Right. Trial-level mean fit score of each scenario with the empirical data is represented as a function of each subjects fitted gain. 20 dots represent 10 subjects x 2 trials. On the rightmost column, all values are aligned vertically to show the mean fit score across trials for the three scenarios. Size indicates the 75th percentile of head z component for each trial. C_FIG

Operant paradigms are powerful tools to quantify motivation and reward. Traditionally, operant conditioning research has been limited to food and drug reinforcers. Recent advances in commercially available operant equipment, however, allow for the quantification of social motivation. These operant assays are an improvement over commonly used social preference tasks, as they enable direct measurement of the effort and motivation driving social behavior. Based on a design by Venniro et al. (2020), the MedPC social operant boxes modify the traditional operant box setup for social interactions. The experimental rat can lever-press to raise a door for an interaction with a target rat behind a porous barrier. These social operant boxes have been widely adapted to test social behavior in adult and adolescent rodents and investigate how a range of conditions (e.g. stress, drug taking, etc.) affect social motivation. However, there is a gap in assessing maternal motivation for pups during the postpartum period, despite ample evidence that postpartum social behavior is highly relevant for offspring health outcomes. Here, we detail 3D-printed modifications to the standard Med PC social operant boxes to adapt the social target chamber to safely house neonatal pups. We have also developed testing protocols to assess motivation during the limited postpartum period. These data demonstrate that, with simple modifications to social operant chambers and testing protocols, the field can implement advanced behavioral approaches to directly assess maternal motivation.

Introduction: Decision making for selecting an object or a course of action from possible alternatives largely depends on our perceptual ability modulated by attention. When multiple stimuli appear close together in time, processing one stimulus can temporarily impair the processing of another due to temporal limitations of attention. Observers frequently fail to detect the second target (T2) presented within a few hundred milliseconds after the first target (T1) in a stream of stimuli, which is commonly known as attentional blink (AB). Existing theories attribute this perceptual lapse to T1 processing, distractor interference, or transient attentional gating; however, the computations underlying suppressive mechanism remains unresolved. We investigated whether pupil-size could reveal the underlying mechanisms of AB and predict conscious perception on a trial-by-trial basis. Methods: Pupil diameter and gaze locations were recorded using an infrared eye tracker. Machine learning techniques were used to classify trials when T2 was detected versus when it was not, after correct identification of T1, during an AB task from the pupil dynamics, which also yielded attentional episode (AE) associated with each element in the stream of visual stimuli when deconvolved. Results: Cross-validating classifiers achieved near-perfect accuracy not only in distinguishing but also predicting perceptual outcomes on a single-trial basis. AEs exhibited greater power when T2 was detected than when it was missed; the differential power in AEs on a logarithmic scale was highly synced with the differential pupil size. Conclusions: Collectively, these findings establish a framework for predicting attention-driven perceptual outcomes from pupil-dynamics at finer time-scale.

Why does an angry face feel uncomfortable? The answer is that it signals a threat. However, a face is only part of an encounter, and distance, facial stimulus type, and gaze may shape discomfort regardless of perceived anger. To separate these cues, we conducted three within-subjects virtual reality experiments. In each experiment, 24 adults viewed avatars at intimate, personal, and social distances (30, 100, and 300 cm, respectively) and rated the faces perceived anger and their own discomfort; head movement was recorded in Experiments 2 and 3. In Experiment 1, the expression (angry, neutral) and facial color (natural, red) were crossed with distance; in Experiment 2, a featureless mannequin served as a nonface comparison; and in Experiment 3, the gaze direction (direct, averted) was manipulated. Expression primarily determined perceived anger, whereas distance predominantly determined discomfort: A nearby neutral face was uncomfortable despite low perceived anger (Experiment 1). A neutral human face was more uncomfortable than a mannequin, although both received similarly low perceived-anger ratings (Experiment 2). Direct gaze increased the discomfort without changing perceived anger (Experiment 3). Backward head movement exhibited a similar pattern, with participants leaning back more from human faces than from the mannequin. These results indicate that the discomfort associated with an angry face is not merely explained by perceived anger. Instead, social discomfort was differentially associated with interpersonal distance and gaze direction and differed between the human-face and mannequin conditions.

The photic sneeze reflex (PSR), in which exposure to bright light triggers sneezing, is widely recognised but inconsistently defined and measured. We conducted the first systematic review and meta-analysis of PSR prevalence to synthesise the epidemiological evidence, assess methodological quality, and identify priorities for future research. We included 18 articles comprising 31 study groups and extracted prevalence estimates, study characteristics, ascertainment methods, and epidemiological information. Fifteen eligible study groups classified as healthy were included in the primary meta-analysis. The pooled prevalence was 22% (95% CI, 15%-29%), with extreme between-study heterogeneity (I2 = 99.3%). Reported prevalence estimates and associations with participant characteristics varied widely, and nearly all studies were judged to be at high risk of bias. The pooled estimate should therefore be interpreted as a descriptive summary of the available evidence rather than a precise estimate of population prevalence. Future studies require a standardised operational definition, representative sampling, transparent reporting, and reproducible methods for assessing light-triggered sneezing.

Effective animal monitoring is essential for assessing health, behavior, and environmental interactions, particularly in research and welfare contexts. This study presents a low-cost, open-source system designed for non-invasive monitoring of budgerigars (Melopsittacus undulatus), a small parrot species frequently used in animal behavior research. The system integrates a perch-based scale for voluntary weight measurement, a temperature sensor, and a camera for image capture, all controlled by a Raspberry Pi. By leveraging fine-tuned neural networks, the system achieves automated individual recognition with high accuracy, eliminating the need for invasive tagging methods. The modular design ensures accessibility, scalability, and minimal disturbance to the animals, while the accompanying software streamlines data collection, processing including labeling, and visualization. This approach provides a comprehensive solution for continuous monitoring, offering valuable insights for research and husbandry while prioritizing animal welfare.

BackgroundThe Trail Making Test (TMT) is a widely used instrument for assessing executive functions due to its sensitivity. But, its traditional scoring, based solely on total completion time, limits its specificity by losing the rich behaviour required to complete the task, involving integrating visual search, motor planning and task switching. A computerised implementation of the TMT (cTMT) allows high-resolution cursor trajectories to be recorded, offering access to fine-grained features of the trajectory, while an online administration improves accessibility and statistical power. ObjectiveThis study aimed to extract high-resolution cursor trajectory features from an online cTMT and evaluate their capacity to predict individual differences in core executive functions, such as visual working memory (VWM), inhibitory control and age, using machine learning, as well as validating the feasibility of a fully online data acquisition pipeline as a basis for digital biomarkers. MethodsParticipants completed an online battery comprising the cTMT and three validation tasks: the Change Detection Task (CDT), the Stop-Signal Task (SST) and the Go/No-Go task (GNG). A total of 104 features (26 features x Part A/B x whole-trial/first-10-targets) were extracted from cursor trajectories, including trajectory profiles and segmentation into latent states: Search, Travel and Hesitation. Regression models were trained within a nested Leave-One-Out cross-validation framework with inner 10-fold hyperparameter tuning, feature selection and standardisation applied strictly within folds. Performance was assessed via mean absolute error (MAE), normalised error (MAE/SD) and permutation testing; SHapley Additive exPlanations (SHAP) were used to characterise feature importance. ResultscTMT features strongly predicted age across all models (p < .001), with MAEs roughly one-third lower than the targets dispersion, driven predominantly by distance-based "circuitousness" metrics from both parts. GNG accuracy and c-coefficient were both significantly predicted, but with a dissociated pattern: accuracy was best explained by Part B (alternation) metrics, whereas the c-coefficient was almost exclusively predicted by Part A (simple sequencing) metrics. SST response time (SSRT) was not significantly predicted by any model. VWM, characterised by the mean Cowan s K, was significantly predicted mainly by the more complex models, and involving state transitions and search phases in both Part A and B. ConclusionsMoving beyond completion time, cursor trajectory dynamics from an online cTMT provide a rich behavioural signal for predicting age, VWM capacity and distinct aspects of inhibitory control. The dissociation between Part A and Part B predictors supports differentiated cognitive processes within the TMT and positions the cTMT as a scalable, portable digital biomarker with promise for computational psychiatry and personalised neuropsychology.

Difficulties understanding speech in noise remain a common complaint even among listeners with normal hearing sensitivity, highlighting the need for objective, more effective measures of real-world listening. The goal of this study was to validate the use of a novel, chirped-speech (Cheech) stimulus - continuous, naturally-spoken speech fused with chirps designed to elicit robust auditory evoked potentials - to characterize relationships between speech recognition, listening effort, and auditory neural encoding. Twenty-five normal-hearing adults completed a sentence-recognition task using both original (unmodified) and Cheech-modified AzBio sentence lists in quiet, +3 dB, and -3 dB signal-to-noise ratio (SNR) conditions while neural responses from the brainstem through cortex were recorded simultaneously. Speech recognition remained near ceiling in quiet but declined with decreasing SNR for both original and Cheech stimuli. Compared with clean speech, Cheech-modified speech showed slightly poorer recognition performance as SNR decreased and somewhat higher perceived effort overall. Yet, Cheech was highly effective at evoking auditory responses from the brainstem (auditory brainstem response, ABR) through the cortex (including middle- and late-latency responses, MLR and LLR) even with <5 minutes listening time per condition. Neural responses showed reduced amplitudes and prolonged latencies as SNR decreased. In general, ABR latencies and wave I amplitudes were associated with speech-in-noise recognition performance, whereas cortical responses (MLR Na, Nb, and LLR P1) were associated with subjective workload. These findings show that Cheech-modified speech preserves intelligibility while yielding robust, multilevel neural recordings during sentence perception, offering a promising approach to examine hierarchical auditory processing under ecologically relevant speech-in-noise conditions.

Behavioral test batteries are increasingly used to characterize multiple functional domains in zebrafish, yet the potential impact of test sequence on behavioral outcomes remains poorly defined. Here, we systematically evaluated whether test order influences behavioral responses in a three-assay battery comprising the novel tank test (NTT), mirror-induced aggression (MIA), and social preference (SP) test. Adult zebrafish (Danio rerio) were exposed to all possible permutations of the three assays in a fully counterbalanced design, allowing assessment of order effects across locomotor, anxiety-like, aggression-related, and social behaviors. Test order produced modest and parameter-specific effects, primarily affecting locomotor activity in the NTT and social proximity in the SP assay. Time-course analysis revealed within-test behavioral dynamics, with limited evidence that test order modulates early adaptation or late engagement with the testing environment but does not alter overall temporal response profiles. Sex-dependent effects were assay-specific and most pronounced in the NTT, with no consistent sex differences observed in MIA or SP. To evaluate the global structure of behavioral variation, Principal Component Analysis (PCA) was performed across assays. Despite localized effects of test order, no clear multivariate separation between test sequences was observed, indicating that sequential testing does not produce distinct baseline phenotypes. Together, these findings support the robustness and reproducibility of multidomain behavioral batteries while highlighting the importance of standardized test-order reporting to improve cross-study comparability.

Light reaching the retina is a primary regulator of human circadian physiology, acting largely through melanopsin-expressing retinal ganglion cells with peak short-wavelength sensitivity. Delivering known, repeatable retinal doses outside the laboratory is difficult because conventional light sources leave viewing geometry, gaze, and ambient conditions uncontrolled. Consumer extended-reality (XR) glasses fix a bright binocular display in constant geometry relative to the eye, but their suitability as calibrated photic stimulators has not been established. Here we validate a commercial micro-OLED XR display (VITURE Luma Ultra) for controlled retinal photostimulation. A purpose-built host application renders exact 8-bit RGB stimuli while independently controlling hardware brightness and logging all intensity-determining state; spectral radiance was measured at the retinal position of a 3D-printed phantom head with an open-source miniature spectroradiometer, anchored to absolute units by a luminance transfer calibration. The blue primary peaks at 461 nm (FWHM 43 nm), is spectrally invariant across a >10-fold intensity range, and at maximum output delivers an estimated 299 lx melanopic equivalent daylight illuminance, above consensus daytime recommendations, while remaining roughly two orders of magnitude below photobiological safety limits. The red primary is visually effective with minimal melanopic drive (melanopic DER 0.10), enabling spectrally shifted evening stimulation. Unlike the immersive virtual-reality headsets previously used for calibrated light delivery, the see-through form factor preserves the wearer's view of the surroundings--relevant for clinical monitoring in supervised settings such as the intensive care unit. These results show that consumer XR glasses can serve as a dose-calibrated platform for wearable photostimulation using an open-source measurement chain, and provide groundwork for application-layer dose-response studies.

Objectives: Dentists prescribe approximately one in ten antibiotics worldwide, yet antimicrobial stewardship (AMS) remains underemphasized in dental education. Large language models (LLMs) may support AMS training, but their proficiency and clinical reasoning in this context remain unclear. We evaluated GPT-4o's accuracy and clinical reasoning on dental antibiotic prescribing questions, stratified by question difficulty. Methods: We assembled 125 multiple-choice questions on dental antibiotic prescribing from eight peer-reviewed studies (2017-2023). GPT-4o answered each question and generated a clinical justification. Accuracy was assessed against source-study answer keys and examined across difficulty quartiles. Justifications were evaluated using an adapted 12-axis human-evaluation framework assessing scientific consensus, extent and likelihood of harm, inappropriate and missing content, bias, and both correct and incorrect comprehension, retrieval, and reasoning. Prophylaxis-specific questions were analysed separately. Results: GPT-4o correctly answered 72% of questions. Accuracy remained relatively stable across difficulty quartiles (78%, 78%, 65%, 70%). Experts rated 95.4% of justifications positively across the 12 axes. Comprehension, retrieval, and reasoning each exceeded 96.2% positive ratings. Missing content was the main weakness (7.8%), and 7.1% of justifications showed a moderate-to-severe potential for harm. Performance on prophylaxis-specific questions (98.1%) exceeded non-prophylaxis questions (93.0%). Conclusions: GPT-4o demonstrated moderate-to-high proficiency and clinically defensible reasoning in dental antibiotic prescribing questions. However, residual risks indicate that it is not suitable for unsupervised clinical use but shows potential as a supervised AMS educational tool.