Behavior Research Methods

Purpose: To evaluate the feasibility, validity, and scalability of PLFest, an open-source, Unity-based, cross-platform application designed for standardized, multi-site visual and cognitive assessment and training. Methods: Two hundred sixty participants (mean age = 23 years) were recruited across four university sites in the United States. Participants completed a battery of five visual assessments administered through PLFest, including visual acuity, contrast sensitivity, spatial frequency cutoff, contrast sensitivity at spatial-frequency cutoff, and visual search. Five cognitive assessments measuring visuospatial working memory, verbal working memory, fluid reasoning, inhibitory control, and selective attention were also administered. Descriptive statistics and performance distributions were examined and compared with normative data. Results: Visual acuity and contrast sensitivity measures closely matched previously reported normative values obtained using established clinical and psychophysical methods. Spatial frequency cutoff and visual search tasks produced stable threshold estimates while showing substantial inter-individual variability. Performance across all cognitive assessments was consistent with published validation studies of the corresponding tasks. Across the full battery, adaptive procedures demonstrated reliable convergence and generated well-distributed performance measures without evidence of substantial floor or ceiling effects. Importantly, these findings were observed across four geographically distributed testing sites using standardized consumer-grade tablet hardware. Conclusions: PLFest provides reliable and scalable assessment of visual and cognitive function using portable consumer devices. The platform supports standardized data collection across distributed research settings while maintaining performance characteristics consistent with established laboratory and clinical benchmarks. These findings support the use of PLFest as a reliable framework for large-scale studies of vision and cognition. Translational Relevance: By reducing dependence on specialized laboratory infrastructure and trained personnel, PLFest may facilitate broader access to visual and cognitive assessment, enabling large-scale research, screening, and future rehabilitation applications.

Joint music making offers an ecologically powerful framework for investigating human social interaction and synchronization. Yet, experimental paradigms often rely on traditional instruments that limit accessibility, reproducibility, and experimental control. In parallel, the use of music for therapy and rehabilitation is expanding, motivating the development of digital musical instruments that can serve research, educational, and clinical purposes. Here, we introduce the e-Music Box Roma (eMB Roma), an open, reproducible digital musical instrument designed to study music making behavior regardless of musical training. The eMB Roma plays preregistered music with tempo controlled by hand rotary movements. Building on the original e-Music Box (Novembre et al., 2015), the eMB Roma retains its intuitive rotary hand control while introducing major innovations: a fully open and 3D-printable design, modular hardware with integrated slider and button controls, polyphonic output with multiple simultaneous instruments, and MIDI compatibility. Additionally, a dedicated graphical user interface allows real-time monitoring, experiment control, device synchronization (like neuroimaging or motion capture devices), and both solo and joint music-making paradigms. The eMB Roma provides a flexible and accessible platform for research contexts, allowing experimental control, reproducibility, and future extensions. Its open design and modularity make it suitable not only for research but also for therapeutic, rehabilitation, and educational applications, where it can support personalized interventions and quantitative assessment of motor performance.

Pupillometry provides an objective way to index effort across domains. However, pupil size is strongly affected by luminance changes, which can obscure effort-related effects, and limit its use in most applied scenarios with dynamic visual input. We here introduce and validate a method to overcome this problem. To this end, participants performed an auditory n-back task of differing difficulty while viewing either constant visual input or dynamic driving movie clips. Effort was assessed physiologically (pupil size), behaviorally (accuracy), and subjectively (NASA-TLX). Accuracy, questionnaire scores, and pupil size were analyzed at the session level, while pupillometry additionally provided continuous time-resolved information. As expected, pupillometry tracked differences in effort, but its discriminability was substantially reduced under dynamic visual input. Correcting for the effects of overall luminance and moment-to-moment luminance changes using a dynamic, explainable, and open-source modeling procedure (Open-DPSM) considerably improved effort discriminability on both aggregate and time-resolved levels. At the aggregate level, luminance-corrected pupillometry slightly outperformed accuracy and NASA-TLX. Combining all three measures yielded the highest classification performance (AUC = 0.98), supporting the view that effort is multifaceted and best captured multimodally. These findings establish a practical basis for fine-grained physiological tracking of effort and arousal in both fundamental and applied research using complex, dynamic stimuli. A tutorial section guides researchers in applying luminance correction to their own pupillometric data in dynamic viewing environments using the here validated approach.

Extended Reality (XR) combines experimental control and ecological validity, yet behavioral XR research lacks shared infrastructure: building immersive experiments demands specialized engineering, and custom tools yield data in custom formats that other laboratories cannot readily reanalyze. We present ResXR (Research with XR), an open-source toolkit providing a path from immersive experiment to standardized dataset and quality report, running on standalone headsets. A Unity template records synchronized head, hand, eye, and face tracking with per-sample hardware timestamps; an independent Python pipeline validates quality, masks flagged intervals, and exports raw and derivative datasets in Motion-BIDS format with self-contained quality reports. Three ready-to-run paradigms span common behavioral designs. ResXR is an idea new to XR research: sensor data from consumer headsets must be empirically validated rather than taken from vendor documentation, grounding its schema and quality flags in stress-tested sensor behavior. Its aim is a transparent, community-extensible foundation for reproducible XR experimentation.

Engagement is widely recognised as central to learning and academic achievement. Electrodermal activity (EDA) has emerged as an objective physiological indicator of engagement, as it measures sympathetic nervous system activation. However, the high cost of wearable EDA sensors has limited its widespread application. This study answers the call for affordable, high-temporal-resolution engagement measures by validating a video-based quantitative assessment method. Researchers collected 75 minutes of synchronised EDA and video data from 12 upper secondary students (aged 17-18) during regular instruction. Novel software was developed to analyse student movement and sound level for academically relevant content. The OpenPose AI model for pose estimation was also applied. This approach produced six distinct movement variables: two AI-based and four non-AI-based. Six linear models using varying movement variables and sound level were tested to predict tonic EDA levels. All models effectively predicted EDA levels, with non-AI-based movement metrics outperforming AI-based alternatives. The four non-AI-based movement models showed similar performance, indicating that compressed versions reduced computational time without sacrificing predictive power. These findings validate a novel, objective method for comparing engagement across learning activities on short timescales. This method is particularly useful for collaborative learning environments and enables controlling for movement and sound in quantitative classroom analyses.

BackgroundMice make substantial eye movements during head-fixed visual stimulation, and uncorrected gaze shifts corrupt receptive field measurements and confound stimulus-response relationships. Corneal-reflection video oculography in rodents has provided the methodological foundation for calibrated angular gaze tracking since Stahl (2004) but the calibration procedures used by existing methods -- physical camera rotation, motorized stages, behavioral tasks, or precisely co-aligned dual cameras -- have limited their adoption in many mouse neuroscience laboratories. Most studies instead use uncalibrated pupil tracking, deep learning pose estimation that returns pixel coordinates without angular calibration, or learned shifter networks that lack independent validation. New methodWe present an open-source corneal-reflection eye tracking system for head-fixed mice with two methodological contributions. First, a geometric model recovers gaze in calibrated angular units from the pixel displacements of the pupil and corneal reflections, using the known 3D positions of multiple fiducial LEDs as the source of angular scale. The model requires no estimate of Rp, the per-animal eye-geometry parameter that earlier corneal-reflection methods determine through physical calibration. Second, a self-calibration procedure exploits the redundancy of multiple stationary fiducial LEDs: each LED produces an independent gaze estimate from the same geometric model, and a single residual calibration parameter is determined by minimizing the disagreement between per-LED estimates. This replaces the physical camera-rotation calibrations of earlier video oculography (Sakatani and Isa, 2004, 2007; van Alphen et al., 2013; Kretschmer et al., 2017), the motorized stages of Zoccolan et al. (2010), and the precision dual-camera alignment of Payne and Raymond (2017) with a software operation that requires no moving parts, no behavioral task, and no per-animal procedure. The system provides three interactive GUI stages: (1) pupil and LED detection via Difference-of-Gaussians filtering, (2) 3D geometry definition, and (3) gaze angle computation with blink detection, fiducial correction, and manual curation. ResultsValidation against a rotary-encoder-controlled artificial eye demonstrated mean absolute errors below 1{degrees} across all four fiducial LEDs over the {+/-}20{degrees} working range of mouse eye movements, with Pearson correlations exceeding 0.998 between our methods estimation and encoder ground truth. The self-calibration reduced inter-LED disagreement by a factor of 4-6 in mouse recordings. Gaze-corrected stimulus reconstruction applied to Neuropixels recordings from mouse V1 produced qualitatively sharper receptive field estimates with improved signal-to-noise ratios. Comparison with existing methodsOur method is the first multi-LED, single-camera, fully software-calibrated corneal-reflection eye tracker for mice and includes an integrated open-source pipeline for detection, calibration, blink handling, and artifact correction. The multi-LED redundancy doubles as an internal consistency check -- if two LEDs disagree on gaze direction, the calibration is wrong -- providing a guarantee that learned approaches relying on neural-data-derived correction cannot offer. ConclusionsOur method makes calibrated corneal-reflection eye tracking accessible to non-specialist mouse laboratories using consumer-grade hardware ([~] $2,000-2,700 USD), eliminates the per-animal calibration procedures of earlier methods, and is validated by two independent ground truths at both the absolute angular (artificial eye) and functional (V1 receptive fields) levels. HighlightsO_LIOpen-source corneal-reflection eye tracking for head-fixed mice using a single camera and multiple stationary fiducial LEDs. C_LIO_LIGeometric gaze model derives angular scale from LED positions, eliminating per-animal eye-geometry calibration. C_LIO_LISelf-calibration via multi-LED redundancy replaces physical camera rotation, motorized stages, and dual-camera precision alignment. C_LIO_LIValidated to sub-degree accuracy against a rotary-encoder ground truth across the {+/-} 20{degrees} range of mouse eye movements. C_LIO_LIGaze correction produces sharper V1 receptive field estimates in Neuropixels recordings. C_LI

Cognitive impairment is a prevalent symptom extending from physiological ageing to disease. It commonly manifests itself in initial memory problems, progressing and co-occurring in more severe conditions such as Mild Cognitive Impairment, Alzheimer's Disease and Major Depressive Disorder. However, current non-invasive screening assessments either lack biological information or are invasive and restricted to specialized centers with complex and cost-intensive set-ups. Here, we conducted an initial validation of mobile pupillometry with Virtual Reality (VR) under experimental conditions as a digital biomarker for cognitive impairment by testing required biomarker-specific properties. For this purpose, we first assessed its construct validity by testing healthy participants (n=43) on an n-back task in VR while pupil size was measured. Mixed effects models revealed that similar to lab-based eye-tracking systems, pupil size increased in a sensible and distinguishable fashion as a function of working memory load. Second, to test the signal's reliability, the same participants were tested on the identical set-up two to three months after their first visit. We observed that the pupil response profile was highly stable over this period. Third, for its clinical validity, we examined patients (n=89) from three different cohorts with varying degrees of cognitive impairment and compared them to healthy control participants (n=81). Mixed-effects models indicated that pupil size was reduced as a function of cognitive impairment levels at higher cognitive load and that this effect was stronger pronounced with increasing age. In conclusion, we provide initial evidence for mobile pupillometry being a sensitive, reliable and clinically valid digital biomarker for cognitive functioning and impairment, which offers desirable properties due to its quick, automatized and location-independent set-up. Keywords: digital biomarker, mobile pupillometry, Virtual Reality, cognition, , Major Depressive Disorder, Mild Cognitive Impairment, Alzheimer's Disease

Objective. Figure copy and recall tests are sensitive measures of visuoconstruction and visual episodic memory, but their clinical is constrained by labor-intensive manual scoring. We developed and validated an automated, element-level scoring pipeline using Vertex AI object detection for the tablet-based figure copy and recall tasks in the California Cognitive Assessment Battery (CCAB). The automated scoring pipeline duplicated the scoring procedures used by expert manual raters. Methods. A normative sample of 2,011 community-dwelling adults aged 18-90 completed figure copy and delayed recall trials at baseline, with subsamples retested at 1 day and at 6, 18, and 30 months. Participants completed the drawings with their index finger on a tablet computer with finger position digitized to analyze the speed and timing of individual drawing strokes A convolutional object-detection model trained on the Vertex AI AutoML Vision platform identified each of twelve canonical figure elements in rendered drawings. Separate element presence and location scores were computed after homographically warping drawings onto a canonical template to produce trial-level Element, Location, and Total scores. To compare Vertex and human scores, Vertex AI and expert human raters independently scored 1500 randomly selected drawings to evaluate inter-rater agreement, including a common subset of 100 drawings scored by Vertex AI and all raters. Results. Total scores were virtually indistinguishable (r = 0.966) from human-human agreement (mean r = 0.971) as were Element presence scores (mean r = 0.959 vs. r = 0.963). Location-score agreement (r = 0.951) was slightly below the human-human mean (r = 0.972) due to pixel-level analysis by Vertex AI that was impossible for human raters. The Vertex pipeline showed no preferential advantage for the single expert rater who categorized Elements during training. Automated scores showed strong demographic gradients, age effects on Recall (r = -0.32) were approximately twice those in Copy conditions (r = -0.16). A Memory Cost score (Recall - Copy) showed a monotonic age-related decline from +0.40 z in the youngest subjects to -0.54 z in the oldest. Kinetic analysis revealed that drawing speed and efficiency showed significant age-related changes. Overnight test-retest reliability was high (Recall r = 0.72) and the Recall trial showed a large overnight learning effect ({Delta} = +1.18) that continued with repeated tests up to 30 months ({Delta} = +0.75).

Mind-wandering (MW) is a frequent and pervasive phenomenon, yet it is commonly assessed using self-reports or probe-based methods that offer limited temporal precision regarding its onset. In this study, we introduce a novel paradigm, ReMind, that estimates the onset and duration of MW episodes during natural reading by combining retrospective self-reports with eye-tracking. Participants indicated the words where they believed their mind started and stopped wandering, and these reports were aligned with gaze timestamps to estimate MW onset. Using data from 44 participants, we examined whether knowledge of MW onset improves the detection of MW from eye-tracking signals. To evaluate relevance for both self-report and thought-probe paradigms, we additionally simulated thought probes by randomly sampling time points during reading. Logistic regression classifiers trained on eye-tracking features extracted from time windows anchored to MW onset achieved AUROC scores of 0.659 and 0.621 under the self-report and simulated thought-probe paradigms, respectively, using leave-one-subject-out cross-validation. In both cases, onset-aligned windows outperformed classifiers trained using arbitrary MW windows. Sliding-window analyses further revealed systematic temporal changes around MW onset, with classification performance peaking at approximately 3 seconds after onset. Feature-level analyses showed reduced fixation rate and fixation dispersion, along with increased pupil size following MW onset. Together, these findings characterize the temporal progression from on-task reading to MW. Overall, ReMind provides a useful framework for studying the temporal dynamics of MW during naturalistic reading.

One of our sensory systems key functions is to detect threats in the environment. Sensory information eliciting negative emotions, such as fear or disgust, triggers instinctive avoidance reactions. This core survival mechanism is believed to be expressed as subtle non-conscious postural reactions, even when participants are instructed to stand still. Such avoidance behavior has mainly been studied using indirect measures that make participants aware of their posture (e.g. force-plate based methods) or measures that depend on explicit cognitive tasks, like moving a joystick to indicate an urge to approach or avoid the stimulus; experimental tasks with limited ecological validity and generalizability. Therefore, despite the importance of this basic survival strategy, its underlying mechanisms are still poorly understood. Here, we used a novel 3D-camera-based method allowing direct but implicit measures of postural reactions with high precision. Participants are aware that they are being filmed but, crucially, are not informed that distance measures are obtained. We assessed this ecologically valid measure of approach/avoidance responses in two different sensory modalities: olfaction and vision. Participants were standing upright while exposed to either olfactory or visual stimuli and verbally rating their perceived valence in each trial. In response to subjectively unpleasant odors and images, participants moved away from the stimulus source, as compared to pleasant stimuli. These results demonstrate a putative modality-independent early proxy for avoidance behavior in response to perceived negative valence. Considering its face validity and general applicability, this novel experimental method presents new possibilities for assessing non-conscious approach-avoidance responses in humans.

Background: Large language models (LLMs) demonstrate strong performance in controlled medical environments such as multiple choice exams, but their utility in real-world clinical workflows remains unproven. The NHS Advice & Guidance (A&G) service, where Primary Care clinicians can submit text-based queries to specialists, provides an environment for evaluating the clinical performance of LLMs as a specialist. Methods: We compared responses from MedGemma 4B-IT, an open-weight model deployed locally on hospital infrastructure, against specialist neurologist responses across 50 adult neurology A&G cases from University College London Hospital. Two neurologists and two GPs rated 80 blinded and 20 unblinded responses for outcome, safety, efficacy, and feasibility using standardised criteria; outcome was a binary correct/incorrect, while other domains were scored 1-5. Inter-rater reliability was assessed using intraclass correlation coefficients. Results: Although there were no statistically significant differences between blinded specialist neurologists and LLM responses across any domain (outcome: 84% vs 82%, p=0.67; safety: 3.98 vs 4.02, p=0.85; efficacy: 4.06 vs 3.98, p=0.61; feasibility: 4.39 vs 4.20, p=0.45), 10% of LLM responses received concerning scores ([≤]2 average score) compared to 0% of human responses, indicating potentially clinically important tail risk. Furthermore, unblinded results showed a preference for human responses, with human ratings being preferred across all domains. Only 51% of binary outcomes had unanimous agreement and inter-rater agreement was moderate across other domains (ICC 0.50-0.52). Conclusions: In this pilot study, aggregate scores between blinded human and LLM responses were similar, and no statistically significant differences were detected in this exploratory sample. However, aggregate metrics masked clinically important edge-case failures in LLM responses. Pronounced inter-rater variability and the potential impact of LLM/human syntax on blinded rater judgements highlight the challenges in establishing robust evaluation frameworks for clinical LLM deployment

Continuous Psychophysics, which couples a continuous stimulus with a continuous response, is a promising tool to break out of the confines of traditional designs based on discrete trials. In this pre-registered study, we explore to what extent this paradigm is useful in the study of multisensory integration. We expand on Tonelli et al.s (2025) seminal study by additionally examining the role of eye-movements, using a Kalman filter to estimate the sensory noise underlying behavioral tracking parameters and employing a virtual reality set-up. We immersed two cohorts of participants (n = 30 each) in a virtual meadow environment and asked them to continuously track a drone (Experiment 1) or a swarm of flies (Experiment 2) with a controller, while simultaneously recording their eye movements. We manipulated the reliability of visual cues using four levels of fog (from a completely clear view to impenetrable fog where no visual cues to the targets position were available) as well as the presence of sound cues emitted from the object (sound present/absent). The maximum correlation between stimulus and response was higher when sound was present in some conditions, particularly when visual uncertainty was high, while the tracking delay remained unaffected across all fog levels. Using a Kalman filter to estimate the underlying sensory noise, we found strong evidence that sensory noise was lower when sound was present than when sound was absent both for manual and for ocular tracking, particularly for those conditions with higher visual uncertainty. In exploratory analyses, we further show strong correlations between manual and ocular tracking in all measures (maximum correlation, tracking delay, sensory precision). However, when isolating the multisensory advantage, these correlations all but disappeared for maximum correlation and tracking delay, while remaining substantial for sensory precision. Similarly, behavioral tracking correlated generally strongly with underlying sensory noise, but much less so when it came to the advantage conferred by added sound cues. Our results show that continuous psychophysics is well-suited for the study of multisensory integration, particularly when a Kalman filter analysis is used to estimate sensory uncertainty from behavioral data.

This paper presents a six-stage methodological framework for Convolutional Neural Net-work (CNN)-based cetacean vocalization detection and classification in Passive Acoustic Monitoring (PAM), implemented as the open-source toolkit ai-pam-pipeline. The frame-work is generalizable across species and fully parameterised through a single configuration file, guaranteeing exact experimental reproducibility. Two experiments are reported. Experiment A examines the effect of FFT window length Nfft [isin] {256, 512, 1024} on binary Bottlenose dolphin (Tursiops truncatus) whistle detection using stratified 10-fold cross-validation on an in-domain dataset (Oltremare, 192 kHz) and a cross-domain benchmark (DCLDE 2022). In-domain performance is uniformly high (macro F1{approx} 0.98; Wilcoxon, all p > 0.05). Cross-domain results diverge substantially: Nfft = 256 is significantly superior (p = 0.006, rank-biserial r = 0.89). The mechanism is an upsampling amplification effect: coarser spectral bins produce wider, higher-contrast FM traces after bilinear resampling to fixed image dimensions. This superiority is threshold-invariant: precision equals 1.000 across all configurations and thresholds{theta} [isin] [0.1, 0.9], confirming that the advantage is not an artifact of threshold choice. These findings demonstrate that preprocessing choices -- often treated as secondary implementation details -- can significantly affect cross-domain generalisation. While Nfft serves here as a controlled case study, the framework is designed to enable systematic, reproducible evaluation of arbitrary preprocessing parameters within a unified experimental protocol. Experiment B demonstrates multiclass capability on five T. truncatus vocalization cate-gories (macro F1 = 0.843); inter-class confusion between click trains and burst-pulse sounds reflects biological signal overlap rather than classifier failure.

Efficient face-to-face communication relies on the integration of auditory speech and visual articulatory signals. Over the past five decades, the McGurk illusion has been widely used as an index of audiovisual speech integration. However, substantial variabilities in susceptibility to the illusion across participants and speakers limit its reliability as a stable measure of audiovisual integration ability. Here, we introduce the McGurk illusion dataset (MID), which, to our knowledge, is the largest publicly available McGurk stimulus dataset to date. The MID comprises auditory (N = 400), visual (N = 400), and audiovisual (N = 640) speech stimuli generated from 80 Mandarin speakers and validated through behavioral judgments across 360,900 trials. Using this dataset, we characterized the acoustic and facial articulatory properties of McGurk stimuli, replicated substantial inter-participant and inter-speaker variabilities in illusion susceptibility, and revealed the associations between variations in McGurk illusion rate and the variations in unisensory perception, audiovisual correspondences, and speakers characteristics. Furthermore, the stimulus set enabled systematic comparisons of the reliability of different McGurk illusion-based indices of audiovisual speech integration. Overall, the MID not only provides a standardized resource for investigating audiovisual speech integration and its alterations across populations, but also supports research on speaker normalization, lip-reading, and speech perception.

Zebrafish (Danio rerio) are an important vertebrate model for vision and neuroscience research. In the larval stages, the aquatic species begins to elicit the optomotor response (OMR) to stabilize themselves in water -- a behaviour that may be exploited in the laboratory to measure visual acuity. However, up to now, the measurement of the OMR in juvenile and adult zebrafish has been limited due to their behavioural complexity. Here, we optimize a protocol to assay zebrafish aged between 4 and 9 weeks-post-fertilization, by displaying sinusoidal gratings parallel to the zebrafish eye to elicit a robust OMR. We assessed the visual spatial-frequency tuning function of an environmentally induced myopia model to confirm the sensitivity and robustness of the protocol. Additionally, we show the OMR is sensitive to the contrast and temporal resolution of the sinusoidal gratings. Furthermore, we found that the time between stimulus presentations impact the spatial-frequency tuning function likely as time is required for zebrafish to return to baseline swimming after eliciting the OMR. Finally, we found that the OMR after ten versus twenty seconds of stimulus onset appears comparable; indicating that robust OMR responses in zebrafish can be elicited through relatively short stimulus presentations. Through the experiments conducted, we present an optimized protocol specific to zebrafish. The protocol may be used to follow the progression or treatment efficacy of progressive neurological disorders including specific visual disorders and higher brain functions with visual endophenotypes. Ultimately, this protocol allows for high-throughput robust measures of visual and neural function in zebrafish.

Selective auditory attention decoding (AAD) enables tracking which of multiple concurrent speakers a listener attends to and is a key building block for neuro-steered hearing devices. While AAD integrated in a closed-loop system with real-time neurofeedback (NFB) is hypothesized to improve decoding through neural adaptation and error-correction behaviour, the short-term behavioral and algorithmic impact of such a bilateral human-machine interaction remains poorly understood. Here we evaluated the effects of NFB on AAD accuracy and user experience in a single-session AAD paradigm with online NFB involving nineteen participants. They performed a selective listening task with enforced attention switches across four conditions: open-loop (OL), closed-loop with auditory gain feedback (CLA), closed-loop with visual feedback (CLV), and a condition with pseudo-auditory gain control (psCLA) decoupled from the participants individual neural activity. AAD was performed online using both subject-specific and subject-independent linear decoders on 5 s sliding windows, followed by Hidden Markov Model post-processing. Online analysis showed comparable decoding performance across all conditions. However, offline posthoc analysis using subject-independent decoders revealed that AAD accuracy in the CLA condition was significantly lower than in the OL baseline. Subjectively, participants reported that CLA was significantly more distracting and required higher switching effort. Crucially, a causal analysis of the psCLA condition found no robust evidence that higher audio gains inherently improve decoding accuracy. Our results demonstrate that within a single-session paradigm with rapidly varying feedback cues, auditory neurofeedback may degrade AAD performance by increasing cognitive load and distraction. These findings suggest that suboptimal feedback can impede rather than facilitate learning. We conclude that more accurate and stable decoders and longitudinal, multi-session training protocols are likely essential prerequisites for achieving beneficial neurofeedback effects in closed-loop auditory attention systems.

1. Video-tracking programs have now become an essential tool for researchers measuring animal behavior across biological fields. The panel of available programs is growing rapidly, providing researchers with numerous specific tools that will match their precise needs. However, their proliferation may complicate post-tracking data processing, and some programs do not even provide tools for correcting tracking errors or analysing tracking data. In the case of commercial software, the loss of access to a program due to budget limitations or researchers' mobility from one institution to another could prevent them from accessing and visualizing their tracking data. 2. There is therefore a growing need for an accessible and flexible tool to handle post-tracking processes such as the correction and analysis of tracking data obtained across different video-tracking programs. 3. We present here the latest update of the video tracking and analysis program AnimalTA. With this new release, we propose to solve the above-mentioned problems by providing the scientific community with a program that will allow for data importation from other video-tracking programs. Like in its previous versions, AnimalTA remains a free, open-source, and highly user-friendly program, ensuring that it will always be accessible without restriction. Now, with this new importation option, users who performed their tracking with other programs can benefit from AnimalTA's complete toolset of data visualization, correction, and analysis. 4. Finally, this article gives an overview of the other main improvements associated with this new release. The program is now faster in both video importation and tracking, proposes an amplified toolset for data visualisation and correction, and features new options for data analysis.

Visual search is a critical component of many professions such as military operations, baggage screening, and radiology. Aided Target Recognition (AiTR) systems are designed to highlight potential threats across the operator visual field in real-time, directing attention and improving accuracy. However, these systems may impact search and, consequently, situational awareness by diverting attentional resources from non-highlighted, yet relevant, locations. Previous work suggests that scene gist is extracted within the first 250 ms of scene onset (Vo & Henderson, 2010). As such, this study examined whether a 250 ms AiTR onset delay could encourage a more even distribution of attention. Participants searched synthetically generated scenes and classified each person in the scene as armed or unarmed. Depending on their condition, participants either saw the scenes unaugmented (No AiTR condition), with AiTR highlights consisting of red bounding boxes around armed people and yellow boxes around unarmed (AiTR condition), or with AiTR highlights presented 250 ms post scene onset (Delayed AiTR condition). A surprise memory test of background objects presented in the search scenes was administered to all participants upon completion of the search task. As predicted and preregistered, results showed less overt attentional deployment to background information (anything other than the people themselves) in the AiTR condition compared to No AiTR , however, decreased overt attentional deployment was not seen in the Delayed AiTR group. A similar pattern was observed in the memory data (with the AiTR condition having a lower score than the No AiTR condition and the Delayed AiTR condition), this difference was not significant.

Background: Motor disturbances are common in neurologic and neurodegenerative syndromes. A standard motor speed and dexterity measure is the finger tapping test (FTT). The FTT has traditionally been administered in clinic using a mechanical FTT, limiting accessibility and early motor change quantification. This study assessed the validity of a smartphone app-based FTT, which may expand access and enable more frequent testing. Methods: The cohort was diagnostically diverse, including participants with frontotemporal dementia (FTD), progressive supranuclear palsy (PSP), corticobasal syndrome, primary progressive aphasia, multiple sclerosis, and clinically unimpaired controls. Participants completed a 20-second ALLFTD Mobile App (mApp)-FTT with each hand. Tapping speed metrics were extracted. Participants completed the gold-standard mechanical FTT, a neurologist-administered finger tapping exam, the PSP Rating Scale (PSPRS) and the Unified Parkinson`s Disease Rating Scale (UPDRS). Correlations assessed mApp-FTT and mechanical FTT relationships; regressions evaluated associations with neurologist-rated finger tapping impairment, PSPRS and UPDRS, adjusting for age and sex. Results: The mApp-FTT showed moderate-to-strong correlations with the mechanical FTT (dominant: r=0.63, p<0.001; non-dominant: r=0.55, p<0.001). Taps per second were associated with PSPRS motor severity (dominant hand: std. {beta}=-0.59, 95% CI [-0.91, -0.27], p<0.001) and the UPDRS (dominant hand: std. {beta}=-0.41, 95% CI [-0.82, 0.00], p=0.049). Flight time was modestly associated with neurologist-rated finger tapping impairment (dominant hand: std. {beta}=0.15, 95% CI [0.00, 0.29], p=0.044). Conclusion: These findings support mApp-FTT validity as a measure of motor function across neurodegenerative conditions. Validation in longitudinal and unsupervised remote settings is warranted to understand scalability and evaluate change over time.

Speech comprehension in noisy environments often requires cognitive effort, but listeners may disengage when comprehension becomes impossible. Eye movements have recently emerged as a promising new measure of listening effort, but it remains unclear whether eye movements are sensitive to the full effort profile across easy, difficult, and impossible speech comprehension. Across four experiments, participants listened to sentences at easy, difficult, and impossible levels of multi-talker background babble while pupil size and eye movements were recorded. Pupil size generally followed the expected inverted u-shaped effort profile: low for easy speech, maximal for difficult but still intelligible speech and lower again for impossible speech, although this pattern partly reflected sustained, condition-specific differences and not only sentence-evoked responses. Gaze dispersion - measuring the spread of eye movements - decreased with high temporal selectivity during difficult relative to easy and impossible speech, indicating reduced eye movements during active, effortful listening. However, gaze dispersion was also lower, but less temporally selective, during impossible compared to easy listening, especially in non-baseline-corrected analyses, suggesting that reduced eye movements do not index listening effort uniquely. Instead, eye movements appear to reflect both attentional engagement during difficult listening and disengagement or inward attention when meaningful listening is no longer possible. These findings indicate that pupil size and eye movements provide complementary indices of listening-related cognition, and highlight the integration of listening, cognition, and motor systems.