Behavior Research Methods

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.

Singing is an innate human behaviour present across cultures and the lifespan. Despite lacking direct biological advantages, its ubiquity suggests that it is intrinsically rewarding. This research aimed to investigate the underlying factors that explain variability in sensitivity to deriving reward and enjoyment from natural singing in the general population. In Study 1 (n = 606), an initial pool of items describing daily, non-professional singing behaviours were administered to an international adult sample. Exploratory factor analysis revealed a unidimensional structure of 20 items with acceptable model fit, organized into five facets representing distinct domains of singing-related rewards: 1) pleasure and emotional evocation, 2) social singing reward, 3) singing frequency, 4) mood regulation through singing, and 5) inattentional singing during routine tasks. In Study 2 (n = 430), confirmatory factor analysis in a new sample supported this structure. When both samples were combined (n = 1036), the unidimensional model defined by these five facets showed acceptable to excellent goodness-of-fit indices, supporting the conceptualization of singing reward as a multidimensional construct with differentiated facets. This led to the Barcelona-Aarhus Natural Singing Engagement Questionnaire (BANSEQ), which demonstrated excellent reliability ( = .94) and population-level stability. Study 3 (n = 1036) tested the convergent validity of BANSEQ with measures of music reward and engagement and identified sociodemographic and psychological correlates across the five facets of singing reward. Overall, these findings characterize the sources of individual differences in the hedonic experience of natural singing and propose BANSEQ as a robust psychometric tool for its assessment in the general population.

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.

Empathy, a key element of social interaction, involves both cognitive and affective processes and is commonly investigated through measures such as empathic accuracy and affective physiological synchrony. While physiological synchrony offers a continuous measure of affective processes, empathic accuracy typically relies on discrete self-reports, leaving their temporal relationship largely unexplored. Advancing this line of research requires datasets that integrate time-continuous self-reports with physiological signals, yet such datasets--particularly those focusing on the empathizee--remain limited. To fill this gap, we present EMPAC (Empathy Measurement: Physiological, Affective, and Cognitive), a multimodal dataset constructed. To create empathy-eliciting stimuli, professional actors performed emotionally intense, pseudo-autobiographical narratives while their physiological signals (e.g., ECG, EDA) and continuous self-reported emotional states were recorded. We then conducted two observer experiments using these video recordings. In Experiment 1, to validate the stimuli as empathy-eliciting materials, observers continuously rated emotional intensity without being informed of the specific emotion portrayed, following the protocol of previous studies on time-series empathic accuracy. Yet this approach sometimes revealed a gap between the emotion category portrayed by the target and that perceived by the observers. In Experiment 2, we introduced a revised procedure in which the target emotion category was disclosed prior to viewing, revealing that specifying the target emotion led to a different relationship between individual empathy traits and empathic accuracy than observed in Experiment 1. EMPAC thus provides a rich, temporally aligned resource for investigating empathy dynamics in naturalistic settings and for evaluating methodological variations in empathic accuracy paradigms.

PurposeWe present a large-scale (N=120) comparative study of gel-based and dry electroencephalography systems for cognitive load analysis in tasks involving information visualization stimuli. Although dry systems are increasingly adopted owing to their portability and fast setup, their sensitivity to cognitive-related measurements (as compared to gel-based systems) remains debated. This limits the understanding of whether dry systems provide sufficient sensitivity for cognitive load assessment under controlled task conditions. MethodsWe analyzed a diverse set of signal quality metrics, such as signal-to-noise ratio and channel retention, combined with spectral features across frequency bands to evaluate the ability for each device to capture workload-related neural markers during information visualization tasks. ResultsAlthough the gel-based device showed consistently better quality results than the dry one, the effect sizes suggest a small practical significance of the differences between systems. These results demonstrate that dry systems can provide adequate physiological sensitivity for cognitive load assessments. ConclusionOur findings highlight the trade-off between usability (setup, calibration, etc.) and data fidelity, providing practical guidance for choosing electroencephalography systems for cognitive workload monitoring and applied neuroengineering research. Overall, the results suggest that dry systems can support coarse-grained cognitive load assessment, while gel-based systems remain advantageous when greater sensitivity is required.

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.

Summary paragraphA hallmark of learning is the need for sensory stimuli (Ginns, 2015; McGraw et al., 2009; Reinwein, 2012; Spence, 1950) so that learning is fundamentally based on sensory input signals affecting behaviour, physiology, and neurology. If behavioural measures of learning can be causally linked to physiological and neurological variables, a broader understanding of the mechanisms related to learning in schools, learning disabilities, and learning and health issues may emerge (McGraw et al., 2009). Despite decades of research on the physiological/neurological variable of sympathetic activation, learning, and achievement (Horvers et al., 2021), any causal relation remains unclear (Cowley et al., 2014; Mason et al., 2020; Pijeira-Diaz et al., 2016; Sung et al., 2023; Yu et al., 2024) and issues with instrument validation remain (Costantini et al., 2023; Hu et al., 2024; Milstein & Gordon, 2020; Van Der Mee et al., 2021). Here we investigate the effect of sensory input on sympathetic activation by using validated instruments for skin conductance measurement (Batista et al., 2019) and whether sympathetic activation is connected to learning in a cognitive laboratory context and an ecologically valid classroom context. In both contexts, we found a physiological variable which correlated with learning and that sensory input affected this variable while student movement did not. These sensory inputs varied depending on the different instructional activities the students participated in. Together, these findings bring us one step closer to a model linking sensory input to behavioural, physiological, and neurological variables.

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.

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.

BioBlitzes are widely used citizen science events that combine biodiversity monitoring, public participation, and environmental awareness through short and intensive observation campaigns. However, applying this model to marine environments presents additional challenges related to safety, access, weather dependency, specialised equipment, species identification, and sustained participation. This paper presents the BioMARathon model as a case study of how BioBlitz-inspired events can be adapted to marine citizen science contexts. The BioMARathon extends the conventional BioBlitz format into a longer, seasonal, and distributed engagement model designed specifically for marine and coastal environments. The paper describes the conceptual foundations of the model in the Janus Engagement Framework, which informed both the design of the BioMARathon and the adaptation of the MINKA citizen science observatory to better support participation, validation, feedback, and continuity over time. BioMARato Catalunya, launched in 2021, is presented as the founding implementation of the model and as the basis for later replication in Portugal. Between 2021 and 2025, BioMARato Catalunya showed continued growth in participation, observations, and taxonomic coverage, while also contributing to the detection of non-indigenous species, first regional records, and climate-related ecological impacts. Beyond biodiversity outcomes, the case suggests that extending participation across a season, distributing activities through local mobilising organisations, and combining expert validation with visible feedback mechanisms can support recurrent participation, retention, and community reactivation in marine citizen science. Rather than offering a formal causal evaluation, this article contributes practical lessons for the design of citizen science initiatives in challenging environments.

Understanding speech in noisy environments (SPIN) is an important everyday ability, and engaging in musical activities has been proposed as a factor that may support this ability. However, the cognitive mechanisms underlying a potential musical advantage in SPIN perception remain unclear. Here we investigated whether musical sophistication is associated with better SPIN perception in a large population-based sample, and whether this relationship is mediated by auditory working memory (AWM), verbal working memory (VWM), or non-verbal intelligence. We recruited 203 participants and measured SPIN perception at both word and sentence levels. Musical sophistication was assessed using the Goldsmiths Musical Sophistication Index (Gold-MSI). AWM was measured using delayed matching of tone frequency or the modulation rate of amplitude modulated white noise, VWM was based on backward digit span task, and non-verbal intelligence used matrix reasoning. Mediation analyses revealed that AWM fully mediated the relationship between musical sophistication and SPIN perception, whereas VWM showed no mediation effect. Non-verbal intelligence showed a partial mediating effect. Additional control analyses using structural equation modelling revealed that the indirect effect through AWM remained significant after accounting for age, hearing thresholds, and non-verbal intelligence. Together, these findings suggest that individuals with greater musical sophistication demonstrate better daily life listening abilities, and that superior auditory working memory may be the key cognitive mechanism underlying this advantage.

In contrast to static formalisms, computational definitions describe the operational mechanisms of a model. Simulations are an essential part of the cycle of theory development and refinement, assisting researchers in formulating the precise definitions that models require, and making accurate predictions. This manuscript introduces a computational implementation of Pavlovian learning models in a Python environment, termed Pavlovian Associative Learning Models Simulation (PALMS). In addition to the canonical Rescorla-Wagner model, attentional approaches are implemented, including Pearce-Kaye-Hall, Mackintosh Extended, Le Pelleys Hybrid, and a novel extension of the Rescorla-Wagner model featuring a unified variable learning rate that synthesises Mackintoshs and Pearce and Halls opposing conceptualisations. To our knowledge, only the first attentional model has been previously specified computationally in a general design tool. PALMS integrates a graphical interface that permits the input of entire experimental designs in an alphanumeric format, akin to that used by experimental neuroscientists. It uniquely enables the simulation of experiments involving hundreds of stimuli, such as those used with human participants, and the computation of configural cues and configural-cue compounds across all models, thereby substantially broadening their predictive capabilities. A comprehensive description of the models implementation and the environment functionalities is provided in the paper; these include efficient and accurate operation and instant visualisation of predicted results across different models within a single architecture and environment. We evaluate PALMS by simulating five published experiments in the associative learning literature that assessed the predictive scope of existing models, and we show that this implementation provides neuroscientists with a useful tool for identifying critical variables, refining experimental designs, making precise predictions, comparing model fitness, and formulating new theoretical approaches. PALMS is licensed under the open-source GNU Lesser General Public License 3.0. The environment source code and the latest multiplatform release build are accessible as a GitHub repository at https://github.com/cal-r/PALMS-Simulator. Author summaryResearch on associative learning is multidisciplinary, encompassing disciplines such as neuroscience, AI, psychology, psychiatry, behavioural sciences, planning, and marketing. Unlike static formalisms, precise computational definitions specify how a model operates, enabling model simulation, swift and error-free prediction calculations, which are essential for testing theories, comparing predictions, holding models accountable, and providing a common language across fields. We introduce Pavlovian Associative Learning Models Simulation (PALMS), a user-friendly, open-source Python environment for simulating classical conditioning and studying the role of attention in learning. PALMS implements the prescriptive Rescorla-Wagner and attentional models: Pearce-Kaye-Hall, Mackintosh Extended, Le Pelleys Hybrid, and a new hybrid model with a unified variable learning rate that blends Mackintosh and Pearce-Halls conflicting views. Its graphical interface makes it easy for neuroscientists to enter experiments. Our computational implementation supports simulations with hundreds of stimuli, configural cues, and compounds, broadening the models predictive power. Designed for efficiency, it offers instant visual results and useful features. We evaluate PALMS by simulating five published experiments, highlighting its value for model comparison and refinement, and, more generally, as a tool to assist research.

Graduate-level genomics courses require students to integrate dense material across subfields, concepts and methods. In modular, multi-instructor courses, students may struggle because the coherence between lectures can be difficult to navigate, while the course structure may be visible to instructors. We evaluated a 2025 navigation redesign of BIO322, a graduate genomics course at the Norwegian University of Life Sciences, while preserving course content, multi-instructor teaching, modular organization and assessment framework. The redesign includes introducing a standardized self-learning guide, expanded syllabus, enriched online quiz feedback, and added support for a final group research proposal. Using anonymized course evaluation scores from 2021-2025 and aggregated learning management system access data from 2023-2025, we examined student experience and resource use. In 2025, five of six course evaluation items reached their highest observed BIO322 scores, while one, lecture-specific score remained within the previous range. The consolidated self-learning guide was accessed by nearly all students, whereas access to optional readings declined across the course sequence, despite comparatively stable page views per accessing student. These course-level findings are consistent with improved perceived navigability following the introduction of standardized learning support. However, some students continued to report difficulty identifying priorities and connections among course components, indicating that challenges in perceived course coherence remained for part of the cohort despite the redesign. Practitioner PointsO_LIMaking course structure explicit may improve students perceived navigability in multi-instructor graduate genomics courses. C_LIO_LIA centralized self-learning guide can broaden access to preparatory guidance without changing core course content or assessment. C_LIO_LIOptional learning supports may be used unevenly, so resource availability should not be assumed to translate into uniform resource access. C_LI

Understanding how nutritional interventions alter food evaluations may help clarify mechanisms of dietary behavior change; however, most studies focus on intake outcomes and rarely assess within-person changes in subjective food evaluation. We developed a brief, image-based rating tool that measures two core dimensions of food evaluation, liking and perceived healthiness, using standardized food images. The tool was piloted in adults with type 2 diabetes participating in a medically supervised intervention that included structured glucose monitoring and professional dietary guidance. Ratings were collected at baseline, post-monitoring, and follow-up. In line with the methodological aim of this study, we examined whether the tool demonstrates internal coherence, sensitivity to change, and external validity against expert ratings and physiological measures, and whether it can capture item-level patterns relevant to eating behavior. Results provide preliminary evidence that the tool is feasible, it is low-burden, and capable of detecting coherent relationships between food liking and health perceptions, including coordinated within-person changes over time and meaningful associations with external benchmarks. To support scalability and self-administration, we also developed an online smartphone-based demonstration version to exemplify the task structure and user experience. Overall, this pilot study suggests that a short, flexible rating task can serve as a practical measurement tool for tracking intervention-relevant changes in food evaluation and for informing the design of future nutritional interventions.

Common terns (Sterna hirundo) are regionally threatened migratory seabirds that form large breeding colonies during the North American summer months. They are highly vocal and serve as important bioindicators of aquatic ecosystems. Historically, acoustic studies on colonial seabirds have proven difficult due to the dense aggregations of individuals and high rate of call overlap. However, as passive acoustic monitoring (PAM) becomes increasingly common for studying seabird colonies, quantitative descriptions of species vocalizations are needed to accurately interpret behavioral information from colony soundscapes and support automated analysis of large acoustic datasets. This study aims to quantify the vocal repertoire of adult common terns. We deployed AudioMoths to collect acoustic data at a tern colony on Seavey Island, New Hampshire, USA from across the breeding season. Using RavenPro, unique call types were identified through visual and aural inspection of the acoustic data in the spectrogram. For each call, we then extracted measurements of peak frequency (Hz), bandwidth 90% (Hz), syllable duration 90% (s), and total bout duration (s) to quantify the characteristics of each call type. Statistical analyses for acoustic parameters by call type were performed using Kruskal-Wallis tests, followed by post-hoc Dunn tests. Our results demonstrate that each call type is significantly different from another by at least one parameter, with the exception of the kek and kip/tjuk calls. These findings present the first quantitative analysis of common tern vocalizations for North America. By defining temporal and spectral characteristics for multiple call types, this work helps translate colony soundscape into biologically meaningful information about tern behavior and colony dynamics. These descriptions also provide key parameters for developing automated tools to detect and classify vocalizations in dense, noisy colonies. Integrating quantified vocal characteristics with PAM offers a promising approach for monitoring colony activity and behavior while minimizing disturbance relative to traditional methods.

Course-based undergraduate research experiences (CUREs) can expand undergraduates access to research and motivate students to stay in science. Yet, little research has examined how CURE instruction shapes student motivation. We leveraged a motivation-related characterization of non-content talk of 48 CURE and non-CURE instructors to predict the motivation-related outcomes of 462 students. We fit a series of multi-level models (MLM) in which we regressed students post-course scientific self-efficacy, task values, scientific identity, and science-related intentions onto instructors self-efficacy and task values-related talk, controlling for students pre-course levels. We also fit an MLM to explore whether instructors relationship-building talk (immediacy talk) was associated with students rapport with their instructor. Instructors self-efficacy talk did not affect students self-efficacy, and instructors immediacy talk had a marginally positive but non-significant association with students rapport ratings. Instructors task values talk positively influenced students scientific identity and some but not all of their task values. Instructors task values talk also positively influenced students intentions to pursue a science career, but not graduate education or research careers. Collectively, these results suggest that instructors task values talk may underpin some of the motivational effects of CURE instruction, but that task values talk need not be limited to CUREs. HIGHLIGHTWe examine whether instructor talk predicts students motivational outcomes in CURE and non-CURE lab courses. Self-efficacy talk had no effect on student self-efficacy. Task values talk positively affected students science identity and career intentions, and some value beliefs. Immediacy talk was marginally related to student-instructor rapport.

Standard rat housing may impede species-typical behaviors and impact rat welfare and research outcomes. This research investigated the effects of housing on behavioral and physiological outcomes of rats through the use of modified large animal cages for housing, and was conducted in two studies. Study A: 70 Sprague Dawley (SD) rats (34 males, 36 females; 5 wk old) were randomly assigned to standard polycarbonate shoebox cages (C: 733.9cm2) or modified stainless steel primate cages (T: 10,416cm2) for 18 days. In Study B: 48 SD rats (24 males, 24 females; 7.5 wk old) were held in T housing for 90 days to assess long term impacts. All rats received gentle handling for 15s 3x/week. Rats were assessed for body weight, anxiety-like behavior in an elevated plus maze, response during a voluntary human approach test, and overall home cage behavior, posture, and space usage. Data were analyzed using generalized linear mixed models, with sex and treatment as fixed effects, and cage as the random effect. The results of study A suggest that the modified large animal cages (T) had positive impacts on rat behavior and welfare. T rats were less anxious (P=0.038) and more active (P<0.0001) and explorative (P=0.0003) compared to C rats. In both groups, activity levels declined towards the end of the 18-day study period (P<0.0001). For study B, similar patterns were observed, with rats becoming more inactive (P<0.0001) over 90 days. However, rats spent significant time on elevated shelves in T housing, which increased throughout the study (P<0.0001), suggesting continued use of the resources the housing provided. In both studies, there were no differences in latency to approach humans (P>0.05), but T rats spent less time in contact with human handlers, suggesting differences in motivation to interact with humans that should be explored further.

Long-term, automated tracking of group-housed social animals using RFID (radio frequency identification) is a promising approach in ethological neuroscience. However, low-frequency (LF) RFID, while long-established in the field, is constrained by its inherent low data rates, which lead to two critical limitations: (1) compromised spatiotemporal resolution, and (2) the inability to identify multiple tags (animals) simultaneously. To address these limitations, we developed eeeHive, a high-frequency (HF) RFID-based animal tracking system with a fully custom hardware architecture that enables high-speed, multiplexed antenna polling and concurrent multi-tag reading. The polling time per antenna in eeeHive was 5.9 ms, with an additional 8.2 ms read time per tag. We applied the system to track 24 mice for one week, and six common marmosets for seven weeks. The system successfully tracked individuals even within dense clusters, revealing complex behavioral traits characterized by spatial utilization, temporal dynamics, behavioral regularity, and inter-individual relationships. Additional tests with Japanese fire-bellied newts and Nile tilapia juveniles demonstrated comparable tracking performance in aquatic environments. Taken together, eeeHive overcomes the inherent limitations of conventional LF RFID, establishing a powerful HF RFID-based platform for fine-scale behavioral tracking of group-housed animals across terrestrial and aquatic species.

ObjectiveSimultaneous recording of brain activity, behaviour, and virtual environments is essential for understanding large-scale neural dynamics during behaviour. However, existing systems often rely on software-based synchronization or post hoc alignment, introducing latency, jitter, and drift that obscure fast brain-behavior interactions. ApproachHere, we present a deterministically synchronized widefield calcium imaging platform that unifies neural imaging, high-speed behavioural monitoring, and closed-loop virtual reality (VR) under a shared hardware-defined clock. This system enables millisecond-precision temporal alignment across modalities, including dual-wavelength hemodynamic correction, pupil and orofacial tracking, locomotion sensing, and VR rendering. Main resultsThe platform achieves stable hardware-level synchronization across neural imaging, behavioural recordings, and VR rendering without reliance on software timestamps. It supports widefield imaging rates up to 100 Hz and integrates seamlessly with both ViRMEn and Blender VR engines, exhibiting a mean locomotion-to-VR update latency of [~]1.5 ms. Multimodal recordings during VR navigation demonstrate robust temporal alignment between cortical activity, facial dynamics, pupil signals, and locomotion. SignificanceThis system provides a deterministic multimodal framework for studying brain-behaviour relationships during active behaviour. By enabling millisecond-precision synchronization across neural imaging, behaviour, and virtual environments, this platform enables causal investigation of brain-behaviour interactions at millisecond precision and provides a foundation for next-generation closed-loop neuroengineering experiments.

2)BackgroundCircadian rhythm desynchrony (CD) occurs when there is a mismatch between the circadian clock and local time, such as shift work. Mouse models are commonly employed to study CD, but may have significant shortcomings such as environmental masking, a focus only on sleep physiology, and significant variability between study designs. ObjectiveThis study used in vivo telemetry for simultaneous, real-time monitoring of locomotor activity (LA), core body temperature (CBT), and brain activity (EEG) in freely moving C57BL/6J mice to assess CD effects. MethodsFour-month-old C57BL/6J mice (n=11) were surgically implanted with telemeters enabling simultaneous real-time recording of LA, CBT, EEG.: Mice were sequentially exposed to a control condition standard 12:12h light-dark cycle (T24) then 4, 8-day CD paradigms: 10:10 h short day (T20), social jet lag (SJL), repeated 6h phase advances (6A2), and a 3:3 h ultradian cycle (T6)For each paradigm, the final 48h of data (250 Hz) were analyzed. ResultsWe found clear differences in the severity of the effects of each CD paradigm on sleep and circadian fitness, where T20[~]T6>SJL>6A2. CBT revealed broader disruption, but EEG outputs proved the most sensitive indicators of internal desynchrony. ConclusionsEach CD paradigm produced a unique profile across behavioral, physiological, and neural domains. We have also identified Gamma CV as a novel, sensitive metric of CD. These results highlight the necessity of multimodal monitoring to accurately characterize the impact of ecologically relevant stressors on circadian and sleep physiology. Statement of SignificanceCircadian rhythm desynchrony (CD), driven by shift work, jet lag, and modern irregular light exposure, is a major health burden linked to metabolic, neurodegenerative, and neuropsychiatric diseases. However, standard methods for measuring CD in laboratory models often rely on simple locomotor activity, which can "mask" the true extent of internal circadian stress. In this study, we simultaneously monitored brain EEG activity, core body temperature, and motion across four distinct models of circadian stress. We discovered that locomotor activity is a deceptive indicator of health; while mice appeared to show no alterations under several stress paradigms, their brain waves and body temperatures revealed the underlying impact of CD. Specifically, we identified "Gamma CV" as a highly sensitive new brain-wave marker that detects early circuit instability even when behavior appears normal and sleep quantity is preserved. These findings provide a marker for identifying early neurological vulnerability to irregular light schedules, offering a potential bridge to understanding similar gamma brain-wave alterations seen in addiction, early-stage Alzheimers disease, and other disorders.