Behavior Research Methods

BackgroundLong-term home-cage monitoring is essential to quantify spontaneous locomotor and social behaviors in group-housed mice, but analysis of high-density RFID tracking data remains a barrier to reproducibility. New methodsWe developed IntelliProfiler 2.0, a fully R-based pipeline tailored to the eeeHive 2D floor-mounted RFID array. The workflow performs data import from text logs, preprocessing, coordinate reconstruction, missing-value handling, feature extraction, statistical testing, and visualization in a single environment. Behavioral metrics include travel distance, close contact ratio (CCR), and a newly implemented inter-individual distance metric. ResultsIn four-day recordings of group-housed C57BL/6J mice (8 males and 8 females), IntelliProfiler 2.0 captured circadian phase-dependent locomotion and proximity patterns and reproduced sex-dependent differences consistent with prior analyses while incorporating updated hardware specifications. Radar-chart summaries enabled intuitive comparison of multidimensional behavioral profiles and inter-individual variability across light/dark phases. Comparison with existing methodsCompared with IntelliProfiler 1.0 and multi-tool workflows, IntelliProfiler 2.0 consolidates analysis into a single, script-based R pipeline, reducing operational complexity and improving reproducibility. The updated implementation supports recent manufacturer-driven changes, including antenna renumbering and multi-USB data export. ConclusionsIntelliProfiler 2.0 provides a reproducible, extensible framework for high-throughput behavioral phenotyping of group-housed mice and is scalable across hardware configurations, including simplified single-board recordings. HighlightsO_LIEnd-to-end R pipeline for eeeHive 2D floor-based RFID tracking analysis C_LIO_LIStandardized setup with comprehensive manuals and protocols C_LIO_LIInter-individual distance metric to quantify group spatial structure C_LIO_LICircadian- and sex-dependent behavioral profiling in group-housed mice C_LIO_LIRadar-charts summarize multidimensional behavioral profiles and variability C_LI

In our study, we used virtual reality to investigate how the colour of an objects surroundings influences colour constancy. Using Unreal Engine, we manipulated lighting and object properties in computer-generated scenes illuminated by five different light sources and presented them through an HTC Vive Pro Eye virtual reality headset. Participants assessed colour constancy by selecting the object that best matched a neutral reference from among five differently coloured options within the scene. Our results demonstrated a significant decline in colour constancy performance when the illuminant colour was in the opposite direction to that of the local surround, highlighting the interactive effects of surround colour and illumination.

The light/dark box test can be used to assess visual function in rodents based on their spontaneous behavior in response to light. Commonly used assay relies on a single behavioral metric, dwell time in the light or dark compartment, which may be influenced by factors other than vision, leading to unreliable assessment results. To overcome this, we developed a multi-feature machine learning paradigm by extracting multiple mouse behavioral metrics, standardizing them as features to train machine learning models, thereby achieving reliable and automated vision assessment. We systematically compared the classification performance of single-metric versus multi-feature machine learning approaches in sighted and blind mice, using wild-type and rhodopsin-deficient mice, with a subset further subjected to double optic nerve crush. We found that the multi-feature method can improve classification performance and exhibit great robustness to different experimental settings. Additionally, we further improved model performance by applying feature importance analysis and constructing an optimized feature subset. These findings suggest that the reliability of commonly used single dwell time measure for vision assessment could become unreliable, as shown in our experiment, probably because in addition to vision other factors also impact dwell time. Our study demonstrated an improved assessment method based on a combination of multiple behavior features through machine learning. Author SummaryAssessing visual function in mice is essential for studying eye diseases and drug development. The light/dark box test evaluates visual function by measuring the spontaneous behavioral response of mice to light, providing a training-free behavioral approach that helps simplify the assessment process and improve research efficiency. However, traditional light/dark box tests rely on a single behavioral metric, dwell time in the light or dark compartment, to assess visual function, which may be influenced by factors other than vision, such as anxiety and exploratory behavior, leading to limited reliability of assessment results. Here, we demonstrate that integrating multiple behavioral features through machine learning can improve the reliability and stability of vision assessment. By automatically tracking and analyzing various behavioral metrics of mice, such as movement patterns, speed, and spatial preferences, the proposed method can more reliably distinguish between sighted and blind mice. Furthermore, the method demonstrates stable performance across different experimental settings, showing good applicability. This automated, reliable, and easily generalizable method can provide a convenient and efficient means for visual assessment in preclinical research, facilitating vision disease research and drug development.

Precise measurement of color discrimination across color space is limited by the time and effort required to collect large psychophysical datasets. We investigated whether immersive virtual reality (VR) can support high-throughput measurement of color discrimination without compromising data quality. A standard 4-alternative forced-choice odd-one-out task was embedded in an interactive VR environment inspired by the rhythm game Beat Saber, in which participants indicated the location of a chromatic target by slicing approaching cubes. Stimuli were presented on a color-calibrated VR headset, and chromaticities were specified in DKL space. Discrimination thresholds were measured for hue and chroma shifts around two reference colors. Participants sustained response rates of approximately one trial per second while maintaining stable performance. Thresholds replicated established asymmetries in color discrimination: hue thresholds were lower than chroma thresholds, and the hue-chroma ratio differed between color quadrants. A control experiment comparing VR-based slicing responses with matched keyboard responses revealed comparable psychometric fits and threshold estimates, indicating that motor engagement did not degrade measurement precision. Questionnaire measures further showed significantly higher intrinsic motivation, enjoyment, and stimulation in the immersive condition relative to a classic static psychophysical task, without increases in reported pressure or discomfort. These results demonstrate that calibrated immersive VR can yield reliable color discrimination measurements at substantially increased throughput, providing a scalable approach for mapping the metric structure of color space.

Michottes "launching displays" are animations of collision-like interactions between two objects that elicit a stable and robust impression that one object, the launcher, caused another object, the target, to move. Although it is well-known that unexpected disruptions of movement continuation between launcher and target decrease causal impressions in centre-to-centre collisions, the role of observers visual uncertainty around predicted moving trajectories remains relatively unexplored. In this work, we (1) assess observers uncertainty around post-collision moving angles in a trajectory prediction task and (2) collect their causal impression in a causality rating task. In the latter task, observers viewed centre-to-centre collisions with different levels of movement continuity between the launcher and the target disc. By presenting different launch orientations, we exploited the well-known oblique effect to vary trajectory prediction uncertainty within individuals. If observers rely on their trajectory predictions to rate the causality of the collision, we expect their accuracy in (1) to have a systematic influence on their causality rating in (2). We replicate previous findings that observers report stronger causal impressions in trials where the target and the launcher move in the same direction and weaker causal impressions for collisions where the target and the launcher moving trajectory deviated. Furthermore, causality ratings were on average higher for oblique compared to cardinal launch directions, implying that increased sensory uncertainty induces a stronger causal impression. We hope this work will inspire deeper empirical assessments and computational models describing the role of sensory uncertainty and predictive processes in shaping subjective impressions of causality.

Dance is a core form of human-environment interaction and a powerful medium for emotional expression, yet dancers are routinely exposed to environmental affective cues that may shape their movement. We tested whether a negative emotional context induced immediately before improvisation alters dance biomechanics. Twenty professional dancers performed two 3-min improvised dances. Between dances, they viewed either Neutral or Negatively valenced pictures from the International Affective Picture System (IAPS; 2 min 40 s, 5 s per image). Eye tracking verified attention to the visual stream. Mood was assessed at four time points (PT1-PT4) using the Brazilian Mood Scale (BRAMS), and full-body, three-dimensional kinematics were captured at 300 Hz using a 9-camera optoelectronic system (Qualisys) and processed to measure global movement amplitude and expansion. Negative IAPS exposure increased tension, depression, fatigue, and decreased vigor from PT2 to PT3. Biomechanically, the Negative Stimulus dancers showed a significant reduction in global movement amplitude after negative IAPS exposure, with reduced movement amplitude of the body extremities. In contrast, global movement expansion remained unchanged; that is, the extremities were not positioned closer or farther from the pelvis. Neutral images produced no mood change and no measurable modulation of movement amplitude or expansion. Together, these results support the hypothesis that improvised dance carries biomechanical signatures of the dancers current affective state, beyond the intended expressive content, and provide an automated motion-capture workflow for studying emotion-movement coupling in spontaneous dance. HighlightsNegative visual context shifted dancers mood toward negative affect Negative images reduced movement amplitude in improvised dance Movement expansion remained stable despite mood induction Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/711707v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@aeaacdorg.highwire.dtl.DTLVardef@14f9bf5org.highwire.dtl.DTLVardef@18805fcorg.highwire.dtl.DTLVardef@1411256_HPS_FORMAT_FIGEXP M_FIG C_FIG

When individuals view the same visual input, they often differ in their aesthetic appeal judgments, yet why people differ remains largely unclear. Here, we tested whether individual differences in aesthetic experience are linked to differences in visual exploration. In two experiments, participants watched the documentary "Home" while their eye movements were recorded. In Experiment 1, participants continuously rated aesthetic experience throughout the movie, whereas in Experiment 2, they watched the first half without a task and rated aesthetic experience only during the second half. Inter-individual similarity in gaze patterns, assessed using fixation heatmaps across time, predicted similarity in aesthetic appeal judgments in both experiments. Notably, in Experiment 2, gaze similarity during free viewing in the first half of the movie predicted similarity in aesthetic ratings during the second half, indicating that incidental eye movement patterns predict aesthetic experiences. Together, these results show that shared gaze patterns are linked to shared aesthetic experiences under naturalistic, dynamic viewing conditions.

Computational bioacoustics has seen significant advances in recent decades. However, the rate of insights from automated analysis of bioacoustic audio lags behind our rate of collecting the data - due to key capacity constraints in data annotation and bioacoustic algorithm development. Gaps in analysis methodology persist: not because they are intractable, but because of resource limitations in the bioacoustics community. To bridge these gaps, we advocate the open science method of data challenges, structured as public contests. We conducted a bioacoustics data challenge named BioDCASE, within the format of an existing event (DCASE). In this work we report on the procedures needed to select and then conduct useful bioacoustics data challenges. We consider aspects of task design such as dataset curation, annotation, and evaluation metrics. We report the three tasks included in BioDCASE 2025 and the resulting progress made. Based on this we make recommendations for open community initiatives in computational bioacoustics.

Quantitative phenotyping is essential to studies of animal behavior, enabling systematic analysis of variation arising from natural diversity or experimental manipulation. High-throughput behavioral assays that can simultaneously test multiple animals support sufficiently powered studies of behavioral variation, but accurate tracking of each animal is critical. Furthermore, behavioral tasks and experimental arenas span a wide range of complexity, from the reaction of a single larval zebrafish to an acoustic stimulus to associative conditioning in cue-rich environments. Here, we developed and validated StrIPETrack (Structural similarity-based Image Processing for Estimation and Tracking), a Python-based, modular animal tracking software designed for flexible region-of-interest (ROI) definitions and extensibility across assays. We show that StrIPETrack measures activity comparably to our previous LabVIEW-based zebrafish tracking software and detects similar behavioral differences between wild-type clutches. In addition, StrIPETrack accurately captures behavior in a complex arena: the Y-maze. Our approach for analyzing Y-maze navigation yields an expanded set of metrics beyond turn count and direction, revealing more subtle behavioral variation. Overall, this versatile software can be applied to monitor the activity of multiple animals in parallel in both simple high-throughput and more complex assays, and can be readily adapted to new paradigms. SummaryOur open-source tracking software provides rich behavioral phenotyping of animals in many behavioral tasks. The flexible ROI design and live tracking makes the software adaptable to diverse paradigms.

Animal welfare is a central aspect in animal-based research where mice are most commonly used. Their facial expression can be analyzed to assess their well-being status using the Mouse Grimace Scale. However, its manual application becomes increasingly impractical when used on a large number of animals. This lead to the ongoing integration of computer vision methods to automate the analysis. While such methods have proven effective qualitatively, a systematic assessment to verify their reliability largely remains an open research gap. In this work, we attempted to close this gap as we evaluated three dominant paradigms (i.e., classification from supervised learning features, self-supervised learning features, or landmark locations) for the binary (i.e., well-being un-/impaired) classification of facial mouse images. Our quantitative results showed that such methods can be employed successfully with as low as 16% type II error rates. For qualitative assessment, we visualized the decision-making process and demonstrated that mainly pixels associated with the mouse rather than its environment are used. We further discovered that visual characteristics of the mice beyond those described by the Mouse Grimace Scale contributed to the classification. Our work showed that the automated well-being status assessment in mice is trustworthy and urges towards widespread adoption.

Neuropsychiatric (depression, schizophrenia, etc.) and neurological disorders (Alzheimers disease, AD, Parkinsons disease) are characterized by disruptions in cognition including social interaction and recognition. Developing tools for the assessment of social behaviour in mouse models and its relevance is essential to further advance our understanding of social impairments in these diseases. In the Agora maze for rodents, stranger mice confined into cubicles around the perimeter of the open square mirror the agora (marketplace) in ancient cities. Up to 5 social interaction partners are presented and can be freely selected for interaction (exposure). In the discrimination phase one novel mouse (SNew) is presented while 4 familiar partners remain. Interaction time is recorded via video observation. In Exp 1, we validated the test with different strains of wild-type male mice (C57BL/6J, Balb/c, NMRI) that were able to readily identify SNew and spent significantly more time in zones adjacent to their cubicle; only NMRI mice did not prefer SNew. Exp. 2 explored 5xFAD Alzheimer mice and showed normal exploration and discrimination when aged 6 and 8 months old. Repeat of the experiment in a second cohort confirmed robustness of this phenotype, but also reproducibility of the behavioural paradigm. The Agora task allows semi-automated evaluation of preference for social novelty in a more complex paradigm by expanding the number of social interaction partners from 2 (three-chamber test) to 5 (or more), while still avoiding physical approaches and aggressive episodes. Thus, Agora provides a more physiological behavioural paradigm which is highly robust and reproducible. HighlightsO_LIMore comprehensive behavioural test bed for social recognition C_LIO_LIMale wild-type mice can identify a stranger mouse amongst 5 social interaction partners C_LIO_LINo deficit in amyloid-based Alzheimer model 5xFAD aged 6-8 months. C_LIO_LIRepeat of experiments returned highly robust and reproducible results. C_LI

Lucid dreaming (LD), during which the dreamer is aware that they are dreaming, is frequently induced in laboratory settings by delivering sensory cues during rapid eye movement (REM) sleep. These cues should be incorporated into ongoing dreams and can trigger reflective awareness. This approach relies on the continuity between waking experiences and dream content. In sleep laboratories, participants often dream of the experimental setting itself (lab dreaming), providing a predictable context in which lucidity may emerge. The present studies leveraged this phenomenon by explicitly training participants to associate the sleep laboratory with reflective awareness prior to sleep. Across three studies (total N = 101), participants completed a morning nap following verbal LD instructions and presleep audio designed to prime recognition of the laboratory context in dreams. In addition, conditions included immersive virtual reality (VR) rehearsal of the laboratory environment, VR combined with haptic stimulation (HS) during REM sleep, or VR containing subtle fake system errors intended to prompt reflective checking. LD frequency was assessed through external ratings of signal-verified LD (SVLD) dream reports. Lucidity rates were high across all conditions, with approximately 40-45% of dreams externally rated as lucid and 11%-32% SVLDs occurring in every group. However, neither VR rehearsal, haptic stimulation, nor implicit VR errors increased lucidity relative to the baseline laboratory induction procedure. Exploratory analyses investigated the overlap between laboratory dreaming, false awakenings (FAs), and lucidity. These findings suggest that explicit training focused on the predictable context of the sleep laboratory may already provide a powerful pathway to lucidity, with additional technological manipulations offering limited benefit under a single-nap protocol. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=140 SRC="FIGDIR/small/711049v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@191373corg.highwire.dtl.DTLVardef@c1490corg.highwire.dtl.DTLVardef@1a2c193org.highwire.dtl.DTLVardef@52c5d1_HPS_FORMAT_FIGEXP M_FIG C_FIG

Virtual Reality (VR) applications depend on eliciting spatial presence, the subjective experience of being physically located within a virtual environment. Although individual differences have long been theorised to contribute to this experience, their role in highly immersive VR systems remains contested. The present study investigated whether trait absorption predicts spatial presence and whether this relationship is mediated by attention allocation. Seventy participants (44 female, 26 male; M age = 22.90, SD = 4.88) completed a 6-minute VR session using a Meta Quest 3 Head-Mounted Display and validated self-report measures of trait absorption (Tellegen Absorption Scale), attention allocation, and spatial presence (MEC-Spatial Presence Questionnaire). Path analysis confirmed a significant, complete mediation pathway: trait absorption positively predicted attention allocation ({beta} = 0.27, p = .013), which in turn strongly predicted spatial presence ({beta} = 0.54, p < .001). The direct path from absorption to spatial presence was non-significant ({beta} = 0.11, p = .325), indicating complete mediation. The indirect effect was significant ({beta} = 0.15; 95% BCa CI [0.025, 0.291]). The model explained a sizeable 33.8% of the variance in spatial presence (Cohens f{superscript 2} = 0.51). Post-hoc dose-response analysis revealed that trait absorption acts as a cognitive amplifier: the strength of the attention-presence relationship tripled from low-absorption ({beta} = 0.33, R{superscript 2} = .15) to high-absorption individuals ({beta} = 1.00, R{superscript 2} = .56). These findings demonstrate that individual differences remain important in highly immersive VR by modulating the effectiveness of attentional focus, offering promising directions for tailoring VR interventions.

IntroductionImproving the efficiency and accuracy of annotation and extraction of performance data from mouse behavioural tasks will improve both the throughput and scientific value of preclinical research. MethodsHere, we present and validate an automated pipeline for the annotation and quantification of performance in a mouse olfactory habituation-dishabituation task, using a single side-view camera, resulting in occluded body parts. We created a pipeline for task analysis, combining DeepLabCut, for pose-estimation, and SimBA, for behavioural classification to automatically quantify odour interaction (sniffing time) in a three-odour (water, familiar mouse social odour, novel mouse social odour) variant of the task. We used a subset of previously published, fully manually annotated datasets to train the models and unseen videos from the same study to validate the utility of our machine learning pipeline. Results and conclusionOur analysis pipeline estimated behavioural performance in the task with high accuracy, and the data produces similar technical and biological results to manual methods when analysed by linear mixed modelling. Thus, we validated the utility of our new pipeline for the automated scoring of this mouse sensory task.

Toddlerhood is a critical period in the development of facial expression processing. Prior research suggests that in the natural environment, the frequency of faces in the toddlers egocentric view declines relative to infancy. However, the specific statistics of the emotional facial expressions available to the developing toddler remain unknown. This study implemented a dual-perspective set-up to record the egocentric view of toddlers and their caregivers during everyday situations at home (N = 26 families). Using automated computer vision models, we quantified both the frequency of faces and the emotional expressions displayed. Confirming our hypotheses, faces were sparse in toddler views and significantly less frequent than in caregiver views. Across both perspectives, happiness was the dominant expression, while negative facial expressions were extremely rare. Notably, faces expressing surprise were frequent in toddler view, whereas caregivers encountered significantly more happy and sad facial displays than their children. This is the first ecological study to objectively quantify the occurrence of emotional facial expressions in the home environment. These findings challenge the assumption of an abundance of emotional signals in the early development. Instead, they demonstrate that toddlers develop face representations based on sparse input that is biased towards positive expressions (e.g., happy), suggesting high efficiency in extracting and generalizing information from limited input.

Home cage monitoring (HCM) captures longitudinal animal behavioural data without human intervention. However, the systems complexity is rarely addressed in their design, increasing the risk of data loss, which wastes workhours, resources, and animal lives. To assess the feasibility of implementing modern, robust architectures in complex operant HCM paradigms, the VersatiLe Autonomous DevIce for Scheduled Learning Assessment Via Wi-Fi (VLADISLAV) was developed and employed to test cognitive deficits in the intracerebroventricular streptozotocin-induced rat model of sporadic Alzheimers disease (sAD). Reliability was modelled against a system architecture common in commercial HCM systems by modelling the failure rate of the devices critical components across typical durations of animal experiments. VLADISLAV assessed multiple cognitive dimensions of a rat model of sAD with automated, scheduled testing. Its design enabled simultaneous, redundant recording to multiple devices in real time, as well as batch remote control and supervision of tens of VLADISLAVs. VLADISLAV is estimated to reduce component failure rate [~]200-fold at {euro}40/device. Data loss due to system failure shouldnt be accepted as a normal occurrence and robust system design is an ethical imperative. VLADISLAVs robustness and utility demonstrate the potential of embedded networked systems, used in other industries and consumer electronics for over a decade. Today, the open source ecosystem enables cost-effective implementation of such architectures in HCM by biomedical researchers with no electronic engineering education, preventing data loss and facilitating researchers and technicians day-to-day work. Considering these findings, it is apparent that the implementation of modern architectures in HCM is long overdue.

Biodesign is an interdisciplinary research domain that incorporates principles from design and the life sciences to develop new systems, processes, and objects. Collegiate biodesign educators face unique pedagogical challenges, including an absence of relevant scholarship on curriculum design and instructional best practices for cultivating student scientific literacy. These difficulties may be overcome with newly available technologies, like generative AI systems, that enable personalized learning through domain-specific semantic spaces. This article examines the instructional value of one such domain-specific LLM, Biodesign Buddy, through a mixed-methods analysis of an eight-week study involving 64 students participating in an international biodesign competition. Results indicate strong support for integrating AI into biodesign coursework. Surveys captured attitudes toward AI, scientific literature, and learning experiences to assess AIs impact on learning outcomes. Findings suggest that integrating AI into biodesign pedagogy can meaningfully redress conceptual issues in biodesign while informing broader debates on AIs role in higher education. Impact StatementThis article introduces Biodesign Buddy, a domain-specific generative AI system for collegiate biodesign education, and reports on its exploratory deployment, offering design principles and preliminary findings to inform the development of AI-supported pedagogies for interdisciplinary biodesign instruction.

Structured AbstractO_ST_ABSObjectiveC_ST_ABSTo facilitate the scalability of EEG research, this paper compares the data quality and evaluates the absolute agreement of EEG features between laboratory and clinic settings. MethodsResting state EEG recordings were obtained from 36 participants (11 infants, 10 children, and 15 adults) from the waiting room of a primary care clinic and a laboratory. Intraclass correlation coefficients (ICC(2,1)) quantified the absolute agreement between laboratory and clinic settings for periodic power bands, alpha peak characteristics, and aperiodic components. The mean absolute difference (MAD) between laboratory and clinic recorded EEGs were calculated to describe signal consistency across settings. ResultsMore components were rejected from clinic-recorded EEGs, though data quality otherwise did not differ between settings. The ICC (2,1) for all EEG measures were generally in the good-to-excellent range across ages and regions of interest. The MAD decreased with age and was largest in the alpha frequency range. ConclusionsHigh quality EEG data can be collected from outpatient clinic settings among infants, children, and adults. There is high reliability in the parameterized periodic and aperiodic EEG features between laboratory and clinic settings. SignificanceFuture research may collect EEG datasets from naturalistic settings with confidence in their reliability relative to laboratory recordings.

Biomedical research relies on scientifically validated tools to assess pain, suffering, and distress in laboratory animals to ensure their well-being. In mice, the most frequently used laboratory animals, the Mouse Grimace Scale (MGS) provides a reliable tool for the assessment of facial expression changes caused by impaired well-being. However, no automated tool can yet reliably assess all features of the MGS across different mouse strains under varying experimental or housing conditions in real-time, as the variability present in recorded image datasets poses substantial challenges for computer vision models. Despite this technical difficulty, variability across subsets in terms of mouse strain, treatments, laboratory, and image acquisition setup is essential for paving the way toward MGS assessment under non-standardized conditions in the home cage rather than standardized cage-side recording setups. Against this background, a large and diverse dataset containing five subsets is introduced and a deep learning model was trained to predict the average MGS scores ranging between 0 and 2. It achieved a root mean squared error (RMSE) of 0.26 when trained on all subsets of the dataset, outperforming the average human rater in terms of error magnitude. The correlation between human raters and automated MGS scores was very high (Pearsons r=0.85). In the cross-dataset evaluation, one subset was excluded from training and used for testing the model. This approach yielded higher errors compared to models trained and tested on the same subsets. A model restricted to the feature of orbital tightening showed lower performance than one trained on all facial features of the MGS. Overall, the most reliable model for predicting average MGS scores for a novel dataset is the one trained on the combined subsets. Performance may be further enhanced by fine-tuning the model using human-generated MGS scores for a portion of the novel subset.

How does the structure of events influence the when and the where of experience in comparison to the what? We developed a novel virtual reality (VR) environment to understand how the quantity of information within nested structures influence participants memory for events. Participants moved through a series of virtual rooms (events) where images (items) appeared in randomised locations on a 3 by 3 grid located on a wall. Participants were asked to remember the what (old/new), when (timeline location), and where (grid location), of the images they experienced. Two types of nested events were tested (6 rooms, each containing 4 images; 3 rooms, each containing 8 images) without a difference in the number of seconds of presentation. We found a strong temporal compression effect at nested levels in which participants remembered early items and events happening later, and later items and events happening earlier, than the original experience. Crucially, presenting four-item events resulted in a greater compression rate than eight-item events. We also found greater temporal distances between pairs of items occurring within eight-item events than pairs of items which occurred on either side of a boundary. Memory for when depends on the compression of information within events.