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The assessment of smoothness, range of motion (ROM), and head repositioning accuracy (HRA) has gained attention in identifying sensorimotor impairments. Uncertainty persists on the approach for acquiring reliable measures, including choice of smoothness metric, normalization factors, and the required number of measurements for reliable results. This study aimed to address this uncertainty. Thirty healthy participants were included in this single-session randomized cross-over study. The experiment consisted of two parts. One focused on the test–retest assessment of head ROM into right rotation to the end of range from a neutral position using a self-selected movement speed and the HRA when returning to the start-position. In the other part, participants repeated the previous tasks and performed head rotations at slower and faster speeds than their self-selected pace and to the beat of a metronome. All tasks were repeated ten times. For the test–retest, the inter-class-correlation (ICC) values for ROM were between 0.84–0.91, 0.20–0.31 for HRA, and 0.65–0.90 for jerk for 1–10 repetitions. Normalizing jerk through vmean and vpeak had similar variability and appeared equally valid for our data. However, normalizing by vmax ensures desirable properties in the smoothness metric. Lower variability was observed when standardizing movements using a metronome. Based on test–retest findings, three repetitions are recommended, as ICC values show marginal improvement beyond 2–3 repetitions, providing limited additional value.

How well can modern wearable eye trackers cope with head and body movement? To investigate this question, we asked four participants to stand still, walk, skip, and jump while fixating a static physical target in space. We did this for six different eye trackers. All the eye trackers were capable of recording gaze during the most dynamic episodes (skipping and jumping). The accuracy became worse as movement got wilder. During skipping and jumping, the biggest error was 5.8 ∘ . However, most errors were smaller than 3 ∘ . We discuss the implications of decreased accuracy in the context of different research scenarios.

Animals move their head and eyes as they explore the visual scene. Neural correlates of these movements have been found in rodent primary visual cortex (V1), but their sources and computational roles are unclear. We addressed this by combining head and eye movement measurements with neural recordings in freely moving mice. V1 neurons responded primarily to gaze shifts, where head movements are accompanied by saccadic eye movements, rather than to head movements where compensatory eye movements stabilize gaze. A variety of activity patterns followed gaze shifts and together these formed a temporal sequence that was absent in darkness. Gaze-shift responses resembled those evoked by sequentially flashed stimuli, suggesting a large component corresponds to onset of new visual input. Notably, neurons responded in a sequence that matches their spatial frequency bias, consistent with coarse-to-fine processing. Recordings in freely gazing marmosets revealed a similar sequence following saccades, also aligned to spatial frequency preference. Our results demonstrate that active vision in both mice and marmosets consists of a dynamic temporal sequence of neural activity associated with visual sampling. Parker et al. recorded neural activity in V1 of freely moving mice and freely gazing marmosets. In both species, neurons respond to gaze shifts in a temporal sequence, such that new visual input is processed in a ‘coarse’ to ‘fine’ manner.

Adolescent changes in human brain function are not entirely understood. Here, we used multiecho functional MRI (fMRI) to measure developmental change in functional connectivity (FC) of resting-state oscillations between pairs of 330 cortical regions and 16 subcortical regions in 298 healthy adolescents scanned 520 times. Participants were aged 14 to 26 y and were scanned on 1 to 3 occasions at least 6 mo apart. We found 2 distinct modes of age-related change in FC: “conservative” and “disruptive.” Conservative development was characteristic of primary cortex, which was strongly connected at 14 y and became even more connected in the period from 14 to 26 y. Disruptive development was characteristic of association cortex and subcortical regions, where connectivity was remodeled: connections that were weak at 14 y became stronger during adolescence, and connections that were strong at 14 y became weaker. These modes of development were quantified using the maturational index (MI), estimated as Spearman’s correlation between edgewise baseline FC (at 14 y, FC14) and adolescent change in FC (ΔFC14−26), at each region. Disruptive systems (with negative MI) were activated by social cognition and autobiographical memory tasks in prior fMRI data and significantly colocated with prior maps of aerobic glycolysis (AG), AG-related gene expression, postnatal cortical surface expansion, and adolescent shrinkage of cortical thickness. The presence of these 2 modes of development was robust to numerous sensitivity analyses. We conclude that human brain organization is disrupted during adolescence by remodeling of FC between association cortical and subcortical areas.

Visual search is a ubiquitous task; people search for objects on a daily basis. However, the majority of the existing visual search literature focuses on passive search on a 2D computer screen, a far cry from emulating a real-world environment. Search is a real-world task that involves active observation. Search targets may be occluded, completely out of the observer’s line of sight, or oriented in unconventional ways. This is typically mitigated by actively selecting viewpoints, an important aspect of search behaviour with limited scope on a computer screen. Our goal was to explore viewpoint selection in active visual search. Subject eye and head movements were tracked as they moved freely while searching for toy objects in a controlled 3-dimensional environment, yielding the first such record of search-driven viewpoint selection. We found that subjects utilized their full range of eye and head motion to move from viewpoint to viewpoint, apparently employing a variety of objectives including changing viewing height and pose depending on object 3D pose. Subjects were also adept at selecting unobstructed views to search through otherwise occluded areas with objects. Furthermore, subjects completed the search task with high accuracy, even with no training on the environment. Although no learning was found in terms of accuracy over the duration of the experiment, increases in efficiency were found for other metrics such as response time, number of fixations, and distance travelled, particularly in target present trials where the target was not visible from the starting location. These results paint the story of a visual system that selects and moves to useful and informative views to facilitate the successful execution of an active visual search task, and stresses the significance of active vision research in understanding how vision is used in naturalistic environments.

Blind football players use head movements to accurately identify sound location when trapping a ball. Accurate sound localization is likely important for motor learning of ball trapping in blind football. However, whether head movements affect the acquisition of ball-trapping skills remains unclear. Therefore, this study examined the effect of head movements on skill acquisition during ball trapping. Overall, 20 sighted male college students were recruited and assigned to one of the following two groups: the conventional training group, where they were instructed to move leftward and rightward to align their body with the ball’s trajectory, and the head-movement-focused group, where they were instructed to follow the ball with their faces until the ball touched their feet, in addition to the conventional training instructions. Both groups underwent a 2-day training for ball trapping according to the specific instructions. The head-movement-focused group showed a decrease in errors in ball trapping at near distances and with larger downward head rotations in the sagittal plane compared to the conventional training group, indicating that during the skill acquisition training for ball trapping, the sound source can be localized more accurately using larger head rotations toward the ball. These results may help beginner-level players acquire better precision in their movements while playing blind football.

Virtual reality (VR) technology has gained popularity across various fields; however, its use often induces cybersickness, characterized by symptoms such as dizziness, nausea, and eye strain. This study investigated the differences in cybersickness levels and head movement patterns under three distinct VR viewing conditions: dynamic VR (DVR), static VR (SVR), and a control condition (CON) using a simulator. Thirty healthy adults participated, and their head movements were recorded using the Meta Quest 2 VR headset and analyzed using Python. The Virtual Reality Sickness Questionnaire (VRSQ) assessed subjective cybersickness levels. The results revealed that the SVR condition induced the highest VRSQ scores (M = 58.057), indicating the most severe cybersickness symptoms, while the DVR condition elicited significantly higher values in head movement variables, particularly in the coefficient of variation (CV) and integral values of head position along the vertical axis, and mean velocity (p < 0.05). These findings suggest that VR content characteristics directly influence users’ head movement patterns, closely related to cybersickness occurrence and severity. This study highlights the importance of analyzing head movement patterns in cybersickness research and provides insights for VR content design.

Moving through a dynamic world, humans need to intermittently stabilize gaze targets on their retina to process visual information. Overt attention being thus split into discrete intervals, the automatic detection of such fixation events is paramount to downstream analysis in many eye-tracking studies. Standard algorithms tackle this challenge in the limiting case of little to no head motion. In this static scenario, which is approximately realized for most remote eye-tracking systems, it amounts to detecting periods of relative eye stillness. In contrast, head-mounted eye trackers allow for experiments with subjects moving naturally in everyday environments. Detecting fixations in these dynamic scenarios is more challenging, since gaze-stabilizing eye movements need to be reliably distinguished from non-fixational gaze shifts. Here, we propose several strategies for enhancing existing algorithms developed for fixation detection in the static case to allow for robust fixation detection in dynamic real-world scenarios recorded with head-mounted eye trackers. Specifically, we consider (i) an optic-flow-based compensation stage explicitly accounting for stabilizing eye movements during head motion, (ii) an adaptive adjustment of algorithm sensitivity according to head-motion intensity, and (iii) a coherent tuning of all algorithm parameters. Introducing a new hand-labeled dataset, recorded with the Pupil Invisible glasses by Pupil Labs, we investigate their individual contributions. The dataset comprises both static and dynamic scenarios and is made publicly available. We show that a combination of all proposed strategies improves standard thresholding algorithms and outperforms previous approaches to fixation detection in head-mounted eye tracking.

There are several factors for vehicle accidents during driving such as drivers’ negligence, drowsiness, and fatigue. These accidents can be avoided, if drivers are warned in time. Moreover, recent developments in computer vision and artificial intelligence (AI) have helped to monitor drivers and alert them in case they are not concentrating on driving. The AI techniques can extract relevant features from expressions of driver’s face, such as eye closure, yawning, and head movements to infer the level of sleepiness. In addition, they can acquire biological signals from the driver’s body, and indications from the vehicle behavior. This paper provides a comprehensive review of the detection techniques of drowsiness and fatigue of drivers using machine learning (ML) and deep learning (DL). The current techniques for this application are classified into four categories: image- or video-based analysis during the driving, biological signal analysis for drivers, vehicle movement analysis, and hybrid techniques. A review of supervised techniques is presented for detecting fatigue and drowsiness on different datasets, with a comparison of the various techniques in terms of pros and cons. Results are presented in terms of accuracy of detection for each technique. The results are discussed according to the recent problems and challenges in this field. The paper also highlights the applicability and reliability of the different techniques. Furthermore, some suggestions are presented for the future work in the field of driver drowsiness detection (DDD).

Vision enables many animals to perform spatial reasoning from remote locations 1 . By viewing distant landmarks, animals recall spatial memories and plan future trajectories. Although these spatial functions depend on hippocampal place cells 2 , 3 , the relationship between place cells and active visual behaviour is unknown. Here we studied a highly visual animal, the chickadee, in a behaviour that required alternating between remote visual search and spatial navigation. We leveraged the head-directed nature of avian vision to track gaze in freely moving animals. We discovered a profound link between place coding and gaze. Place cells activated not only when the chickadee was in a specific location, but also when it simply gazed at that location from a distance. Gaze coding was precisely timed by fast ballistic head movements called ‘head saccades’ 4 , 5 . On each saccadic cycle, the hippocampus switched between encoding a prediction of what the bird was about to see and a reaction to what it actually saw. The temporal structure of these responses was coordinated by subclasses of interneurons that fired at different phases of the saccade. We suggest that place and gaze coding are components of a unified process by which the hippocampus represents the location that is relevant to the animal in each moment. This process allows the hippocampus to implement both local and remote spatial functions. Place cells in the chickadee hippocampus coherently represent locations in space, whether physically visited or viewed, enabling spatial reasoning at a distance through a unified process coordinated by head saccades.