Motion Cues Research Articles

Eye pupil signals life motion perception.

The ability to readily detect and recognize biological motion (BM) is fundamental to survival and interpersonal communication. However, perception of BM is strongly disrupted when it is shown upside down. This well-known inversion effect is proposed to be caused by a life motion detection mechanism highly tuned to gravity-compatible motion cues. In the current study, we assessed the inversion effect in BM perception using a no-report pupillometry. We found that the pupil size was significantly enlarged when observers viewed upright BMs (gravity-compatible) compared with the inverted counterparts (gravity-incompatible). Importantly, such an effect critically depended on the dynamic biological characteristics, and could be extended to local feet motion signals. These findings demonstrate that the eye pupil can signal gravity-dependent life motion perception. More importantly, with the convenience, objectivity, and noninvasiveness of pupillometry, the current study paves the way for the potential application of pupillary responses in detecting the deficiency of life motion perception in individuals with socio-cognitive disorders.

Audiovisual correspondence facilitates the visual search for biological motion

Hearing synchronous sounds may facilitate the visual search for the concurrently changed visual targets. Evidence for this audiovisual attentional facilitation effect mainly comes from studies using artificial stimuli with relatively simple temporal dynamics, indicating a stimulus-driven mechanism whereby synchronous audiovisual cues create a salient object to capture attention. Here, we investigated the crossmodal attentional facilitation effect on biological motion (BM), a natural, biologically significant stimulus with complex and unique dynamic profiles. We found that listening to temporally congruent sounds, compared with incongruent sounds, enhanced the visual search for BM targets. More intriguingly, such a facilitation effect requires the presence of distinctive local motion cues (especially the accelerations in feet movement) independent of the global BM configuration, suggesting a crossmodal mechanism triggered by specific biological features to enhance the salience of BM signals. These findings provide novel insights into how audiovisual integration boosts attention to biologically relevant motion stimuli and extend the function of a proposed life detection system driven by local kinematics of BM to multisensory life motion perception.

Goalkeepers’ plasticity during learning of a whole-body visuomotor rotation in a stable or variable environment

ABSTRACT Postural adjustments performed in anticipation of uncertain visual events is a common sensorimotor control problem in open sport skills. In this study, we examined how expert soccer goalkeepers and non-athletes learn a whole body visuomotor rotation during postural tracking of constant and variable visual target motions. Twenty-one (21) soccer goalkeepers (18 ± 15 years, 75 ± 12 kg) and 25 age-matched non-athletes (18 ± 12 years, 75 ± 15 kg) practiced lateral weight shifting on a dual force platform while tracking the motion of a constant (11 goalkeepers and 12 non-athletes) or a variable (10 goalkeepers and 13 non-athletes) visual target with provision of online visual feedback (VF). After 40s of tracking (baseline), the visual presentation of the VF signal reversed direction relative to the participant’s motion (180° visuo-motor rotation) for 60s (adaptation) and then returned to its veridical direction for another 20s (washout). During adaptation, goalkeepers reduced the spatiotemporal error to baseline levels at an earlier time block (3rd block) compared to non-athletes (6th block), but this difference was significant only for groups tracking of the constant and not the variable target motion. Only the groups tracking the constant target increased the spatiotemporal error during the 1st washout block demonstrating a significant aftereffect. It is concluded that goalkeepers adapt faster to the feedback rotation due to their prior field knowledge of relevant visuomotor transformations in anticipation of deceptive visual cues. This expertise advantage however is present only in a stable visual environment possibly because learning is compromised when tracking uncertain motion cues requiring closed loop control. Highlights We examined how expert goalkeepers and non-athletes adopt to a novel whole body visuomotor rotation when tracking a constantly or variably moving target Goalkeepers adopted faster to the visuomotor rotation than non-athletes Expertise related differences were evident only for groups tracking the constant target motion Groups tracking the variable target motion did not learn the visuomotor rotation

A bio-inspired robot navigation model based on the environmental cognitive mechanism of the rat brain hippocampus

Self-positioning and goal-oriented navigation behaviors in complex and variable environments are important capabilities that mammals rely on for survival in the natural world. To this end, this article proposes a robot navigation model with bio-inspired environmental cognition and autonomous localization capabilities based on the spatial cognitive mechanism of the hippocampus in the rat brain. The model constructs a cognitive map by integrating its own motion cues and visual observation features, and uses the dynamic predictive relationship between each location cognitive node to achieve a goal-oriented navigation process. The research approach in this article will provide inspirational implications for a robot navigation approach with brain-like cognitive mechanisms.

A Lightweight and Detector-Free 3D Single Object Tracker on Point Clouds

Recent works on 3D single object tracking treat the task as a target-specific 3D detection task, where an off-the-shelf 3D detector is commonly employed for the tracking. However, it is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete. In this paper, we address this issue by explicitly leveraging temporal motion cues and propose DMT, a Detector-free Motion-prediction-based 3D Tracking network that completely removes the usage of complicated 3D detectors and is lighter, faster, and more accurate than previous trackers. Specifically, the motion prediction module is first introduced to estimate a potential target center of the current frame in a point-cloud-free manner. Then, an explicit voting module is proposed to directly regress the 3D box from the estimated target center. Extensive experiments on KITTI and NuScenes datasets demonstrate that our DMT can still achieve better performance ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim$</tex-math> </inline-formula> 10% improvement over the NuScenes dataset) and a faster tracking speed (i.e., 72 FPS) than state-of-the-art approaches without applying any complicated 3D detectors. Our code is released at https://github.com/jimmy-dq/DMT.

Taking into account a location of aircraft’s center of mass during motion cuing

Compared to other information sources, motion cues provide a pilot with anticipatory information about spatial position and movement of aircraft. For motion cueing a flight simulator cockpit is installed on a motion system, movement of which motion cueing. Therefore, motion system is one of the most important components of full flight simulators. The problem of effective use of constructive resources of six-degrees of freedom synergistic motion system has been solved. But the problem of improving the motion cueing remained unsolved, due to the fact that location of motion system center of rotation is significantly different from location of aircraf’s center of gravity, and motion cues differ from real, flight one. The study subject is motion cueing on flight simulators. The problem was solved on the basis of simplified operator for transformation of motion system movements along individual degrees of freedom into jack movements, cubic spline functions to describe the dependence of the centers of rotation along pitch and yaw, and optimization theory using the deformable polyhedron method. The formulated and solved problem of taking into account of location of aircraft’s center of gravity during motion cueing along pitch and yaw increases an efficiency of using of constructive resource of a six-degrees of freedom synergistic motion system, a motion cueing fidelity and training realism on flight simulator.

Inducing Self-Motion Sensations With Haptic Feedback: State-of-The-Art and Perspectives on "Haptic Motion".

While virtual reality applications flourish, there is a growing need for technological solutions to induce compelling self-motion, as an alternative to cumbersome motion platforms. Haptic devices target the sense of touch, yet more and more researchers managed to address the sense of motion by means of specific and localized haptic stimulations. This innovative approach constitutes a specific paradigm that can be called "haptic motion". This article aims to introduce, formalize, survey and discuss this relatively new research field. First, we summarize some core concepts of self-motion perception, and propose a definition of the haptic motion approach based on three criteria. Then, we present a summary of existing related literature, from which we formulate and discuss three research problems that we estimate key for the development of the field: the rationale to design a proper haptic stimulus, the methods to evaluate and characterize self-motion sensations, and the usage of multimodal motion cues.

Enhancing motion visual cues for self-supervised video representation learning

Building the general feature from unlabeled videos is the core of self-supervised video representation learning. However, recent research on video representation focuses on static visual pixel information, which makes these models unable to capture the dynamics in the temporal dimension. In order to solve the aforementioned issue and improve the generalization of the model, we propose an enhanced motion visual cue (EMVC) method for self-supervised video representation learning to reduce the background bias and increase the motion information. Our EMVC includes a background replacing module and a foreground fixing module that leverages the foreground and background of the original video sequence to make the background of same-action videos less identical and their motion cues more distinct. Experimental results show that the proposed method effectively reduces the biases in the background and significantly improves the video’s ability to comprehend motion information, leading to an increase in recognition accuracy. Specifically, the EMVC method achieved an accuracy of 84.6%, 53.1%, and 68.3% on the UCF101, HMDB51, and Diving48 datasets, respectively, outperforming the existing algorithms. Additionally, significant improvements were obtained in the video retrieval task on benchmark datasets, with an average improvement of over 14.6%.

Vestibular and visual brain areas in the medial cortex of the human brain.

Self-motion perception involves an interaction between vestibular and visual brain regions. In the lateral brain, it includes the parietoinsular vestibular cortex and the posterior insular cortex. In the medial cortex, the cingulate sulcus visual (CSv) area is known to process visual-vestibular cues. Here, we show that the vestibular-visual network of the medial cortex extends beyond area CSv. We examined brain activations of 36 healthy right-handed participants by functional magnetic resonance imaging (fMRI) during stimulation with caloric vestibular, thermal, or visual motion cues. Consistent with previous research, we found that area CSv responded to both vestibular and visual cues but not to thermal cues. Moreover, the V6 complex and the precuneus motion (PcM) area responded primarily to (laminar-translational) visual motion cues. However, we also observed a region inferior to CSv within the pericallosal sulcus (vicinity of anterior retrosplenial) that primarily responded to vestibular cues. This vestibular pericallosal sulcus (vPCS) region did not respond to either visual or thermal cues. It was also distinct from a more posterior motion-sensitive region in the retrosplenial complex (mRSC) that responded to (radial) visual motion but not to vestibular and thermal cues. Together, our results suggest that the vestibular-visual network in the medial cortex not only includes areas CSv, PcM, and the V6 complex but also two additional brain regions adjacent to the callosum. These two brain regions exhibit similarities in terms of their locations and responses to vestibular and visual cues with self-motion-related brain regions recently described in nonhuman primates.NEW & NOTEWORTHY Self-motion perception involves several vestibular and visual cortical regions. Within the medial cortex, the cingulate sulcus visual (CSv) area, the precuneus motion (PcM) area, and the V6 complex respond selectively to self-motion cues. Here, we show that vestibular information is also processed in the pericallosal sulcus (vPCS), whereas (radial) visual motion information is associated with activation in the retrosplenial cortex (mRSC).

Gaze patterns reflect and predict expertise in dynamic echocardiographic imaging.

Echocardiography is the most important modality in cardiac imaging. Rapid valid visual assessment is a critical skill for image interpretation. However, it is unclear how skilled viewers assess echocardiographic images. Therefore, guidance and implicit advice are needed for learners to achieve valid image interpretation. Using a signal detection approach, we compared 15 certified experts with 15 medical students in their diagnostic decision-making and viewing behavior. To quantify attention allocation, we recorded eye movements while viewing dynamic echocardiographic imaging loops of patients with reduced ejection fraction and healthy controls. Participants evaluated left ventricular ejection fraction and image quality (as diagnostic and visual control tasks, respectively). Experts were much better at discriminating between patients and healthy controls ( of 2.58, versus 0.98 for novices). Eye tracking revealed that experts fixated diagnostically relevant areas earlier and more often, whereas novices were distracted by visually salient task-irrelevant stimuli. We show that expertise status can be almost perfectly classified either based on judgments or purely on eye movements and that an expertise score derived from viewing behavior predicts diagnostic quality. Judgments and eye tracking revealed significant differences between echocardiography experts and novices that can be used to derive numerical expertise scores. Experts have implicitly learned to ignore the salient motion cue presented by the mitral valve and to focus on the diagnostically more relevant left ventricle. These findings have implications for echocardiography training, objective characterization of echocardiographic expertise, and the design of user-friendly interfaces for echocardiography.

Naïve chicks do not prefer objects with stable body orientation, though they may prefer behavioural variability

Domestic chicks (Gallus gallus domesticus) have been widely used as a model to study the motion cues that allow visually naïve organisms to detect animate agents shortly after hatching/birth. Our previous work has shown that chicks prefer to approach agents whose main body axis and motion direction are aligned (a feature typical of creatures whose motion is constrained by a bilaterally symmetric body plan). However, it has never been investigated whether chicks are also sensitive to the fact that an agent maintains a stable front–back body orientation in motion (i.e. consistency in which end is leading and which trailing). This is another feature typical of bilateria, which is also associated with the detection of animate agents in humans. The aim of the present study was to fill this gap. Contrary to our initial expectations, after testing 300 chicks across 3 experimental conditions, we found a recurrent preference for the agent which did not maintain a stable front–back body orientation. Since this preference was limited to female chicks, the results are discussed also in relation to sex differences in the social behaviour of this model. Overall, we show for the first time that chicks can discriminate agents based on the stability of their front–back orientation. The unexpected direction of the effect could reflect a preference for agents’ whose behaviour is less predictable. Chicks may prefer agents with greater behavioural variability, a trait which has been associated with animate agents, or have a tendency to explore agents performing “odd behaviours”.

Motion Cueing Algorithm for Flight Simulator: Interfacing and Pilot Controls Implementation

Flight simulation nowadays is essential to the training of pilots in both civiland military fields. Generally, the use of parallel motion mechanisms has becomethe standard for human-in-the-loop simulation. However, the realization of a flightsimulator with reasonable fidelity is only achievable using a well-tuned motioncueing algorithm that conveys accurate, real-time cues to the user whilemaintaining safe use of the available platform workspace. In this paper, thenecessary setup and interfacing is done with the X-Plane simulation environmentfor the purposes of testing and tuning the parameters of a developed motion cueingalgorithm. In addition, the design and implementation of prototype pilot controls,namely the yoke and rudder pedals are also undertaken to fulfill interfacingobjectives and are integrated into a fixed-seat simulator platform to conduct a fullpilot-in-the-loop simulation. MATLAB/Simulink is then used for interfacinghardware pilot controls and the simulation environment as well as the real-timetuning of motion cueing parameters.

A Progressive Difference Method for Capturing Visual Tempos on Action Recognition

Visual tempos show the dynamics of action instances, characterizing the diversity of the actions, such as walking slowly and running quickly. To facilitate action recognition, it is essential to capture visual tempos. To this end, previous methods sample raw videos at multiple frame rates or integrate multi-scale temporal features. These methods inevitably introduce two-stream networks or feature-level pyramid structures, leading to expensive computation. In this work, we propose a progressive difference method to capture visual tempos for efficient action recognition, by computing coarse-to-fine motion information within a small neighborhood around temporal frames. Specifically, the uniform sampling method is first applied to each video, and then first-order temporal differences around each frame are calculated to describe local motions. On the basis of differences, further computing the variations of differences, namely second-order differences, can gradually capture fine-grained spatiotemporal features and characterize the areas where the motion cues are more prominent. On one hand, multi-order motion differences can be combined with raw input to describe the diversity of the actions. On the other hand, the variations of first-order differences information can be used to activate first-order salient motion regions, thereby facilitating the discrimination of finer-grained actions. Our method can be combined with existing backbones in a plug-and-play manner. Extensive experiments are conducted on several video benchmarks, including Kinetics400, HMDB51, UCF101, UAV-Human, Something-Something V1 and V2. We also give detailed analysis and qualitative experiments to demonstrate the effectiveness of our method.

Examination of audiovisual prosody in cochlear implant recipients

Prosody plays a vital role in verbal communication. It is important for the expression of emotions but also carries information on sentence stress or the distinction between questions and statements. Cochlear Implant (CI) recipients are restricted in the use of acoustic prosody cues, especially in terms of the voice fundamental frequency. However, prosody is also perceived visually, as head and facial movements accompany the vocal expression. To date, few studies have addressed multimodal prosody perception in CI users. Controlled manipulations of acoustic cues are a valuable method to uncover and quantify prosody perception. For visual prosody, however, such a technique is more complicated. We describe a novel approach based on animations via virtual humans. Such a method has the advantage that–in parallel to acoustic manipulations–head and facial movements can be parametrized. It is shown that animations based on a virtual human generally provide similar motion cues as video recordings of a real talker. Parametrization yields fine-grained manipulation of visual prosody, which can be combined with modifications of acoustic features. This allows generating both congruent and incongruent stimuli with different salience. Initial results of using this method with CI recipients are presented and discussed.

Re-detection and distractor association from a global perspective: A long-term tracking system

Great success has been achieved in short-term visual object tracking in recent years. However, long-term tracking which is closer to practical applications still lacks enough attention, especially for the target disappearances and reappearances which are common phenomena and important properties in long-term tracking due to full occlusion and out-of-view. In this paper, we propose a long-term tracking framework based on the cooperation of a short-term local tracker and the high-quality global re-detection and distractor association mechanisms. The former mechanism provides target candidates for target relocation, and the latter utilizes the Kalman Filter and Hungarian algorithm with motion cues to build distractor tracklets which attempts to avoid incorrect target relocation after the target disappears. We apply the tracking framework on the state-of-the-art algorithm STARK and evaluate the performance on the popular long-term benchmarks VOTLT-2020 and TLP. The experimental results show the effectiveness of the proposed framework and our tracker performs well compared with other competing algorithms.

Mice and primates use distinct strategies for visual segmentation.

The rodent visual system has attracted great interest in recent years due to its experimental tractability, but the fundamental mechanisms used by the mouse to represent the visual world remain unclear. In the primate, researchers have argued from both behavioral and neural evidence that a key step in visual representation is 'figure-ground segmentation', the delineation of figures as distinct from backgrounds. To determine if mice also show behavioral and neural signatures of figure-ground segmentation, we trained mice on a figure-ground segmentation task where figures were defined by gratings and naturalistic textures moving counterphase to the background. Unlike primates, mice were severely limited in their ability to segment figure from ground using the opponent motion cue, with segmentation behavior strongly dependent on the specific carrier pattern. Remarkably, when mice were forced to localize naturalistic patterns defined by opponent motion, they adopted a strategy of brute force memorization of texture patterns. In contrast, primates, including humans, macaques, and mouse lemurs, could readily segment figures independent of carrier pattern using the opponent motion cue. Consistent with mouse behavior, neural responses to the same stimuli recorded in mouse visual areas V1, RL, and LM also did not support texture-invariant segmentation of figures using opponent motion. Modeling revealed that the texture dependence of both the mouse's behavior and neural responses could be explained by a feedforward neural network lacking explicit segmentation capabilities. These findings reveal a fundamental limitation in the ability of mice to segment visual objects compared to primates.

ORT: Occlusion-robust for multi-object tracking

Although the joint-detection-and-tracking paradigm has promoted the development of multi-object tracking (MOT) significantly, the long-term occlusion problem is still unsolved. After a period of trajectory inactivation due to occlusion, it is difficult to achieve trajectory reconnection with appearance features because they are no longer reliable. Although using motion cues does not suffer from occlusion, the commonly used Kalman Filter is also ineffective in its long-term inertia prediction in cases of no observation updates or wrong updates. Besides, occlusion is prone to cause multiple track-detection pairs to have close similarity scores during the data association phase. The direct use of the Hungarian algorithm to give the global optimal solution may generate the identity switching problem. In this paper, we propose the Long-term Spatio-Temporal Prediction (LSTP) module and the Ordered Association (OA) module to alleviate the occlusion problem in terms of motion prediction and data association, respectively. The LSTP module estimates the states of all tracked objects over time using a combination of spatial and temporal Transformers. The spatial Transformer models crowd interaction and learns the influence of neighbors, while the temporal Transformer models the temporal continuity of historical trajectories. Besides, the LSTP module also predicts the visibilities of the motion prediction boxes, which denote the occlusion attributes of trajectories. Based on the occlusion attribute and active state, the association priority is defined in the OA module to associate trajectories in order, which helps to alleviate the identity switching problem. Comprehensive experiments on the MOT17 and MOT20 benchmarks indicate the superiority of the proposed MOT framework, namely Occlusion-Robust Tracker (ORT). Without using any appearance information, our ORT can achieve competitive performance beyond other state-of-the-art trackers in terms of trajectory accuracy and purity.

DeepStep: Direct Detection of Walking Pedestrian From Motion by a Vehicle Camera

Pedestrian detection has wide applications in intelligent transportation. It is essential to understand pedestrian&#x2019;s position and action instantaneously for autonomous driving. Most algorithms divide these tasks into sequential procedures where pedestrians are detected from shape-based features in video frames, and their behaviors are analyzed with frame tracking. Different from those, this work introduces a deep learning-based pedestrian detection method that only uses motion cues. The pedestrian motion, which is much different from that of static background and dynamic vehicles, is investigated in the spatial-temporal domain. The pedestrian leg movement forms a chain-type trace in the motion profile images even if the ego-vehicle moves. Instead of modeling walking actions based on kinematics, the chain structure is directly learned from a large pedestrian dataset in driving videos. This method works for the more challenging scenes observed on moving vehicles than those scenes from static cameras. The aim is to detect not only pedestrians promptly but also predict their walking direction in the driving space. Since a video is reduced to temporal images, real-time performance is achieved with a high mean average precision and a low false-positive rate on a publicly available dataset.

Effects of dance on gait and dual-task gait in Parkinson's disease.

Gait impairments in Parkinson's disease (PD) limit independence and quality of life. While dance-based interventions could improve gait, further studies are needed to determine if the benefits generalise to different terrains and when dual-tasking. The aim was to assess the effects of a dance intervention, based on the Dance for PD® (DfPD®) program, on gait under different dual-tasks (verbal fluency, serial subtraction) and surfaces (even, uneven), and to determine if a larger scale follow-up RCT is warranted. A dance group (DG; n = 17; age = 65.8 ± 11.7 years) and a control group (CG: n = 16; age = 67.0 ± 7.7 years) comprised of non-cognitively impaired (Addenbrooke's score: DG = 93.2 ± 3.6, CG = 92.6 ± 4.3) independently locomoting people with PD (Hoehn & Yahr I-III). The DG undertook a one-hour DfPD®-based class, twice weekly for 12 weeks. The CG had treatment as usual. The spatiotemporal variables of gait were assessed at baseline and post-intervention while walking on two surfaces (even, uneven) under three conditions: regular walking; dual-task: verbal-fluency (DTVERB), and serial-subtraction (DTSUBT). The data were analysed by means of a linear mixed model. At baseline, there was no significant group difference for any spatiotemporal gait variable. The DG improved significantly compared to the CG with and without a dual task when walking on even surface. During regular walking, DG improved in gait velocity (p = 0.017), cadence (p = 0.039), step length (p = 0.040) and stride length (p = 0.041). During DTVERB significant improvements were noted in gait velocity (p = 0.035), cadence (p = 0.034) and step length (p = 0.039). The DG also exhibited significant improvement compared to the CG during DTSUBT in the measures of gait velocity (p = 0.012), cadence (p = 0.021), step length (p = 0.018), and stride length (p = 0.151). On the uneven surface, improvements were noted when walking while performing serial subtractions only. During regular walking, improvements were noted for the CG but not for the DG. CG has spent less time in double support following the intervention than DG. While DTVERB condition had no significant group differences for any gait parameter (p's >0.05), in the DTSUBT condition, the DG improved significantly compared to the controls on gait velocity (p = 0.048), cadence (p = 0.026), and step length (p = 0.051). DfPD®-based classes produced clinically significant improvement in spatiotemporal gait parameters under dual-task conditions and on uneven surfaces. This could arise from improved movement confidence and coordination; emotional expression; cognitive skills (planning, multitasking), and; utilisation of external movement cues. A large-scale RCT of this program is warranted. A protocol for this study has been registered retrospectively at the Australian New Zealand Clinical Trials Registry. Identifier: ACTRN12618001834246.

Effect of Motion Cues on Highway Driving Performance in Simulated Driving

The motion system is a major component of a driving simulator. It provides motion cues in various directions with different degrees of freedom (DOF). Different DOF could influence response variables widely used in highway design safety evaluation conducted in simulators. Few studies, though, have comprehensively explored the effect of driving simulator motion cues on driving performance. The high-fidelity Tongji University Driving Simulator (TUDS) was used to separately compare five dependent variables: speed, speed change, lane deviation, longitudinal acceleration, and lateral acceleration. The Wilcoxon signed-rank test was used to examine the difference for each variable using 8-DOF and fixed-base motion system configurations for nine types of combined highway alignments. A subjective assessment of participants’ driving experience was conducted, and the chi-square test for each term was used to compare the difference in motion system fidelity and adverse reactions between the two configurations. Results show that: (1) speed and lateral acceleration are more likely to be significantly affected by motion cues than speed change, lane deviation, and longitudinal acceleration; (2) the horizontal curve with up-grade is the type of combined alignment where speed and lateral acceleration are mostly affected; (3) characteristics of the five variables in different combined alignments vary between the two simulators; (4) the 8-DOF motion system provides better perceived motion fidelity than the fixed-base system does, and the 8-DOF simulator causes less discomfort and tiredness in simulator sickness; and (5) the presence of motion cues is suggested for highways with complex combined curves when focusing on speed and lateral acceleration.