Dynamic Stimuli Research Articles

Space-time interference: The asymmetry we get out is the asymmetry we put in.

Temporal judgments are more affected by space than vice versa. This asymmetry has often been interpreted as primacy of spatial representations over temporal ones. This interpretation is in line with conceptual metaphor theory that humans conceptualize time by spatial metaphors, but is inconsistent with the assumption of a common neuronal magnitude system. Here we review the accumulating evidence for a genuinely symmetric interference between time and space and discuss potential explanations as to why asymmetric interference can arise, both with respect to the interaction between spatial size and temporal duration, and the interaction between traveled distance and travel time. Contrary to the view of hierarchical representations of time and space, our review suggests that asymmetric interference can be explained on the basis of working memory processes and the aspect of speed inherent in dynamic stimuli. We conclude that the asymmetry we often get out (space affects time more than vice versa) is a consequence of the asymmetry we put in (by using biased paradigms and stimuli facilitating spatial processing).

Going through the motions: Biasing of dynamic attentional templates.

Attention must coordinate with memory to actively anticipate sensory input and guide action. Memory content may be biased away from veridical when it is functionally adaptive. So far, research has considered the biasing of still features in static displays. It is unknown whether the biasing of attentional templates can functionally adapt dynamic stimuli to facilitate search when targets and distractors compete within temporally extended contexts. Biasing of dynamic templates would require learning and modulatory mechanisms capable of abstracting over space and time to guide perception. Four experiments used a novel dynamic visual search task combined with a memory probe to test whether dynamic attentional templates can be biased. In Experiments 1-3, participants searched for a moving target among distractors that systematically moved either clockwise or counterclockwise relative to the target. On memory probe trials, participants recalled the target direction as biased away from the distractors. The distortion bias was adaptively changed (Experiment 2), grew over time (Experiment 2), and occurred even when motion direction was not the target-defining feature (Experiment 3). Experiment 4 manipulated the speed of targets and distractors to test the generalizability of the findings. Participants searched for a target of a given speed among faster or slower distractors. Memory probing revealed that participants remembered the target speed as biased away from that of distractors. Across different tasks, the magnitude of the biasing correlated positively with search performance. Our findings provide compelling evidence that dynamic stimulus attributes in attentional templates can become functionally biased when adaptive. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Bioelastic state recovery for haptic sensory substitution.

The rich set of mechanoreceptors found in human skin1,2 offers a versatile engineering interface for transmitting information and eliciting perceptions3,4, potentially serving a broad range of applications in patient care5 and other important industries6,7. Targeted multisensory engagement of these afferent units, however, faces persistent challenges, especially for wearable, programmable systems that need to operate adaptively across the body8-11. Here we present a miniaturized electromechanical structure that, when combined with skin as an elastic, energy-storing element, supports bistable, self-sensing modes of deformation. Targeting specific classes of mechanoreceptors as the basis for distinct, programmed sensory responses, this haptic unit can deliver both dynamic and static stimuli, directed as either normal or shear forces. Systematic experimental and theoretical studies establish foundational principles and practical criteria for low-energy operation across natural anatomical variations in the mechanical properties of human skin. A wireless, skin-conformable haptic interface, integrating an array of these bistable transducers, serves as a high-density channel capable of rendering input from smartphone-based 3D scanning and inertial sensors. Demonstrations of this system include sensory substitution designed to improve the quality of life for patients with visual and proprioceptive impairments.

Frequency and amplitude dependence of nuclear displacement and phase delay in mechanical vibrations for determining cellular natural frequency

Cultured cells biochemically respond to mechanical vibrations. However, the mechanisms of sensing mechanical vibrations and transducing biochemical responses remain unclear. A previous study reported that the expression of the alkaline phosphatase gene of osteoblastic cell under mechanical vibrations peaks at 50 Hz, which seems like a resonance curve in the mechanical vibration theory. Since forced displacement excitation is a dynamic mechanical stimulus that differs from other static mechanical stimuli in that an external force is equivalent to inertia, force is apparently exerted on the mass element by considering the equation of motion. In this study, the method for obtaining the change of a nucleus’s relative displacement to an excited dish was refined, and the frequency and acceleration amplitude dependence of the nucleus’s relative displacement and phase delay under mechanical vibrations were demonstrated by regarding a cell model as a vibration system. The change of the relative displacement of a HeLa nucleus to an excited dish decreases with increasing frequency in the 12.5–100 Hz range at 0.5 G and increases with increasing acceleration amplitude in the 0.5–2.0 G range at 50 Hz. Phase reversal occurs between 12.5 Hz and 50 Hz, which suggests the existence of the natural frequency of the cell between 12.5 Hz and 50 Hz. The tension estimated from the nucleus’s relative displacement change in this method was 2.3–10 pN and can be a biochemical response of the mechanotransducer. These findings can contribute to clarifying the mechanism of cell mechanotransduction in dynamic mechanical stimuli.

Combining visual cues with relaxing music, perfume and ventilation to reduce passenger car sickness: An experimental study

The autonomous, digital, and electric transformation of passenger vehicles is projected to elevate the incidence of motion sickness among occupants. This study investigates the effectiveness of an “anti-motion sickness” system that features dynamic light stimuli to support visual perception of longitudinal motion, combined with relaxing music, perfume and ventilation pulses to enhance user wellbeing. In an experimental design aimed at replicating a realistic passenger drive, fifty-three subjects were tested as front passengers engaged in a gaming activity. Subjective motion sickness ratings were collected throughout the drive, and the development of symptoms was monitored through in-ear physiological measurements, including RR-intervals, core temperature, and oxygen saturation. Post hoc analyses indicated non-significant improvement in subjective assessments of motion sickness. Similarly, physiological measurements did not demonstrate any significant difference across conditions. The tested system was however more effective in mitigating car sickness for low susceptible than for mid-to-high susceptible individuals.

The Mechanisms and Neural Signature of Time-averaged Numerosity Perception.

The animal brain is endowed with an innate sense of number allowing to intuitively perceive the approximate quantity of items in a scene, or "numerosity." This ability is not limited to items distributed in space, but also to events unfolding in time and to the average numerosity of dynamic scenes. How the brain computes and represents the average numerosity over time, however, remains unclear. Here, we investigate the mechanisms and EEG signature of the perception of average numerosity over time. To do so, we used stimuli composed of a variable number (3-12) of briefly presented dot arrays (50 msec each) and asked participants to judge the average numerosity of the sequence. We first show that the weight of different portions of the stimuli in determining the judgment depends on how many arrays are included in the sequence itself: the longer the sequence, the lower the weight of the latest arrays. Second, we show systematic adaptation effects across stimuli in consecutive trials. Importantly, the EEG results highlight two processing stages whereby the amplitude of occipital ERPs reflects the adaptation effect (∼300 msec after stimulus onset) and the accuracy and precision of average numerosity judgments (∼450-700 msec). These two stages are consistent with processes involved with the representation of perceived average numerosity and with perceptual decision-making, respectively. Overall, our findings provide new evidence showing how the visual system computes the average numerosity of dynamic visual stimuli, and support the existence of a dedicated, relatively low-level perceptual mechanism mediating this process.

Compressive stresses in cancer: characterization and implications for tumour progression and treatment.

Beyond their many well-established biological aberrations, solid tumours create an abnormal physical microenvironment that fuels cancer progression and confers treatment resistance. Mechanical forces impact tumours across a range of biological sizes and timescales, from rapid events at the molecular level involved in their sensing and transmission, to slower and larger-scale events, including clonal selection, epigenetic changes, cell invasion, metastasis and immune response. Owing to challenges with studying these dynamic stimuli in biological systems, the mechanistic understanding of the effects and pathways triggered by abnormally elevated mechanical forces remains elusive, despite clear correlations with cancer pathophysiology, aggressiveness and therapeutic resistance. In this Review, we examine the emerging and diverse roles of physical forces in solid tumours and provide a comprehensive framework for understanding solid stress mechanobiology. We first review the physiological importance of mechanical forces, especially compressive stresses, and discuss their defining characteristics, biological context and relative magnitudes. We then explain how abnormal compressive stresses emerge in tumours and describe the experimental challenges in investigating these mechanically induced processes. Finally, we discuss the clinical translation of mechanotherapeuticsthat alleviate solid stresses and their potential to synergize with chemotherapy, radiotherapy and immunotherapies.

Neural Encoding of Bodies for Primate Social Perception.

Primates, as social beings, have evolved complex brain mechanisms to navigate intricate social environments. This review explores the neural bases of body perception in both human and nonhuman primates, emphasizing the processing of social signals conveyed by body postures, movements, and interactions. Early studies identified selective neural responses to body stimuli in macaques, particularly within and ventral to the superior temporal sulcus (STS). These regions, known as body patches, represent visual features that are present in bodies but do not appear to be semantic body detectors. They provide information about posture and viewpoint of the body. Recent research using dynamic stimuli has expanded the understanding of the body-selective network, highlighting its complexity and the interplay between static and dynamic processing. In humans, body-selective areas such as the extrastriate body area (EBA) and fusiform body area (FBA) have been implicated in the perception of bodies and their interactions. Moreover, studies on social interactions reveal that regions in the human STS are also tuned to the perception of dyadic interactions, suggesting a specialized social lateral pathway. Computational work developed models of body recognition and social interaction, providing insights into the underlying neural mechanisms. Despite advances, significant gaps remain in understanding the neural mechanisms of body perception and social interaction. Overall, this review underscores the importance of integrating findings across species to comprehensively understand the neural foundations of body perception and the interaction between computational modeling and neural recording.

Computationally derived endosteal strain and strain gradients correlate with increased bone formation in an axially loaded murine tibia model

Osteoporosis is a common metabolic bone disorder characterized by low bone mass and microstructural degradation of bone tissue due to a derailed bone remodeling process. A deeper understanding of the mechanobiological phenomena that modulate the bone remodeling response to mechanical loading in a healthy bone is crucial to develop treatments. Rodent models have provided invaluable insight into the mechanobiological mechanisms regulating bone adaptation in response to dynamic mechanic stimuli. This study sheds light on these aspects by means of assessing the mechanical environment of the cortical and cancellous tissue to in vivo dynamic compressive loading within the mouse tibia using microCT-based finite element model in combination with diaphyseal strain gauge measures. Additionally, this work describes the relation between the mid-diaphyseal strains and strain gradients from the finite element analysis and bone formation measures from time-lapse in vivo tibial loading with a fluorochrome-derived histomorphometry analysis. The mouse tibial loading model demonstrated that cancellous strains were lower than those in the midshaft cortical bone. Sensitivity analyses demonstrated that the material property of cortical bone was the most significant model parameter. The computationally-modeled strains and strain gradients correlated significantly to the histologically-measured bone formation thickness at the mid-diaphyseal cross-section of the mouse tibia.

Age-related differences in detecting facial emotion changes: unveiling the impact of dynamic stimuli on sensitivity

Age-related differences in detecting facial emotion changes: unveiling the impact of dynamic stimuli on sensitivity

Neural correlates of dynamic lightness induction.

The lightness of a surface depends not only on the amount of light reflected off, it but also on the context in which it is embedded. Despite a long history of research, neural correlates of context-dependent lightness perception remain a topic of ongoing debate. Here, we seek to expand on the existing literature by measuring functional magnetic resonance imaging (fMRI) responses to lightness variations induced by the context. During the fMRI experiment, we presented 10 participants with a dynamic stimulus in which either the luminance of a disk or its surround is modulated at four different frequencies ranging from 1 to 8Hz. Behaviorally, when the surround luminance is modulated at low frequencies, participants perceive an illusory change in the lightness of the disk (lightness induction). In contrast, they perceive little or no induction at higher frequencies. Using this frequency dependence and controlling for long-range responses to border contrast and luminance changes, we found that activity in the primary visual cortex (V1) correlates with lightness induction, providing further evidence for the involvement of V1 in the processing of context-dependent lightness.

(Invited) Triboelectric Nanogenerators for Self-Powered Sensing and Human-Machine-Interfaces

Wearable and portable electronics are garnering substantial interest for applications ranging from personalized healthcare monitoring to implantable devices and prosthetics. The demand for long-term operation poses significant challenges to their power supply. To address this, the development of novel self-powered systems holds considerable promise. In this talk, I will discuss our recent advancements in crafting triboelectric nanogenerators (TENGs) for self-powered sensing and human-machine interfaces (HMIs). I will first introduce our multifunctional tactile sensors inspired by human skin. These sensors combine a triboelectric nanogenerator layer with a piezoresistive sensing layer, emulating the ability of human skin to detect a variety of static and dynamic stimuli. This sensor can successfully recognize voice patterns, monitor physiological signals, and track human motion in real-time. Then, I will unveil our self-powered smart packaging system, which is driven by a novel desiccant-based triboelectric nanogenerator (D-TENG). This system offers a viable method for continuous monitoring of product conditions and quality control throughout the supply chain, thus minimizing food waste and maintaining a comprehensive log of logistics, especially critical in vaccine transport. Lastly, I will present our novel breath-driven triboelectric sensor in respiratory monitoring. This sensor can precisely detect breath variations and convert different breathing patterns into electrical signals, all without impeding normal breathing. It enables severely disabled individuals to control household appliances through a breath-driven HMI and provides an intelligent monitoring system for emergency situations. This innovative work paves the way for future development in self-powered sensors and advanced HMIs, enhancing assistive technology for individuals with disabilities.

The dissociating effects of fear and disgust on multisensory integration in autism: evidence from evoked potentials.

Deficits in Multisensory Integration (MSI) in ASD have been reported repeatedly and have been suggested to be caused by altered long-range connectivity. Here we investigate behavioral and ERP correlates of MSI in ASD using ecologically valid videos of emotional expressions. In the present study, we set out to investigate the electrophysiological correlates of audiovisual MSI in young autistic and neurotypical adolescents. We employed dynamic stimuli of high ecological validity (500 ms clips produced by actors) that depicted fear or disgust in unimodal (visual and auditory), and bimodal (audiovisual) conditions. We report robust MSI effects at both the behavioral and electrophysiological levels and pronounced differences between autistic and neurotypical participants. Specifically, neurotypical controls showed robust behavioral MSI for both emotions as seen through a significant speed-up of bimodal response time (RT), confirmed by Miller's Race Model Inequality (RMI), with greater MSI effects for fear than disgust. Adolescents with ASD, by contrast, showed behavioral MSI only for fear. At the electrophysiological level, the bimodal condition as compared to the unimodal conditions reduced the amplitudes of the visual P100 and auditory P200 and increased the amplitude of the visual N170 regardless of group. Furthermore, a cluster-based analysis across all electrodes revealed that adolescents with ASD showed an overall delayed and spatially constrained MSI effect compared to controls. Given that the variables we measured reflect attention, our findings suggest that MSI can be modulated by the differential effects on attention that fear and disgust produce. We also argue that the MSI deficits seen in autistic individuals can be compensated for at later processing stages by (a) the attention-orienting effects of fear, at the behavioral level, and (b) at the electrophysiological level via increased attentional effort.

Decoding dynamic visual scenes across the brain hierarchy.

Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a crucial investigation in neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding-Neuropixels dataset and utilize the capabilities of deep learning neural network models to study neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. Our study reveals that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within the visual cortex and subcortical nuclei, in contrast to a relatively reduced encoding activity within hippocampal neurons. Strikingly, our results unveil a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings corroborate existing knowledge in visual coding related to artificial visual stimuli and illuminate the functional role of these deeper brain regions using dynamic stimuli. Consequently, our results suggest a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding quality of dynamic natural visual scenes represented by neural responses, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.

A three-dimensional valve-on-chip microphysiological system implicates cell cycle progression, cholesterol metabolism and protein homeostasis in early calcific aortic valve disease progression

BackgroundCalcific aortic valve disease (CAVD) is one of the most common forms of valvulopathy, with a 50 % elevated risk of a fatal cardiovascular event, and greater than 15,000 annual deaths in North America alone. The treatment standard is valve replacement as early diagnostic, mitigation, and drug strategies remain underdeveloped. The development of early diagnostic and therapeutic strategies requires the fabrication of effective in vitro valve mimetic models to elucidate early CAVD mechanisms. MethodsIn this study, we developed a multilayered physiologically relevant 3D valve-on-chip (VOC) system that incorporated aortic valve mimetic extracellular matrix (ECM), porcine aortic valve interstitial cell (VIC) and endothelial cell (VEC) co-culture and dynamic mechanical stimuli. Collagen and glycosaminoglycan (GAG) based hydrogels were assembled in a bilayer to mimic healthy or diseased compositions of the native fibrosa and spongiosa. Multiphoton imaging and proteomic analysis of healthy and diseased VOCs were performed. ResultsCollagen-based bilayered hydrogel maintained the phenotype of the VICs. Proteins related to cellular processes like cell cycle progression, cholesterol biosynthesis, and protein homeostasis were found to be significantly altered and correlated with changes in cell metabolism in diseased VOCs. This study suggested that diseased VOCs may represent an early, adaptive disease initiation stage, which was corroborated by human aortic valve proteomic assessment. ConclusionsIn this study, we developed a collagen-based bilayered hydrogel to mimic healthy or diseased compositions of the native fibrosa and spongiosa layers. When the gels were assembled in a VOC with VECs and VICs, the diseased VOCs revealed key insights about the CAVD initiation process. Statement of significanceCalcific aortic valve disease (CAVD) elevates the risk of death due to cardiovascular pathophysiology by 50 %, however, prevention and mitigation strategies are lacking, clinically. Developing tools to assess early disease would significantly aid in the prevention of disease and in the development of therapeutics. Previously, studies have utilized collagen and glycosaminoglycan-based hydrogels for valve cell co-cultures, valve cell co-cultures in dynamic environments, and inorganic polymer-based multilayered hydrogels; however, these approaches have not been combined to make a physiologically relevant model for CAVD studies. We fabricated a bi-layered hydrogel that closely mimics the aortic valve and used it for valve cell co-culture in a dynamic platform to gain mechanistic insights into the CAVD initiation process using proteomic and multiphoton imaging assessment.

Quantitative softness and texture bimodal haptic sensors for robotic clinical feature identification and intelligent picking.

Replicating human somatosensory networks in robots is crucial for dexterous manipulation, ensuring the appropriate grasping force for objects of varying softness and textures. Despite advances in artificial haptic sensing for object recognition, accurately quantifying haptic perceptions to discern softness and texture remains challenging. Here, we report a methodology that uses a bimodal haptic sensor to capture multidimensional static and dynamic stimuli, allowing for the simultaneous quantification of softness and texture features. This method demonstrates synergistic measurements of elastic and frictional coefficients, thereby providing a universal strategy for acquiring the adaptive gripping force necessary for scarless, antislippage interaction with delicate objects. Equipped with this sensor, a robotic manipulator identifies porcine mucosal features with 98.44% accuracy and stably grasps visually indistinguishable mature white strawberries, enabling reliable tissue palpation and intelligent picking. The design concept and comprehensive guidelines presented would provide insights into haptic sensor development, promising benefits for robotics.

The Emotion Authenticity Recognition (EAR) test: normative data of an innovative test using dynamic emotional stimuli to evaluate the ability to recognize the authenticity of emotions expressed by faces.

Despite research has massively focused on how emotions conveyed by faces are perceived, the perception of emotions' authenticity is a topic that has been surprisingly overlooked. Here, we present the Emotion Authenticity Recognition (EAR) test, a test specifically developed using dynamic stimuli depicting authentic and posed emotions to evaluate the ability of individuals to correctly identify an emotion (emotion recognition index, ER Index) and classify its authenticity (authenticity recognition index (EA Index). The EAR test has been validated on 522 healthy participants and normative values are provided. Correlations with demographic characteristics, empathy and general cognitive status have been obtained revealing that both indices are negatively correlated with age, and positively with education, cognitive status and different facets of empathy. The EAR test offers a new ecological test to assess the ability to detect emotion authenticity that allow to explore the eventual social cognitive deficit even in patients otherwise cognitively intact.

A high-sensitivity flexible piezoelectric tactile sensor utilizing an innovative rigid-in-soft structure

Piezoelectric tactile sensors are widely adopted to detect vibration and other stimuli. Among them, Polyvinylidene fluoride (PVDF) has the advantage of high flexibility, fast response and low cost. However, its sensitivity is restricted by the traditional d33 working mode. Herein, we proposed a rigid-in-soft structure with truncated pyramid shape and soft bottom layer to enhance the force-transmission efficiency. This design synergizes the d33 working mode and d31 working mode, thereby surpassing the sensitivity limit. The mechanism has been analyzed using Finite Element Modeling (FEM), and experiment results indicate that rigid-in-soft tactile sensor exhibits a sensitivity of 35.6 mV/N, along with a linear force detection range of 1–11 N. The sensitivity is approximately 1.7 times higher than that of control sensor without rigid pillar. Furthermore, the sensor displays a great flexibility and reliability, making it highly promising for detecting dynamic stimuli in robotics, such as slip and vibration.

Data-driven message optimization in dynamic sports media: an artificial intelligence approach to predict consumer response

ABSTRACT Artificial intelligence (AI) and big data have the potential to promote advancement across various industries. Sport management and marketing have also significantly transformed due to rapid technological advances such as those in AI and big data analytics. Especially sports companies, however, are still underutilizing the potential of AI. At the same time, considering the existing sport marketing research, the effectiveness and optimization of dynamic marketing stimuli in dynamic sport media settings remains unclear. This study aims to assess the differences between two AI models’ predictive capabilities with and without access to consumers’ biometric data when forecasting the influence of game features on consumers’ responses. Academic theoretical models indicate that individual biometric features have a considerable influence on consumers’ responses; nevertheless, it remains impractical for companies to access these data concerning message effectiveness and ROI evaluation. Therefore, the study attempts to enhance the feasibility of message optimization for companies by trialing a real-time prediction derived from game features alone, exemplifying how much predictive capability is lost by non-available consumer data. Two supervised machine learning models (one initial, primarily theoretical based model; one adapted model due to available data) were trained to reanalyze large-scale eye tracking and game-related data, resulting in high predictive accuracy and appropriate applicability of the models. Both models were able to predict consumers’ responses with over 90% accuracy (initial model: 96%; adapted model: 94%). This study exemplifies AI usage in sport marketing and management, enabling companies to implement more effective marketing messages and strategies for their sponsorship based on real-time evaluation.

The effects of tunnel radius, turn direction, and zone characteristics on drivers’ visual performance

This study aimed to explore the influence of the tunnel radius, turn direction, and zone characteristics on drivers’ visual performance. The study used a field experiment, in which 30 participants drove real vehicles in different curved tunnels, and measured their fixation duration (FD), fixation frequency (FF), saccade duration (SD), and saccade amplitude (SA) as indicators of visual performance. The results showed that the tunnel radius, turn direction, and zone characteristics had significant effects on all four indicators of visual performance. As the tunnel radius decreases, drivers exhibit longer FD and larger SA, while FF and SD decrease correspondingly. Additionally, the directional aspect of turning significantly influences these eye movement parameters, with drivers demonstrating longer FD, lower FF, shorter SD, and smaller SA during left turns compared to right turns. Irrespective of turning direction, drivers allocate the longest average FD and highest FF in the entrance zone, followed by the middle zone, and the shortest duration in the exit zone under the same tunnel radius condition. Similarly, the mean SD and amplitude are highest in the exit zone, followed by the middle zone, and lowest in the entrance zone, regardless of the turning condition. These findings suggest that drivers adjust their visual behavior according to the dynamic and complex visual stimuli and the varying driving task demands in different scenarios of curved tunnel driving. These findings also imply that drivers face different perceptual and cognitive challenges and preferences when driving in curved tunnels with different radii, turn directions, and zone characteristics, which affect their visual adaptation and preparation processes, as well as their visual integration and differentiation processes. This study has some implications and suggestions for the design and management of curved tunnels, as well as for the education and training of drivers. This study also provides new theoretical and empirical evidence for the visual behavior and cognition of drivers in curved tunnels, and provides practical suggestions for enhancing their visual performance and improving their driving safety and efficiency in curved tunnels.