Decoding visual fatigue in a visual search task selectively manipulated via myopia-correcting lenses.

Introduction: Visual fatigue resulting from sustained, high-workload visual activities can significantly impact task performance and general wellbeing. So far, however, little is known about the underlying brain networks of visual fatigue. This study aimed to identify such potential networks using a unique paradigm involving myopia-correcting lenses known to directly modulate subjectively-perceived fatigue levels. Methods: A sample of N = 31 myopia participants [right eye-SE: -3.77D (SD: 2.46); left eye-SE: -3.75D (SD: 2.45)] performed a demanding visual search task with varying difficulty levels, both with and without the lenses, while undergoing fMRI scanning. There were a total of 20 trials, after each of which participants rated the perceived difficulty and their subjective visual fatigue level. We used representational similarity analysis to decode brain regions associated with fatigue and difficulty, analyzing their individual and joint decoding pattern. Results and discussion: Behavioral results showed correlations between fatigue and difficulty ratings and above all a significant reduction in fatigue levels when wearing the lenses. Imaging results implicated the cuneus, lingual gyrus, middle occipital gyrus (MOG), and declive for joint fatigue and difficulty decoding. Parts of the lingual gyrus were able to selectively decode perceived difficulty. Importantly, a broader network of visual and higher-level association areas showed exclusive decodability of fatigue (culmen, middle temporal gyrus (MTG), parahippocampal gyrus, precentral gyrus, and precuneus). Our findings enhance our understanding of processing within the context of visual search, attention, and mental workload and for the first time demonstrate that it is possible to decode subjectively-perceived visual fatigue during a challenging task from imaging data. Furthermore, the study underscores the potential of myopia-correcting lenses in investigating and modulating fatigue.

Decoding the dynamic perception of risk and speed using naturalistic stimuli: A multivariate, whole-brain analysis.

Time-resolved decoding of speed and risk perception in car driving is important for understanding the perceptual processes related to driving safety. In this study, we used an fMRI-compatible trackball with naturalistic stimuli to record dynamic ratings of perceived risk and speed and investigated the degree to which different brain regions were able to decode these. We presented participants with first-person perspective videos of cars racing on the same course. These videos varied in terms of subjectively perceived speed and risk profiles, as determined during a behavioral pilot. During the fMRI experiment, participants used the trackball to dynamically rate subjective risk in a first and speed in a second session and assessed overall risk and speed after watching each video. A standard multivariate correlation analysis based on these ratings revealed sparse decodability in visual areas only for the risk ratings. In contrast, the dynamic rating-based correlation analysis uncovered frontal, visual, and temporal region activation for subjective risk and dorsal visual stream and temporal region activation for subjectively perceived speed. Interestingly, further analyses showed that the brain regions for decoding risk changed over time, whereas those for decoding speed remained constant. Overall, our results demonstrate the advantages of time-resolved decoding to help our understanding of the dynamic networks associated with decoding risk and speed perception in realistic driving scenarios.

Dynamic measurements of speed and risk perception during driving: Evidence of speed misestimation from continuous ratings and video analysis.

Investigating the factors underlying perceived speed and risk is crucial to ensure safe driving. However, existing studies on this topic usually measure speed and risk perception indirectly after a driving session, which makes it difficult to trace dynamic effects and time points of potential misestimates. To address this problem, we developed and validated a novel continuous method for dynamically measuring risk and speed perceptions. To study the factors affecting risk and speed perception, we presented participants with videos captured on the same racing track from the same point of view but with different drivers who varied in their speed and risk profiles. During the experiment, participants used a joystick to continuously rate the subjectively perceived risk of driving in the first block and the perceived speed in the second block. Our analysis of these dynamic ratings indicates that risk and speed estimates were decoupled, with curves resulting in decreased speeds but increased risk ratings. However, a close distance to the car in front increased both speed and risk. Based on actual and estimated speed data, we found that overtaking cars on curves resulted in participants overestimating their own speed, whereas an increase in the distance to the car in front on a straight course led to underestimations of their own speed. Our results showcase the usefulness of dynamic rating profiles for in-depth investigations into situations that could result in drivers misjudging speed or risk and will thus help the development of more intelligent, human-centered driving assistance systems.

Task and Resting-State Functional Connectivity Predict Driving Violations.

Aberrant driving behaviors cause accidents; however, there is a lack of understanding of the neural mechanisms underlying these behaviors. To address this issue, a task and resting-state functional connectivity was used to predict aberrant driving behavior and associated personality traits. The study included 29 right-handed participants with driving licenses issued for more than 1 year. During the functional magnetic resonance imaging experiment, participants first recorded their resting state and then watched a driving video while continuously rating the risk and speed on each block. Functional connectome-based predictive modeling was employed for whole brain tasks and resting-state functional connectivity to predict driving behavior (violation, error, and lapses), sensation-seeking, and impulsivity. Resting state and task-based functional connectivity were found to significantly predict driving violations, with resting state significantly predicting lapses and task-based functional connectivity showing a tendency to predict errors. Conversely, neither impulsivity nor sensation-seeking was associated with functional connectivity. The results suggest a significant association between aberrant driving behavior, but a nonsignificant association between impulsivity and sensation-seeking, and task-based or resting state functional connectivity. This could provide a deeper understanding of the neural processing underlying reckless driving that may ultimately be used to prevent accidents.

Relationship between virtual reality balloon analogue risk task and risky decision-making.

The balloon analogue risk task (BART) is widely used to assess risk-taking tendencies on behavioral tests. However, biases or unstable results are sometimes reported, and there are concerns about whether the BART can predict risk behavior in the real world. To address this problem, the present study developed a virtual reality (VR) BART to enhance the reality of the task and narrow the gap between performance on the BART and risk behavior in the real world. We evaluated the usability of our VR BART through assessments of the relationships between BART scores and psychological metrics and additionally implemented an emergency decision-making VR driving task to investigate further whether the VR BART can predict risk-related decision-making in emergency situations. Notably, we found that the BART score significantly correlated with both sensation-seeking and risky driving behavior. Additionally, when we split participants into groups with high and low BART scores and compared their psychological metrics, we found that the high-score BART group included more male participants and exhibited higher sensation-seeking and more risky decision-making in an emergency situation. Overall, our study shows the potential of our new VR BART paradigm to predict risky decision-making in the real world.

Identifying neural correlates of multidimensional, subjective gaming experiences during active gameplay.

Studying how gaming experiences are encoded is important to understand the effects of gaming on the brain. Although studies have investigated neural correlates of gaming experiences, the brain patterns related to the full range of subjective experiences across different types of games are yet to be identified. The present study used three custom-made, immersive driving games with different input dynamics (controlling a car, a boat, or a spaceship) and different mechanics to assess subjective gaming experiences in a magnetic resonance imaging scanner. A correlational analysis identified several brain networks associated with different subjective gaming experiences, including visual and attentional processing networks. The contributions of these networks were further validated using meta-analysis-based functional term decoding. The results of the present study point to a range of perceptual, motivational, and control networks that are engaged during active gameplay.

Predicting driving speed from psychological metrics in a virtual reality car driving simulation.

Why do some people tend to drive faster than others? Personality characteristics such as the evaluation of risk to oneself or to others, impulsivity, adherence to norms, but also other personal factors such as gender, age, or driving experience all may play a role in determining how fast people drive. Since driving speed is a critical factor underlying accident prevalence, identifying the psychological metrics to predict individual driving speed is an important step that could aid in accident prevention. To investigate this issue, here, we used an immersive virtual reality driving simulation to analyze average driving speed. A total of 124 participants first took a comprehensive set of personality and background questionnaires and a behavioral risk-taking measure. In the virtual reality experiment, participants were required to navigate a difficult driving course in a minimally-restricted, non-urban setting in order to provide baseline results for speed selection. Importantly, we found that sensation seeking and gender significantly predicted the average driving speed, and that sensation seeking and age were able to predict the maximum driving speed.

Myopia-correcting lenses decrease eye fatigue in a visual search task for both adolescents and adults.

The steady, world-wide increase in myopia prevalence in children over the past decades has raised concerns. As an early intervention for axial-length-related myopia, correcting lenses have been developed (such as Defocus Incorporated Multiple Segment (DIMS) lenses), which have been shown to be effective in slowing myopia progression. Beyond this direct effect, however, it is not known whether such lenses also affect other aspects important to the wearer, such as eye fatigue, and how such effects may differ across age, as these lenses so far are typically only tested with adolescents. In the present work, we therefore investigated perceived fatigue levels according to lens type (normal vs DIMS) and age (adolescents vs adults) in a demanding visual search task ("Finding Wally") at two difficulty levels (easy vs difficult). Whereas age and difficulty did not result in significant differences in eye fatigue, we found a clear reduction of fatigue levels in both age groups when wearing the correcting lenses. Hence, the additional accommodation of these lens types may result in less strain in a task requiring sustained eye movements at near viewing distances.

To Brake or Not to Brake? Personality Traits Predict Decision-Making in an Accident Situation.

Many situations require decisions to be made in very little time-in emergency or accident situations such decisions will carry potentially harmful consequences. Can we predict how people react in such situations from their personality traits alone? Since experimental tests of accident situations are not possible in the real world, existing studies usually employ text-based surveys or post-situation assessments, making predictions and generalization difficult. In the present study, we used virtual reality to create a more life-like situation in order to study decision-making under controlled circumstances. In our experiment, participants trained in an immersive car simulation to complete a race-course as fast as possible. In the testing phase, pedestrians appeared on the course without warning, forcing participants to react. The experiment used a one-shot design to avoid pre-meditation and to test naïve, rapid decision-making. Participants' reactions could be classified into two categories: people who tried to brake, and people who potentially endangered pedestrians by not braking or conducting hazardous evasion maneuvers. Importantly, this latter group of participants scored significantly higher on psychopathy-related traits among a set of personality-related factors. Additional personality factors, as well as age, gender, gaming expertise, and driving experience did not significantly influence participants' decision-making. This result was true for both a Korean sample (N = 94) and an independently-tested German sample (N = 94), indicating cross-cultural stability of the results. Overall, our results demonstrate that decision-making in an extreme, simulated accident situation is critically influenced by personality traits.

Manipulating and decoding subjective gaming experience during active gameplay: a multivariate, whole-brain analysis.

A large number of perceptual and cognitive processes are instantiated during active gameplay, culminating in what is termed the overall "gaming experience", which encapsulates multiple, subjective dimensions of how one feels about the game. Although some research has been conducted into the neural mechanisms underlying the gaming experience, previous studies so far have relied on commercial games that provide little control over key aspects of gameplay and also have focused only on a few individual dimensions of the gaming experience. Here, we used a custom-made, immersive driving car game in four different gameplay versions (baseline, obstacle increase, goal decrease, speed increase) to assess and modulate the subjective gameplay experience while participants underwent a fMRI scan. A multivariate correlation analysis of whole-brain neural activity with behaviorally-identified subjective gaming experience uncovered brain networks associated with different experiences, including higher-level visual processing networks, the default network, and emotional areas. These regions were in addition able to decode the four different game conditions above chance. Our results for the first time describe the full range of cortical networks that become engaged to create the subjective experience during active gameplay.