Effects of specific emotions on subjective judgment,driving performance,and perceived workload

The aim of this paper was to explore effects of specific emotions on subjective judgment, driving performance, and perceived workload. The traditional driving behavior research has focused on cognitive aspects such as attention, judgment, and decision making. Psychological findings have indicated that affective states also play a critical role in a user’s rational, functional, and intelligent behaviors. Most applied emotion research has concentrated on simple valence and arousal dimensions. However, recent findings have indicated that different emotions may have different impacts, even though they belong to the same valence or arousal. To identify more specific affective effects, seventy undergraduate participants drove in a vehicle simulator under three different road conditions, with one of the following induced affective states: anger, fear, happiness, or neutral. We measured their subjective judgment of driving confidence, risk perception, and safety level after affect induction; four types of driving errors: Lane Keeping, Traffic Rules, Aggressive Driving, and Collision while driving; and the electronic NASA-TLX after driving. Induced anger clearly showed negative effects on subjective safety level and led to degraded driving performance compared to neutral and fear. Happiness also showed degraded driving performance compared to neutral and fear. Fear did not have any significant effect on subjective judgment, driving performance, or perceived workload. Results suggest that we may need to take emotions and affect into account to construct a naturalistic and generic driving behavior model. To this end, a specific-affect approach is needed, beyond the sheer valence and arousal dimensions. Given that workload results are similar across affective states, examining affective effects may also require a different approach than just the perceived workload framework. The present work is expected to guide emotion detection research and help develop an emotion regulation model and adaptive interfaces for drivers.     

Personality,counterfactual thinking,and negative emotional reactivity

ObjectivesPeople differ substantially in their emotional responses to negative stimuli. Separate lines of research have reported that individual differences and mental simulations contribute to emotional symptoms. Here, we explore the independent and interrelated contribution of personality traits and counterfactual thoughts to the intensity, duration, and overproduction of negative emotions.MethodA sample of mixed-level athletes (n = 243) completed questionnaire assessments in relation to their most recent unsuccessful competition.ResultsWe found that personality dimensions (extraversion, neuroticism, and openness) relate to the direction and magnitude of person counterfactuals. We also found that personality dimensions (neuroticism, extraversion, openness, and agreeableness) and the direction of counterfactual thoughts (upward or downward) relate to the intensity, duration, and/or overproduction of negative emotions. Lastly, we found that personality and counterfactual thoughts had independent rather than interrelated contributions to the experience of unpleasant emotions.ConclusionsThese findings carry important theoretical and practical implications with regard to identifying individuals susceptible to experiencing elevated emotional symptoms in response to short-term stressors.     

An Evolutionary Analysis of Learned Attention

Humans and many other species selectively attend to stimuli or stimulus dimensions—but why should an animal constrain information input in this way? To investigate the adaptive functions of attention, we used a genetic algorithm to evolve simple connectionist networks that had to make categorization decisions in a variety of environmental structures. The results of these simulations show that while learned attention is not universally adaptive, its benefit is not restricted to the reduction of input complexity in order to keep it within an organism's processing capacity limitations. Instead, being able to shift attention provides adaptive benefit by allowing faster learning with fewer errors in a range of ecologically plausible environments.     

缺失数据的结构方程建模:全息极大似然估计时辅助变量的作用

本研究通过蒙特卡洛模拟考查了采用全息极大似然估计进行缺失数据建模时辅助变量的作用。具体考查了辅助变量与研究变量的共缺机制、共缺率、相关程度、辅助变量数目与样本量等因素对参数估计结果精确性的影响。结果表明,当辅助与研究变量共缺时:(1)对于完全随机缺失的辅助变量,结果更容易出现偏差;(2)对于MAR-MAR组合机制,纳入单个辅助变量是有益的;对于MAR-MCAR或MAR-MNAR组合机制,纳入多于一个辅助变量的效果更好;(3)纳入与研究变量低相关的辅助变量对结果也是有益的。     

A new method for automated reconstruction of pre-transformation microstructures

A new method, referred to as the cluster method, is proposed and tested for the reconstruction of pre-transformation microstructure in Ti–6Al–4V alloy. The cluster method begins by constructing clusters of daughter grains (room-temperature hcp α-phase) that have a high probability of being transformed from the same parent grain (high-temperature bcc β-phase). These clusters are then grown, and back-transformed to the parent grain orientations using the ‘summation of mutual misorientation angle method’ (SMMA method). The cluster method is validated by comparing the obtained results with the results of the SMMA and triplet methods.     

Children's creation of algorithms: simulations and gestures

Experiments showed that children are able to create algorithms, that is, sequences of operations that solve problems, and that their gestures help them to do so. The theory of mental models, which is implemented in a computer program, postulates that the creation of algorithms depends on mental simulations that unfold in time. Gestures are outward signs of moves and they help the process. We tested 10-year-old children, because they can plan, and because they gesture more than adults. They were able to rearrange the order of 6 cars in a train (using a siding), and the difficulty of the task depended on the number of moves in minimal solutions (Experiment 1). They were also able to devise informal algorithms to rearrange the order of cars when they were not allowed to move the cars, and the difficulty of the task depended on the complexity of the algorithms (Experiment 2). When children were prevented from gesturing as they formulated algorithms, the accuracy of their algorithms declined by13% (Experiment 3). We discuss the implications of these results.     

Does COVID-19 impact the frequency of threatening events in dreams? An exploration of pandemic dreaming in light of contemporary dream theories

Ninety-one dreams collected during the Covid-19 pandemic (the epidemic-situation sample) were compared with ninety-one dreams collected before the start of the epidemic (the non-epidemic-situation sample). The dreams were classified according to their content, using methods based on previous studies. The frequency of themes was compared to predictions that would be anticipated by three contemporary theories of dreaming: 1) threat simulation theory (TST); 2) incorporation continuity hypothesis (ICH); and 3) social simulation theory (SST). The epidemic-situation sample dreamed more of threatening events than the non-epidemic-situation sample (supporting the TST) and more of non-aggression threatening events, possibly due to the hyperassociation during sleep. However, the epidemic-situation sample did not show a greater prevalence of illness events in dreams (not supporting the ICH). Additionally, there was no significant difference in social neutral and positive events in dreams between the two samples as would have been predicted by the SST.     

An adaptive temporal-causal network model to analyse extinction of communication over time

The persistence of information communicated between humans is difficult to measure as it is affected by many features. This paper presents an approach to computationally model the cognitive processes of information sharing to describe persistence or extinction of communication in Twitter over time. The adaptive mental network model explains, for example, how an individual can experience information overflow on a topic, and how this affects the sharing of information. Parameter tuning by Simulated Annealing is used to identify characteristics of the network model that fit to empirical data from Twitter. The data collected is related to the independentism in Catalunya, Spain, which is considered a global issue with repercussion in Europe.     

Mapping drivers’ mental models of adaptive cruise control to performance

Driver support features (DSF) have the potential to improve safety, but they also change the driver-vehicle relationship —as well as their respective roles and responsibilities. To maximize safety, it is important to understand how drivers’ knowledge and understanding of these technologies—referred to as drivers’ mental models—impact performance and safety. This simulator study examined how drivers with different mental models of adaptive cruise control performed in edge cases. The study compared the responses of groups of drivers, with strong and weak mental models of ACC, established through a combination of screening, training, and exposure, in edge case situations in a high-fidelity driving simulator. In general, participants with strong mental models were faster than those with weak mental models to respond in edge-case situations—defined as cases where the ACC did not detect an approaching object, such as a slow-moving motorcycle. The performance deficits observed for drivers with weak mental models appear to reflect uncertainty surrounding how ACC will behave in edge cases.     

Influence of the median opening length on driving behaviors in the crossover work zone—A driving simulation study

Work zone safety has been a major concern for drivers and transportation agencies. Considering the severe consequences of crossover work zone crashes coupled with the limited literature on the median opening length, this paper aims to investigate the influence of the median opening length on driving behaviors from a behavioral perspective in crossover work zones. A driving simulation study with 32 participants was carried out to investigate the driving performance by metrics (speed, acceleration, maximum steering wheel speed, and lane-changing trajectory) with respect to five median opening lengths. The significant differences of each driving performance metric among various median opening lengths were found in the process of crossing through the median opening on the entrance by-pass and exit by-pass. The results indicated that drivers’ speeds were always higher than the speed limits for all median opening lengths. A narrower median opening may induce risky driving behaviors such as sharp deceleration and decelerating while lane-changing, which may increase speed variance. When crossing through the median opening with a larger length, high speed variance, sluggish lane-changing operations, and improper lane-using behaviors were observed because the excessive lane-changing space provided by the larger median opening length would decrease the situation awareness and increase the speed preference heterogeneity among drivers. Finally, some traffic management strategies were proposed, which can improve the safety and mobility of crossover work zones and further provide a basis for the formulation of related standards.