Correlation Among Behavior, Personality, and Electroencephalography Revealed by a Simulated Driving Experiment

Brain signatures of catastrophic events: Emotion, salience, and cognitive control

Anticipatory brain activity makes it possible to predict the occurrence of expected situations. However, events such as traffic accidents are statistically unpredictable and can generate catastrophic consequences. This study investigates the brain activity and effective connectivity associated with anticipating and processing such unexpected, unavoidable accidents. We asked 161 participants to ride a motorcycle simulator while recording their electroencephalographic activity. Of these, 90 participants experienced at least one accident while driving. We conducted both within-subjects and between-subjects comparisons. During the pre-accident period, the right inferior parietal lobe (IPL), left anterior cingulate cortex (ACC), and right insula showed higher activity in the accident condition. In the post-accident period, the bilateral orbitofrontal cortex, right IPL, bilateral ACC, and middle and superior frontal gyrus also showed increased activity in the accident condition. We observed greater effective connectivity within the nodes of the limbic network (LN) and between the nodes of the attentional networks in the pre-accident period. In the post-accident period, we also observed greater effective connectivity between networks, from the ventral attention network (VAN) to the somatomotor network and from nodes in the visual network, VAN, and default mode network to nodes in the frontoparietal network, LN, and attentional networks. This suggests that activating salience-related processes and emotional processing allows the anticipation of accidents. Once an accident has occurred, integration and valuation of the new information takes place, and control processes are initiated to adapt behavior to the new demands of the environment.

Applications of brain imaging methods in driving behaviour research

Applications of neuroimaging methods have substantially contributed to the scientific understanding of human factors during driving by providing a deeper insight into the neuro-cognitive aspects of driver brain. This has been achieved by conducting simulated (and occasionally, field) driving experiments while collecting driver brain signals of various types. Here, this sector of studies is comprehensively reviewed at both macro and micro scales. At the macro scale, bibliometric aspects of these studies are analysed. At the micro scale, different themes of neuroimaging driving behaviour research are identified and the findings within each theme are synthesised. The surveyed literature has reported on applications of four major brain imaging methods. These include Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), Functional Near-Infrared Spectroscopy (fNIRS) and Magnetoencephalography (MEG), with the first two being the most common methods in this domain. While collecting driver fMRI signal has been particularly instrumental in studying neural correlates of intoxicated driving (e.g. alcohol or cannabis) or distracted driving, the EEG method has been predominantly utilised in relation to the efforts aiming at development of automatic fatigue/drowsiness detection systems, a topic to which the literature on neuro-ergonomics of driving particularly has shown a spike of interest within the last few years. The survey also reveals that topics such as driver brain activity in semi-automated settings or neural activity of drivers with brain injuries or chronic neurological conditions have by contrast been investigated to a very limited extent. Potential topics in driving behaviour research are identified that could benefit from the adoption of neuroimaging methods in future studies. In terms of practicality, while fMRI and MEG experiments have proven rather invasive and technologically challenging for adoption in driving behaviour research, EEG and fNIRS applications have been more diverse. They have even been tested beyond simulated driving settings, in field driving experiments. Advantages and limitations of each of these four neuroimaging methods in the context of driving behaviour experiments are outlined in the paper.

An Evaluation of Executive Control Function and Its Relationship with Driving Performance

The driver's attentional state is a significant human factor in traffic safety. The executive control process is a crucial sub-function of attention. To explore the relationship between the driver's driving performance and executive control function, a total of 35 healthy subjects were invited to take part in a simulated driving experiment and a task-cuing experiment. The subjects were divided into three groups according to their driving performance (aberrant driving behaviors, including lapses and errors) by the clustering method. Then the performance efficiency and electroencephalogram (EEG) data acquired in the task-cuing experiment were compared among the three groups. The effect of group, task transition types and cue-stimulus intervals (CSIs) were statistically analyzed by using the repeated measures analysis of variance (ANOVA) and the post hoc simple effect analysis. The subjects with lower driving error rates had better executive control efficiency as indicated by the reaction time (RT) and error rate in the task-cuing experiment, which was related with their better capability to allocate the available attentional resources, to express the external stimuli and to process the information in the nervous system, especially the fronto-parietal network. The activation degree of the frontal area fluctuated, and of the parietal area gradually increased along with the increase of CSI, which implied the role of the frontal area in task setting reconstruction and working memory maintaining, and of the parietal area in stimulus-Response (S-R) mapping expression. This research presented evidence of the close relationship between executive control functions and driving performance.

The Impact of Two MMPI-2-Based Models of Personality in Predicting Driving Behavior. Can Demographic Variables Be Disregarded?

The driver’s personality is a key human factor for the assessment of the fitness to drive (FTD), affecting driving decisions and behavior, with consequences on driving safety. No previous study has investigated the effectiveness of Minnesota Multiphasic Personality Inventory (MMPI)-2 scales for predicting the FTD. The present study aimed to compare two MMPI-2-based models of normal and pathological personality traits (i.e., Inventory of Driving-related Personality Traits (IVPE)-MMPI vs. Personality Psychopathology Five (PSY-5) scale) in predicting the cognitive FTD. One hundred young and eighty-seven adult active drivers completed the MMPI-2 questionnaire as a measure of personality and a computerized driving task measuring for resilience of attention (Determination Test (DT)), reaction speed (Reaction Test (RS)), motor speed (MS), and perceptual speed (Adaptive Tachistoscopic Traffic Perception Test (ATAVT)). The effects of age, gender, and education were also controlled. Results showed that the models controlled for demographics overperformed those neglecting them for each driving outcome. A negative effect of age was found on each driving task; the effect of gender, favoring males, was found in both the RS and the MS, and the effect of education was found on the DT and the ATAVT. Concerning personality traits, significant effects were found of sensation seeking (IVPE-MMPI) on each outcome; of anxiety (as a measure of emotional instability; IVPE-MMPI) and introversion (PSY-5) on the measures of MS; and of psychopathic deviation (as a measure of self-control; IVPEMMPI) on the DT. The study confirmed the key role of demographic factors in influencing the FTD, further suggesting the usefulness of some MMPI2-based personality scales in the assessment of driving-related personality determinants.

Understanding the Implementation of Airbnb in Urban Contexts: Towards a Categorization of European Cities

The sharing economy has experienced exponential growth in recent years, especially in the short-term rentals (STRs) tourist accommodation sector. This growth has caused disruptive effects in rural and urban contexts, especially in highly touristic cities. These effects can be both positive and negative, revitalizing certain areas and bringing about tension in the socioeconomic fabric. Today, Airbnb is considered the paradigm of this sharing economy model and the STR industry leader. However, as this study suggests, on many occasions the implementation of Airbnb exhibits more of a traditional economic business model than a collaborative economic business model. Through hierarchical cluster analysis, this study identifies different groups of European cities according to the degree of professionalization of Airbnb implementation in their territory. The goal is to find similar patterns in the Airbnbisation process in major European cities, as the social, economic, and spatial impacts of various typologies are very different and even contrary. By understanding and identifying such different models implemented in each territory, better policies can be informed, and more adapted strategies can be pursued by local governments and the tourism industry.

Sad Music Modulates Pain Perception: An EEG Study

Background

Music has shown positive effects on pain management in previous studies. However, the relationship between musical emotional types and therapeutic effects remains unclear. To investigate this issue, this study tested three typical emotional types of music and discussed their neural mechanisms in relation to pain modulation.

Subjects and Methods

In this experiment, 40 participants were exposed to cold pain under four conditions: listening to happy music, listening to neutral music, listening to sad music and no sound. EEG and pain thresholds were recorded. The participants were divided into the remission group and the nonremission group for analysis. Differences among conditions were quantified by the duration of exposure to the pain-inducing stimulus in the remission group. EEG data were obtained using a fast Fourier transform (FFT) and then correlated with the behavioral data.

Results

We found that sad music had a significantly better effect on alleviating pain, as a result of brain oscillations in a higher beta band and the gamma band at the O2 and P4 electrodes. The comparison between the remission group and the nonremission group suggested that personality may affect music-induced analgesia, and dominance, liveliness and introvert and extrovert personality traits were associated with pain modulation by sad music. Additionally, in the network analysis, we compared brain networks under the three conditions and discussed the possible mechanisms underlying the better analgesic effect of sad music.

Conclusion

Sad music may have a better effect on alleviating pain, and its neural mechanisms are also discussed. This work may help understand the effects of music on pain modulation, which also has potential value for clinical use.

Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits

This study introduces two datasets for multimodal research on cognitive load inference and personality traits. Different to other datasets in Affective Computing, which disregard participants’ personality traits or focus only on emotions, stress, or cognitive load from one specific task, the participants in our experiments performed seven different tasks in total. In the first dataset, 23 participants played a varying difficulty (easy, medium, and hard) game on a smartphone. In the second dataset, 23 participants performed six psychological tasks on a PC, again with varying difficulty. In both experiments, the participants filled personality trait questionnaires and marked their perceived cognitive load using NASA-TLX after each task. Additionally, the participants’ physiological response was recorded using a wrist device measuring heart rate, beat-to-beat intervals, galvanic skin response, skin temperature, and three-axis acceleration. The datasets allow multimodal study of physiological responses of individuals in relation to their personality and cognitive load. Various analyses of relationships between personality traits, subjective cognitive load (i.e., NASA-TLX), and objective cognitive load (i.e., task difficulty) are presented. Additionally, baseline machine learning models for recognizing task difficulty are presented, including a multitask learning (MTL) neural network that outperforms single-task neural network by simultaneously learning from the two datasets. The datasets are publicly available to advance the field of cognitive load inference using commercially available devices.