Maintaining an appropriate level of trust in automated driving (AD) is critical to safe driving. However, few studies have explored factors affecting trust in AD in general, and no study, as far as is known, has directly investigated whether driver personality influences driver trust in an AD system. The current study investigates the relation between driver personality and driver trust in AD, focusing on Level 2 AD. Participants were required to perform a period of AD in a driving simulator, during which their gaze and driving behavior were recorded, as well as their subjective trust scores after driving. In three distinct measures, a significant correlation between Openness and driver trust in the AD system is found: participants with higher Openness traits tend to have less trust in the AD system. No significant correlations between driver trust in AD and other personality traits are found. The findings suggest that driver personality has an impact on driver trust in AD. Theoretical and practical implications of this finding are discussed.

Over the last decade, researchers have explored the influence of visual working-memory (WM) load on selective attention in general, by focusing on the modulation of visual WM load on distractor processing in perception. However, there were three distinct hypotheses (perceptual-load hypothesis, resolution hypothesis, and domain-specific hypothesis) with different predictions. While the perceptual-load hypothesis suggests that visual WM capacity load serves as a type of perceptual load, the latter two hypotheses consider visual WM capacity load acting as a type of central executive load, with a constraint that the domainspecific hypothesis claimed that only a content overlap existed between WM load and the perceptual task. By adding a flanker task into the maintenance phase of visual WM, here we attempted to understand the influence of visual WM load on distractor processing. We systematically manipulated the parameters of the task setting between WM and flanker tasks (Experiments 1-4), the perceptual load of flanker task (Experiment 5), the settings of the flanker stimuli and the WM load (Experiment 6), and the content overlap between WM task and flanker task and the exposure time of flanker task (Experiments 7, 8, and 9). However, in 11 out of 12 sub-experiments we consistently found that the visual WM load did not modulate the distractor processing. The implications of these findings are discussed.

Biological motion (BM) is the movement of animate entities, which conveys rich social information. To obtain pure BM, researchers nowadays predominantly use point-light displays (PLDs), which depict BM through a set of light points (e.g., 12 points) placed at distinct joints of a moving human body. Most prevalent BM stimuli are created by state-of-the-art motion capture systems. Although these stimuli are highly precise, the motion capture system is expensive and bulky, and its process of constructing a PLD-based BM is time-consuming and complex. These factors impede the investigation of BM mechanisms. In this study, we propose a free Kinect-based biological motion capture (KBC) toolbox based on the Kinect Sensor 2.0 in C++. The KBC toolbox aims to help researchers acquire PLD-based BM in an easy, low-cost, and user-friendly way. We conducted three experiments to examine whether KBC-generated BM can genuinely reflect the processing characteristics of BM: (1) Is BM from this source processed globally in vision? (2) Does its BM (e.g., from the feet) retain detailed local information? and (3) Does the BM convey emotional information? We obtained positive results in response to all three questions. Therefore, we think that the KBC toolbox can be useful in generating BM for future research.

No abstract

No abstract