| 其他摘要 | How to reduce traffic accidents has been an important topic in the field of traffic psychology. In the field of transportation, risky drivers refer to those drivers who are involved in accidents, and if these drivers can be identified by certain factors, traffic safety can be greatly promoted, and the likelihood of traffic accidents can be reduced. Most of the previous studies have screened for risky drivers through some stable individual factors, such as personality differences and risky driving attitudes. These factors are relatively mature, but today's research on cognitive functioning as a risk factor for traffic accidents is scarce and has the following problems: first, the direction of the influence of some cognitive functions on driving behavior is inconsistent or opposite across studies, and it is not known whether higher levels of cognitive functioning lead to safer driving behaviors; second, the previous literature has not examined the differences between high- and low-risk drivers on some cognitive functions. It is still not known whether there are differences in these cognitive functions between high- and low-risk drivers. Therefore, this study aims to address the above questions based on the Task-capability Interface model and the general information processing model of cognitive functions and to further investigate which cognitive functions and which driving behavior indicators differ between high- and low-risk drivers.
Study 1 focused on investigating whether high- and low-risk drivers differed in driving behaviors in executive functions versus daily driving scenarios. Experiment 1 recruited 26 low-risk drivers versus 33 high-risk drivers. The results found that high-risk drivers had lower levels of inhibition and engaged in more risky driving behaviors such as violations and errors. The results of the path analysis found that inhibition positively predicted the likelihood of the accident but did not indirectly affect accidents through everyday risky driving behaviors. The shifting function, on the other hand, positively affects accidents by positively influencing general violations and thus accidents. These results suggest that high-risk drivers may be more inclined to engage in more aggressive behavioral decisions, which can lead to accidents.
Study 2 focused on exploring high and low-risk drivers with differences in basic cognitive functioning and specific driving tasks. The subjects of all experiments in Study 2 were the same as those in Study 1, and the basic cognitive functions of all experiments included perception, attention, and response selection functions. Experiment 2 focused on drivers' ability to cope with critical incidents, and aimed to investigate whether high-risk drivers differed from low-risk drivers in terms of basic cognitive functions and critical incident driving indexes, and the results of Experiment 2 found that high-risk drivers had higher motor perception and coordination abilities. However, high-risk drivers engage in more risky driving behaviors, as shown in the data results that high-risk drivers have faster longitudinal speeds, faster speeds/smaller maximum deceleration before encountering an emergency braking event, and closer lateral distances to construction obstacles when in construction sections. The results of the path analysis also showed that motor perception, coordination ability and alertness ability affect the occurrence of accidents by influencing risky driving behaviors; Experiments 3 and 4 mainly started from the driver's ability to stabilize and control the vehicle, and designed a following task (Experiment 3) and a lane-changing overtaking task (Experiment 4), so as to test whether the high-risk drivers have differences in basic cognitive functions, following behavioral indexes, and lane-changing overtaking behavioral indexes. differences. Only differences in lateral position in the following task were found for high-risk drivers compared to low-risk drivers in Experiment III. The results of the path analysis indicated that both basic cognitive functions were significant negative predictors of lateral position and standard deviation of lateral position, but not on the remaining indicators of the following task. Experiment 4 found that high-risk drivers had poorer lateral position maintenance in both high and low traffic density environments, and that high-risk drivers preferred changing lanes in curves in low traffic density, while high-risk drivers instead overtook more often in high traffic density. However, path analyses only showed that response selection and attention to charge functions were significant negative predictors of overtaking frequency in high-density traffic environments, and there was no evidence that cognitive functioning in a lane-changing overtaking task could affect risky driving behavior indicators such as the number of lane-changing overtakes and thus accidents.
In summary, inhibition, motion perception, and coordination functions have direct effects on accidents, while shifting and vigilance have only indirect effects. Drivers with higher levels of cognitive resources engaged in more cognitive functions (such as motion perception and coordination) may be more prone to overconfidence in driving activities and thus more likely to be involved in accidents. Conversely, cognitive functions requiring fewer cognitive resources (such as inhibition, vigilance, and shifting) may be unrelated to overconfidence. Drivers with better or worse cognitive functions in these areas may exhibit unique behaviors in pro-social driving attitudes, impulsive personality traits, and attention control resource systems. Since this study did not measure variables such as personality traits or subjective confidence levels, the above speculations await further exploration in future research.
The present study bridges the gap that exists in previous research. From the theoretical level, firstly, this study further validates the driving task-ability matching model; secondly, it lays the foundation for subsequent modeling studies on how to identify high-risk drivers based on various cognitive function parameters; from the application level, firstly, this study provides guidance for subsequent fitness-to-drive tests, so that relevant test developers can decide which cognitive tests to include based on the results of this study; secondly, it identifying the cognitive functions that effectively detect high-risk drivers can help us to subsequently develop appropriate cognitive interventions, thereby reducing the likelihood that high-risk drivers will be involved in traffic violations in the future. |
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