Differences in visual-spatial working memory and driving behavior between morning-type and evening-type drivers

Accident Analysis and Prevention 136 (2020) 105402

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Differences in visual-spatial working memory and driving behavior between morning-type and evening-type drivers

T

Yan Gea,b, Biying Shenga,b, Weina Qua,b,*, Yuexing Xionga,b, Xianghong Suna,b, Kan Zhanga,b a b

CAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China Department of Psychology, University of Chinese Academy of Sciences, Beijing, China

A R T I C LE I N FO

A B S T R A C T

Keywords: chronotype visual-spatial working memory driving simulator dangerous driving behaviors

Circadian rhythms are changes in life activities over a cycle of approximately 24 hours. Studies on chronotypes have found that there are significant differences in physiology, personality, cognitive ability and driving behavior between morning-type and evening-type people. The purpose of this study is to explore the relationship between visual-spatial working memory and driving behavior between morning-type and evening-type drivers in China. A total of 42 Chinese drivers were selected to participate in this study according to their score on the Morningness-Eveningness Questionnaire, including 22 morning-type drivers and 20 evening-type drivers. During the experiment, the participants completed one cognitive task (visual-spatial working memory), two simulated driving tasks (car-following task and pedestrian-crossing task), and the Dula Dangerous Driving Index (DDDI). The results showed that evening-type drivers self-reported more dangerous driving behaviors but had better lateral control on the simulated driving task than morning-type drivers. In addition, evening-type drivers had greater accuracy when performing the visual-spatial working memory task. Moreover, the accuracy on the visual-spatial working memory task positively predicted the percentage of time over the speed limit by 10 mph (POS10) and negatively correlated with the reaction time measure (time to meet pedestrians) in the pedestriancrossing task. The relationships among chronotype, cognitive ability and driving behavior are also discussed. Understanding the underlying mechanisms could help explain why evening-type drivers perform dangerous driving behaviors more often.

1. Introduction In daily life, we find that some people typically go to bed early and wake up early, while others typically go to bed late and wake up late; these are morning-type (MT) and evening-type (ET) people, respectively (Horne and Ostberg 1976, Adan et al. 2012). According to the chronotype literature, circadian rhythms may influence driving behaviors (Chipman and Jin 2009, Di Milia et al. 2011). Researchers have demonstrated individual differences in driving behaviors between MT and ET individuals (Fafrowicz et al. 2010, Riobermudez et al. 2014, Qu et al. 2015). However, researchers have also begun to question why MT and ET individuals show different patterns behind the wheel. On the one hand, cognitive factors have proven to be useful predictors of unsafe driving (Anstey et al. 2005, Mathias and Lucas 2009, Asimakopulos et al. 2012). On the other hand, researchers also found that cognitive performance is influenced by chronotype (Gritton et al. 2012, Ritchie et al. 2017). Therefore, it is possible that chronotype may influence driving behaviors through its effects on cognitive ability. The

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exploration of this issue will help researchers to reveal the mechanism behind this phenomenon. Early studies on chronotype, defined as an endogenous biological rhythm, found that there were significant differences between MT and ET people, including biological and psychological differences, such as the timing of changes in core temperature (James et al. 1991) and melatonin secretion, as well as visual and auditory memories (Montrone et al. 1980, Weijer-Bergsma et al. 2015), and personality (Vidacek et al. 1988). Similarly, research has shown that basic behavioral differences, such as procrastination (Digdon and Howell 2008) and memory processing (Roberts and Kyllonen 1999), are influenced by timing or phase differences in endogenous biological rhythms. More importantly, some studies have recently shown that driving behaviors (Chipman and Jin 2009, Oginska et al. 2010, Riobermudez et al. 2014) and driving accidents (Petridou and Moustaki 2000, Matthews et al. 2012) could also be influenced by chronotype. Therefore, verifying the effect of chronotype on driving behaviors and exploring how chronotype affects driving behaviors have become topics of interest in the

Corresponding author at: 16 Lincui Road, Chaoyang District, Beijing, 100101, China. E-mail address: [email protected] (W. Qu).

https://doi.org/10.1016/j.aap.2019.105402 Received 14 January 2019; Received in revised form 8 October 2019; Accepted 10 December 2019 0001-4575/ © 2019 Elsevier Ltd. All rights reserved.

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driving field. Previous studies that focused on the relationship between chronotype and driving behaviors have found ET drivers exhibit more unsafe driving behaviors than MT drivers. Some studies have revealed that ET drivers self-reported more dangerous driving behaviors, including risky driving, aggressive driving, negative cognitive/emotional driving and drunk driving (Bergomi et al. 2010, Qu et al. 2015). Another study showed that MT drivers had fewer driving errors and code violations during simulated driving (Bergomi et al. 2010). Other studies have found that the difference between MT and ET drivers was influenced by the time of day. For example, one study suggested that MT drivers’ performance is better than ET drivers’ performance only in the morning (Milia et al. 2008, Correa et al. 2014). Through self-report questionnaires, MT drivers reported they had better driving performance at 6, 8 and 10 A.M., but ET drivers reported they had better performance at 10 and 12 P.M. (Milia et al. 2008). Additionally, a driving simulator demonstrated that MT subjects drove more safely than ET subjects in a morning session. These results suggest that circadian patterns influence driving safety. In similar types of experimental research with motorcycles, MT motorcyclists drive significantly more safely than ET motorcyclists in the morning (Riobermudez et al. 2014). Researchers have suggested that these results are due to the poor sleep quality of ET drivers (Taillard et al. 2010, Tamar et al. 2010, Riobermudez et al. 2014). As circadian patterns differ between MT and ET individuals, attempts to compare differences in their driving performances when their optical time of day is controlled will help researchers better understand the nature of this phenomenon. However, why do MT and ET individuals exhibit different driving behaviors? Our previous study demonstrated that ET drivers exhibited more dangerous driving behavior, had more traffic violations and were involved in more accidents than MT drivers (Qu et al. 2015). We also found that personality had an interactive effect with chronotype on driving behaviors. Chronotype and personality traits could be seen as stable and longitude individual traits that influence people’s driving habits or styles. However, regarding driving performance, cognitive processing and vehicle control may be more important factors than personal traits. Thus, chronotype and cognitive ability may work together to influence driving performance. The important role of cognitive ability in relation to safe driving behavior has been proven in many studies on driving performance (Anstey et al. 2005, Asimakopulos et al. 2012). Many cognitive functions, such as working memory (Ross et al. 2013), visual attention capability (Richardson and Marottoli 2003), visual-spatial working memory (Weijer-Bergsma et al. 2015), etc., are important predictors of driving behavior. In fact, driving tasks could be considered complex perceptual-information-processing tasks, as they require the integration of various processes such as perception, identification, response selection, etc. (Kim and Bishu 2004). Visual-spatial perception, as a basic process, has received considerable attention from researchers. Visualspatial working memory can provide individuals the capacity to detect potentially dangerous situations and the power to react to these situations appropriately, which are important for safe driving behaviors. For example, hazard perception systems have been developed to measure and train perceptual skills during driving (Underwood et al. 2013). Therefore, in this study, we mainly focus on the related cognitive function visual-spatial working memory. Visual-spatial working memory is the ability to temporarily store and mentally manipulate visual-spatial information (Mcafoose and Baune 2009). This ability plays an important role in driving behaviors. Previous research found that people with lower working memory capacity had poorer hazard perception performance under driving conditions (Wood et al. 2016). Moreover, some studies reported that visualspatial skills were directly related to driving performance (Zesiewicz et al. 2002, Stolwyk et al. 2006, Worringham et al. 2010). Heenan et al. (2014) focused on conversation, working memory and situation awareness during driving situations, and they revealed that

conversation impaired situation awareness by taxing working memory. In brief, high loads on working memory could reduce awareness. Additionally, in a series of studies on patients with Parkinson's disease, researchers found that visual-spatial working memory is an important cognitive ability related to driving (Heikkilä et al. 1998, Stolwyk et al. 2006). Specifically, patients with Parkinson's disease and impaired visual information processing showed reduced operational levels of driving performance (Stolwyk et al. 2006). In other words, visual-spatial working memory plays a very important role in driving behavior. Another issue involves whether there is a relationship between chronotype and visual-spatial working memory. Although no studies have directly compared visual-spatial working memory between MT and ET people, some studies have found that visual-spatial working memory has features of biological rhythm changes, such as changes in body temperature, and researchers have found that the accuracy on a visual-spatial working memory task changed with a 3 -h phase delay and corresponded to rectal temperature (Colquhoun 1971, Carrier and Monk 2000, Ramírez et al. 2006). With the exception of one study on the changes in biological rhythms, the relationship between visualspatial working memory and sleep has focused elsewhere. Rana et al. (2017) found that better sleep quality led to better visual-spatial recall and working memory. Conversely, many studies have proven that sleep loss has a negative effect on multiple aspects of cognition (Waters and Bucks 2011). Additional research has found that hippocampal-dependent episodic memory was impacted by sleep loss (Stickgold and Walker 2005, Waters and Bucks 2011, Prince et al. 2014). Although assessing ET people in the morning or MT people in the evening is not completely analogous to sleep loss, different sleep timetables can lead to differences in sleep quality (Cudney et al. 2016, Selvi et al. 2017). Based on these results, there may be some differences in visual-spatial working memory between MT and ET people; however, no research has explored this possibility. Therefore, another important goal of this study is to explore the relationship between chronotype and visualspatial working memory. As an important cognitive ability that influences driving performance, visual-spatial working memory may mediate the effect of chronotype on driving behavior. Therefore, we also explored the mediation effect of visual-spatial working memory in this study. In summary, the purpose of this study is to explore the differences in visual-spatial working memory and driving behavior between MT and ET drivers in China and the mediating effect of visual-spatial working memory. Based on the findings of previous studies, three hypotheses are proposed: (1) There are some differences between MT and ET drivers in driving behavior. Both self-reported driving behavior and simulated driving behavior were measured. MT drivers are expected to show safer driving behavior than ET drivers. However, there may be no difference in simulated driving performance, as the time of day is controlled. (2) ET drivers are expected to show better visual-spatial working memory than MT drivers. (3) Visual-spatial working memory mediates the effect of chronotype on simulated driving behavior. 2. Method 2.1. Participants Forty-two drivers were recruited through the internet and participated in this study. During the recruitment process, participants needed to satisfy the following requirements: (1) be over the age of 18 years; (2) have a valid driver’s license; (3) have at least six months driving experience; (4) drive once a week at least; and (5) be of an MT (rMEQ score = 18-25) or ET (rMEQ score = 4-11) person based on the Chinese version of the reduced Morningness-Eveningness Questionnaire (rMEQ) 2

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accelerate and drive out of the participants’ view at a speed of 70 feet/s. Notably, to ensure that the drivers met the speed requirement initiated the next brake event, another leading car would appear and drive with the participants’ vehicle for at least 1000 feet after the previous braking point. The following measures were recorded in this task: (1) the standard deviation of the lateral position from the central line (SDLP), which represents the lateral position control capability; (2) the braking reaction time (BRT), which refers to the time interval between the braking of the leading car to the braking of the participant car; (3) the minimum time to collision (TTC), which represents the longitudinal position holding capability; (4) total driving time (TIME) for the whole task on the entire road; (5) average speed (AS) on the entire road; and (6) the percentage of time over the speed limit by 10 mph (POS10). The speed limit was 45 mph in this task, so this measure represents the percentage of time above 55 mph.

Fig. 1. Experimental procedure for the visual-spatial working memory task.

(LI et al. 2011). The included participants were 21 to 35 (M = 27.43, SD = 3.46) years old and had a driver’s license for 5–15 (M = 3.08, SD = 2.80) years.

2.2.2.3. Pedestrian-crossing task. In the pedestrian-crossing task, participants were asked to drive naturally under 30 mph and to stay in the center of the right lane. The participants were informed that there might be pedestrians on the side of the road and that some of them would cross the road. During this driving period, the participants encountered 48 pedestrians, including 24 pedestrians on the right side of the road and 24 pedestrians on the left side of the road. Among these pedestrians, 1/3 of the pedestrians (8 from the right and 8 from the left) would cross the road when the participants’ vehicle was 200 feet away. If the participants saw the pedestrian, they were asked to respond based on their daily behavior and ignore the speed and location requirements. Several measures were recorded in the pedestrian-crossing task: (1) time to meet pedestrian (TMP), which indicates the time interval between the beginning of a pedestrian crossing the road and the driver arriving at the same location as the pedestrian; (2) TIME on the entire road; (3) AS on the entire road; and (4) POS10. In this task, the speed limit was 30 mph; therefore, the POS10 index represents the percentage of time above 40 mph. Only some of the variables that were found to be significant are presented in the Results.

2.2. Equipment and Materials 2.2.1. Visual-spatial working memory task (VSWM) The parameters used in this experiment were modified from a previous study (Ramírez et al. 2006). Fig. 1 shows the experimental procedure, which contained 4 phases. First, a fixation cross was presented at the center of the screen for 500 ms. Then, three solid black dots (0.7° diameter) appeared at random positions of 3°, 4°, 5° or 6° from the fixation cross for 300 ms, and the participants were required to remember the spatial position of the three solid black dots but did not need to react otherwise. Third, in the reference phase, a number (1 to 9) appeared on the screen for 3000 ms as an interference stimulus, and the participants needed to press “1” if it was an odd number and “2” if it was an even number. The recognition phase was the final step. A hollow circle appeared near the fixation cross for 2000 ms, and the participants needed to judge whether the circle’s position matched the position of any one of the black dots they had seen before and press “1” if it was a match and “2” if it did not match. There were 24 trials, and the match rate was 50%. Before the formal trials, participants were required to practice several times until they reached an accuracy of more than 70% to ensure that they were familiar with the responses in the interference phase and recognition phase. As the accuracy in the interference phase was high (more than 95%), only the accuracy and reaction time from the recognition phase were recorded (Ramírez et al. 2006). The experiment was implemented using MATLAB 2014a (MathWorks, Inc., USA). The stimulus materials were shown on a laptop with a 13-inch screen with 1280 × 800 pixel resolution and a 60 Hz refresh rate.

2.2.2.4. Driving simulator. In this experiment, a STISIM® driving simulator (STISIM DRIVE M100 K) was used, which was installed on a Dell Workstation (Precision 490, Dual Core Intel Xeon Processor 5130 2 GHz) with a 256 MB PCIe×16 NVIDIA graphics card, Sound Blaster® X-FiTM system, and Dell A225 Stereo system. Driving scenarios were presented on a 27-inch LCD with a 1920 × 1200 pixel resolution. The driving simulator also included a Logitech Momo® steering wheel with force feedback, a gas pedal, and a brake pedal. 2.3. Questionnaires

2.2.2. Simulated driving task 2.2.2.1. Driving situation. A two-lane straight roadway, approximately 51000 feet in length, in a village was used for this driving scenario; there were double amber lines but no other vehicles. The drivers needed to complete a car-following task (15-20 min) and a pedestriancrossing task (15-20 min) using the same road.

2.3.1. The reduced Morningness-Eveningness Questionnaire (rMEQ) The score on the reduced Morningness-Eveningness Questionnaire represents the morning/evening preference (chronotype) of an individual. The Chinese version of the rMEQ (Li et al., 2011) included five items, which were derived from items 1, 7, 10, 18, and 19 of the Morningness-Eveningness Questionnaire (Horne and Ostberg 1976), and has acceptable internal consistency (alpha = 0.65) (Richard et al. 2012). Furthermore, the two relevant chronotypes, MT and ET, were defined in this experiment by their scores on the rMEQ: 4-11 represented ET, 18-25 represented MT, and 12-17 represented neutraltype (excluded from this experiment).

2.2.2.2. Car-following task. In the car-following task, participants followed a leading car that would brake randomly, and the participants were asked to drive at a speed of 45 mph and stay in the center of the right lane. When the leading car braked, the participants were asked to respond based on their daily behavior and ignore the speed and location requirements. During the driving period, 10 brake events occurred randomly. Before braking, the two vehicles were driving at the same speed (45 mph) and maintained a 100-foot distance between them. Then, the leading car would randomly brake, and the speed dropped to 20 feet/s in 4 seconds. At this time, the participants were asked to brake immediately, and when the distance to the leading car was less than 100 feet, the leading car would abruptly

2.3.2. The Dula Dangerous Driving Index (DDDI) In this experiment, self-reported driving behaviors were measured by the DDDI (Dula and Ballard 2010). The Chinese DDDI has been validated with good internal consistency (Cronbach’s α = 0.90) (Qu et al. 2014). It contains 28 items and 4 subscales: 7-item Aggressive Driving (α = 0.78), 9-item Negative Cognitive/Emotional Driving (α = 3

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which indicated that MT drivers controlled the vehicles better than ET drivers. Additionally, the results showed that accuracy in the visualspatial working memory task was positively related to speed in the carfollowing task, which indicated that drivers with higher accuracy in visual-spatial working memory tasks drove faster. The details are shown in Table 2.

0.80), 10-item Risky Driving (α = 0.78) and 2-item Drunk Driving (α = 0.63). Participants were asked to report the frequency of each item using a 5-point Likert scale (1 = “never” to 5 = “always”). 2.3.3. Demographic variables Participants’ sex, age, driving years and estimated average annual mileage were recorded.

3.3. Hierarchical multiple regression (HMR) 2.4. Procedure To explore whether the chronotype can predict accuracy and reaction time in the visual-spatial working memory task, hierarchical multiple regression (HMR) was used. In step 1, the demographic variables, including age and driving years, were entered as control variables, and then the rMEQ was entered in step 2. Overall, the rMEQ significantly predicted the accuracy of visualspatial working memory, accounting for 16.2% of the variance, and sociodemographic factors accounted for 7.8% of the variance. Age significantly negatively predicted accuracy in the visual-spatial working memory task in model 1. When sociodemographic factors were controlled for, the rMEQ scores still showed a significant negative relationship with accuracy in the visual-spatial working memory task in model 2. Additionally, demographic variables and rMEQ did not predict reaction time in the visual-spatial working memory task. The details are shown in Table 3. Additionally, the prediction of the self-reported and simulated driving behaviors with HMR was tested in this experiment. The demographic variables were included in step 1, the rMEQ score was included in step 2, and the accuracy and reaction time in the visualspatial working memory task were included in step 3. The results showed that the rMEQ score significantly and negatively predicted selfreported driving behaviors and positively predicted the track deviation distance in the car-following task. Tables 4 and 5 show the HMR results. Additionally, in the pedestrian-crossing task, the HMR results showed that the annual mileage and reaction time in the visual-spatial working memory task significantly predicted POS10. Driving years and accuracy in the visual-spatial working memory task showed a negative relationship with TMPs. The results are shown in Table 6.

Before the participants arrived at the lab, they completed the rMEQ over the internet and demonstrated that they met the inclusion criteria. Based on previous studies, (Correa et al. 2014) MT participants were expected to drive better in the morning. In contrast, ET participants were expected to drive better in the evening. Therefore, visual-spatial working memory and driving behaviors measured by simulators were separately assessed in the morning for MT participants and in the evening for ET participants. Thus, the participants were invited to the laboratory at one of two different time periods (8:00 a.m. for the MT participants and 4:00 p.m. for the ET participants). Upon arrival, participants were required to complete the rMEQ questionnaire again. Only when the score of the second questionnaire was the same or in the same classification range as that of the first questionnaire were they allowed to participate in the experiment. Then, they were asked to sign a consent document and provide their demographic information. After completing the questionnaires, participants completed the visual-spatial working memory task (approximately 10 min). Finally, they were asked to complete the simulated driving task, which included the practice phase (approximately 15 min) and test phase (approximately 20 min). This experiment was approved by the Internal Review Board of the Institute of Psychology, Chinese Academy of Sciences. 3. Results 3.1. Descriptive statistics The means (M), standard deviations (SD) and ranges (Min-Max) for the demographic variables, rMEQ scores, accuracy and reaction times from the visual-spatial working memory task, scores from the four subscales of the DDDI, and all measures from the car-following task and pedestrian-crossing task are shown in Table 1. The mean score on the rMEQ was 14.76 (range, 6–23). Because the accuracy in the interference phase of the working memory task was high (more than 95%) and was not significantly different between the two groups, only the accuracy and reaction time from the recognition phase are reported. The mean accuracy and reaction time on the visual-spatial working memory task were 89.7% and 1818.79 ms, respectively. An independent-samples t-test was used to analyze the differences between the MT (22 drivers) and ET (20 drivers) groups. The results showed that compared to MT drivers, ET drivers had significantly higher accuracy on the visual-spatial working memory task, ET drivers engaged in more dangerous driving behaviors, and ET drivers deviated less from the track on the car-following task.

4. Discussion The aim of this research is to explore differences in the performance of morning-type versus evening-type individuals in visual-spatial working memory and driving behavior and to explore the relationships among chronotype, visual-spatial working memory, and driving behavior. Certain results from this study verified that ET drivers reported more dangerous driving behavior than MT drivers; however, the effects of chronotype on visual-spatial working memory and simulated driving behaviors were different. The main finding is that the chronotype significantly influenced visual-spatial working memory and driving behavior. First, the ET drivers performed better on the visual-spatial working memory task in the evening than the MT drivers did in the morning. Specifically, ET drivers performed at a higher level of accuracy in the evening than the MT drivers did in the morning, based on no differences in reaction time. On the one hand, this result provides novel evidence about the differences in cognitive ability across chronotypes. On the other hand, this result verifies inferences from previous results; for example, chronotype could lead to a variety of psychological differences (Montrone et al. 1980, Weijer-Bergsma et al. 2015), and at the same time, psychological differences could lead to different cognitive abilities (Baranski et al. 2002, Doobay et al. 2014), which was indeed found in the present study. Second, for self-reported driving behavior, the ET drivers reported more dangerous driving behaviors than the MT drivers, especially in risky driving and negative cognitive/emotional driving scales. These results were consistent with previous research (Qu et al. 2015). Differences in cognitive style may be influencing factors. Specifically,

3.2. Correlation analysis The results showed that the rMEQ scores were negatively related to accuracy in the visual-spatial working memory task (r = -0.354, p < 0.01), which indicated that MT drivers had a higher capability of visual-spatial working memory than ET drivers. In line with our expectations, rMEQ was negatively associated with driving behaviors, including total scores on the DDDI (r = -0.412, p < 0.01) and the subscales of NECD, RD and DD, which indicated that MT drivers exhibited less dangerous driving behaviors. Meanwhile, the results showed a positive relationship between the rMWQ scores and the track deviation distance in the car-following task (r = 0.308, p < 0.05), 4

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Table 1 Descriptive statistics and the results of independent-samples t-tests for demographic variables, the rMEQ and DDDI scores and variables from the simulated driving task Measures

M (SD) N = 42

Demographic variables Age Driving years Yearly mileage rMEQ score Visual-spatial working memory Accuracy Reaction time DDDI NCE AD RD DD Car-following task SDLP (feet) BRT (s) TTC (s) TIME (s) AS (mph) POS10 Pedestrian-crossing task TMP (s) POS10

27.38 (3.42) 3.15 (2.81) 3927.14 (4420.70) 14.76 (5.36) task 0.90 (0.08) 1818.79 (250.23) 2.18 (0.51) 1.92 (0.55) 2.70 (0.62) 2.03 (0.58) 1.42 (0.64) 0.24 (0.25) 0.85 (0.11) 1.52 (0.53) 842.19 (47.22) 36.46 (1.68) 0.01 (0.02) 8.73 (1.90) 0.02 (0.03)

M (SD) MT N = 22

M (SD) ET N = 20

Levene's test

t

　 28.23 (3.55) 3.46 (3.15) 4761.36 (4943.97) 19.55 (1.82) 　 0.87 (0.09) 1848.38 (272.42) 55.41 (10.35) 22.23 (5.17) 12.50 (3.11) 18.14 (3.59) 2.55 (0.91) 　 0.31 (0.31) 0.85 (0.12) 1.50 (0.52) 847.54 (62.14) 36.44 (2.15) 0.01 (0.03) 　 8.98 (2.06) 0.02 (0.03)

　 26.45 (3.10) 2.82 (2.42) 3009.5 (3669.19) 9.50 (1.67) 　 0.92 (0.05) 1784.52 (224.18) 67.00 (15.76) 26.65 (5.23) 14.50 (4.40) 22.70 (6.89) 3.15 (1.57) 　 0.15 (0.12) 0.85 (0.10) 1.55 (0.56) 836.31 (21.76) 36.49 (0.99) 0.01 (0.01) 　 8.45 (1.71) 0.02 (0.03)

　 2.46, n.s. 0.37, n.s. 4.59* 0.16, n.s. 　 3.94, n.s. 0.79, n.s. 6.00* 0.02, n.s. 4.74* 6.93* 2.52, n.s. 　 4.78* 0.39, n.s. 0.01, n.s. 1.53, n.s. 2.91, n.s. 2.33, n.s. 　 0.84, n.s. 0.14, n.s.

　 1.72, n.s. 0.73, n.s. 1.31, n.s. 　 −2.29* 0.81, n.s. −2.79** −2.75** −1.69, n.s. −2.65* −1.55, n.s. 　 2.22* 0.23, n.s. −0.28, n.s. 0.77, n.s. −0.09, n.s. 0.76, n.s. 　 0.89, n.s. −0.16, n.s.

Note: n.s. p > 0.10; * p < 0.05; ** p < 0.01. M (SD) MT = the means and standard deviations of the morning-type group; M (SD) ET = the means and standard deviations of the evening-type group. SDLP = standard deviation of lateral position; TIME = driving time; BRT = braking reaction time; TTC = time to collision; AS = average speed; POS10 = percentage over speed limit (by 10 mph); TMP = time to meet pedestrians. Table 2 Correlations among the rMEQ scores, visual-spatial working memory task measures, DDDI scores, and driving behaviors in the car-following task and pedestriancrossing task Variables

rMEQ

2

3

4

5

6

7

8

9

10

11

12

13

14

15

2 VSWM_ACC 3 VSWM_RT 4 DDDI 5 NCED 6 AD 7 RD 8 DD 9 CFT POS10 10 CFT TIME 11 CFT AS 12 CFT SDLP 13 PDT POS10 14 PDT TMP 15 Age 16 Driving years

-.35* .16 -.41** -.46** -.24 -.34* -.33* .13 .05 -.01 .31* -.00 .06 .29 .07

-.43** .05 .13 -.12 .05 .04 -.25 -.34* .42** -.28 -.22 -.36* -.20 .07

-.07 -.22 -.01 .07 -.08 .32* -.12 -.00 .20 .33* .01 .05 .01

.901 .88** .86** .91** .58** -.07 -.17 .02 -.24 .17 -.16 .02 .13

.801 .62** .67** .59** -.04 -.07 .04 -.22 .14 -.09 -.04 .11

.781 .75** .40** -.10 -.07 -.07 -.13 .29 -.06 .16 .13

.781 .38* -.02 -.27 .05 -.23 .13 -.28 -.02 .12

.631 -.17 -.18 -.04 -.23 -.17 .06 -.02 -.04

-.11 .30 .33* .23 .05 -.02 .04

-.66** .05 .07 .48** .12 -.22

.01 -.13 -.39* -.23 .08

.34* .04 .11 -.11

-.19 .19 .21

.00 -.32*

.50**

Note: rMEQ = total score on the Morningness-Eveningness Questionnaire; VSWM_ACC = accuracy in the visual-spatial working memory task; VSWM_RT = reaction time in the visual-spatial working memory task; DDDI = total score on the Dula Dangerous Driving Index; NCED = negative cognitive/emotional driving; AD = aggressive driving; RD = risky driving; DD = drunk driving; CFT = car-following task; PDT = pedestrian-crossing task; POS10 = percentage of time over the speed limit (by 10 mph); TIME = driving time; AS = average speed; SDLP = standard deviation of lateral position; TMP = time to meet pedestrians. 1 the reliability of variables. * p < 0.05. ** p < 0.01.

result may be related to cognitive ability. We found that ET drivers showed better performance in visual-spatial working memory in the evening. The impact of visual perception and working memory on driving behaviors has been verified (Karlrandeborg and Hedman, 1999, Sasin and Nieuwenstein 2016, Wood et al. 2016). Thus, ET drivers may have high cognitive ability and may be adept at car manipulation. Additionally, this result may be due to prejudice. As the old Chinese saying goes, “Early to bed and early to rise makes a man healthy”; thus, it may be a general assumption that MT individuals perform better in all respects. Therefore, ET individuals might worry about performing badly, and by trying their best, they performed better than MT individuals (Rosenthal 1987). Interestingly, the effect of chronotype showed different trends on

immediate pleasure attracts ET individuals more than MT individuals, which can lead ET individuals to engage in more risky behavior. In contrast, MT individuals were more likely to think about the consequences of some behaviors, including driving behaviors. (Stolarski et al. 2013). However, for the simulated driving task, ET drivers performed better than MT drivers in the car-following task. The ET drivers showed better lane holding capacity, as measured by the deviation distance from the center of the line. As ET drivers and MT drivers could have different peak times (Pavard et al. 1982, Bailey and Heitkemper 2001a, Taillard et al. 2011), they completed their driving tasks during their preferred time period in our experiment. However, we still observed differences between ET drivers in the evening and MT drivers in the morning. This result may be explained by the following. First, this 5

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Table 3 Hierarchical regression models of visual-spatial working memory. Class variables

Predictive variable in class

Accuracy

Reaction time

Model 1

Demographic variable rMEQ Regression model summary

Note: n.s. p > .10;

+

Age Driving years rMEQ F R2 ΔF ΔR2

Model 2

Model 1

Model 2

β

t

β

t

β

t

β

t

-.31 .22

−1.75+ 1.23, n.s.

-.21 .19 -.31 2.39+ .16 3.71+ .08

−1. 14, n.s. 1.07, n.s. −1.93+

.07 -.03

.36, n.s. -.134 n.s.

.01 -.01 .15 .31 .03 .81 .02

.07, n.s. -.05, n.s. .90, n.s.

1.61 .08 1.61 .08

.07 .00 .07 .00

p < .10; * p < .05.

Table 4 Hierarchical regression models of self-reported driving behaviors (DDDI). Class variables

Predictive variable in class

DDDI Model 1

Demographic variables

rMEQ Visual-spatial working memory Regression model summary

Note: n.s. p > .10;

+

Age Driving years Annual mileage rMEQ score Accuracy Reaction time F R2 ΔF ΔR2

Model 2

Model 3

β

t

β

t

β

t

−0.03 0.18 −0.07

−0.14, n.s. 0.96, n.s. −0.34, n.s.

0.11 0.13 −0.05 −0.42

0.51, n.s. 0.76, n.s. −0.28, n.s. −2.63*

0.32, n.s. 0.86, n.s. −0.19, n.s. −2.66* −0.74, n.s. −0.29, n.s.

2.02 0.18 6.92* 0.16

0.07 0.16 −0.04 −0.45 −0.14 −0.05 1.38 0.20 0.27 0.01

Model 2

Model 3

0.33 0.03 0.33 0.03

p < .10; * p < .05.

Table 5 Hierarchical regression models of the standard deviation of lateral position in the car-following task. Class variables

Predictive variable in class

SDLP Model 1

Demographic variables

rMEQ Visual-spatial working memory Regression model summary

Note: n.s. p > .10;

+

Age Driving years Annual mileage rMEQ score Accuracy Reaction time F R2 ΔF ΔR2

β

t

β

t

β

t

0.39 −0.19 −0.32

1.89, n.s. −1.04, n.s. −1.64, n.s.

0.28 −0.15 −0.33 0.34

1.38, n.s. −0.86, n.s. −1.78+ 2.18*

0.30 −0.13 −0.38 0.29 −0.06 0.20 2.02+ 0.26 1.09 0.05

1.40, n.s. −0.74, n.s. −2.01+ 1.80+ −0.33, n.s. 1.16, n.s.

2.47+ 0.22 4.76* 0.10

1.55 0.11 1.55 0.11

p < .10; * p < .05.

performance could therefore indicate that the ET drivers were in a better physiological state. However, better performance could result in two different consequences: the drivers may be safer because they controlled their car better, or the drivers may be too confident in their ability and therefore may act less cautiously. For example, somebody who is very confident in their driving may take more risks while driving, such as speeding. On the other hand, there is also the potential problem of speaking one way and thinking another way (Helman and Reed 2015). Self-reported driving behaviors may be influenced by social expectations (Cloutier et al. 2011). As different driving behaviors were found between MT and ET drivers in this experiment, more stable and objective indexes should be used to assess driving outcomes in future studies.

driving behavior depending on the measure. ET drivers seemed to be more dangerous based on their self-reported driving behavior, but they performed better in the simulated driving tasks than the MT drivers. These results seem to be inconsistent, but they could be explained in several ways. On the one hand, dangerous driving behaviors and the deviation distance from the track are two aspects of driving behaviors. The self-reported driving behaviors focused on more general behaviors that were similar to driving habits (Qu et al. 2014, Qu et al. 2015). As such, they reflect the frequency of engaging in some risky driving behaviors. However, the track deviation distance could reflect lane holding capability, which has been related to physiological factors such as adrenocortical activity, wakefulness and sleepiness (Bailey and Heitkemper 2001b, Kudielka et al. 2006, Schaal et al. 2010). The better 6

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Table 6 Hierarchical regression models of driving behaviors in the pedestrian-crossing task. Class variables

Predictive variable in class

The percentage of overspeed (10 mph) Model 1

Demographic variables rMEQ Visual-spatial working memory Regression model summary

Note: n.s. p > .10;

+

Age Driving years Annual mileage rMEQ score Accuracy Reaction time F R2 ΔF ΔR2

Model 2

Time to meet pedestrian Model 3

Model 1

Model 2

Model 3

β

t

β

t

β

t

β

t

β

t

β

t

0.30 0.20 −0.33

1.44, n.s. 1.10, n.s. −1.72+

0.30 0.20 −0.33 −0.00

1.38, n.s. 1.08, n.s. −1.69+ −0.01, n.s.

0.33 0.22 −0.43 −0.08 −0.07 0.38 2.32+ 0.29 3.85* 0.16

1.61, n.s. 1.29, n.s. −2.32* −0.50, n.s. −0.41, n.s. 2.32*

0.38 −0.41 −0.31

1.93+. −2.38* −1.66, n.s.

0.37 −0.40 −0.31 0.02

1.81+ −2.32* −1.64, n.s. 0.15, n.s.

0.28 −0.34 −0.28 −0.08 −0.38 −0.09 2.46* 0.30 2.49+ 0.10

1.36, n.s. −1.98+ −1.49, n.s. −0.49, n.s. −2.19* −0.56, n.s.

1.83 0.13 1.83 0.13

1.34 0.13 0.00 0.00

3.09* 0.20 3.09* 0.20

2.26+ 0.20 0.02 0.00

p < .10; * p < .05.

morningness-eveningness preference, visual-spatial working memory and driving behaviors. Theoretically, this study is the first to explore the differences in visual-spatial working memory between MT and ET people. Moreover, this study is the first to combine chronotype and visual-spatial working memory to consider their influence on driving behavior. Our results enrich theoretical models of the factors influencing driving behavior and can inspire subsequent research ideas in related cognitive functions related to chronotype and driving behavior. To assist society and individuals, this study attempted to identify the aspects of driving-related behavior that are affected by morningnesseveningness preference. Our findings could be used in selecting professional drivers and arranging work schedules for public transportation drivers.

Unfortunately, visual-spatial working memory did not show a mediating effect between morningness-eveningness preference and driving behaviors. A few reasons may explain these results. First, driving behavior may be affected by different cognitive abilities (Liu and Wu 2009, Wong et al. 2012, Kaber et al. 2016), such as working memory capacity (Gugerty et al. 1999), dynamic visual processing skills (Bolstad, 2001) and attentional resources (Mulder 1986). Driving behaviors are complex and include multiple influencing factors (Paulke et al. 2015), with visual-spatial working memory representing just one of these important factors. Another possible reason is that morningnesseveningness preference and visual-spatial working memory might affect different driving behaviors. Specifically, the deviation distance from the track in the car-following tasks was sensitive to morningness-eveningness preference. However, visual-spatial working memory affected the pedestrian-crossing task because it requires more visual perception (Whitebread and Neilson 2011, Scott et al. 2012). Notably, the driving behaviors in the pedestrian-crossing task showed no significant differences between ET and MT drivers. Therefore, more comprehensive and effective indicators of driving behavior should be identified and verified. The final reason for the lack of a mediation effect was that the difference in driving behaviors caused by the morningness-eveningness preference was not due to visual-spatial working memory. Morningness-eveningness preference is seen as a trait (Qu et al. 2015, Putilov 2017), but visual-spatial working memory is an ability that can be trained (Schlickum et al. 2011, Gade et al. 2017). Therefore, future investigations into the influencing factors of morningness-eveningness preference on driving behaviors should consider additional related factors. There are many limitations to this experiment. First, the study sample mainly comprises young drivers who may not have extensive driving experience. Because the influence on driving behavior in morningness-eveningness preference may depend on practiced driving skills, the inclusion of young participants may have led to nonsignificant results. In addition, measuring driving behaviors at different times of the day may be affected by different physiological and psychological states, which were not controlled in this experiment. Because previous research found that driving performance and accident risk were affected by the time of day (Lenne et al. 1997, Riobermudez et al. 2014), we measured driving behaviors at different times based on morningness-eveningness preference. In future studies, physical and psychological baseline status measurements should be performed prior to driving behavior measurements. Finally, driving behaviors were measured by a simulator, which may lead to a lack of ecological validity. Therefore, subsequent research should consider recording real driving behavior by installing devices for recording data in an actual vehicle. In summary, this study explored the relationships among

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