The Impact of Two MMPI-2-Based Models of Personality in Predicting Driving Behavior. Can Demographic Variables Be Disregarded?
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ABSTRACT:
SUBMITTER: Tinella L
PROVIDER: S-EPMC8000114 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Road traffic injuries are the ninth cause of death across all age groups, globally (WHO, 2015). Many road traffic crashes are caused by Driving Under the Influence (DUI) of alcohol by persons who have previously had their license suspended for the same reason. The aim of this study was to identify specific risk factors and personality characteristics in repeat offenders. The sample was comprised of 260 subjects who were not repeat DUI offenders (DUI-NR), but had a single license suspension between 2010 and 2011; and 97 repeat offenders who received at least two DUI convictions within a period of 5 years. At the time of their first driving license suspension, participants provided their blood alcohol content (BAC) and completed a valid MMPI-2 test. ANOVA and MANOVAs were performed to determine whether there were significant differences in BAC and MMPI-2 profiles between DUI-NR and DUI-R participants and a logistic regression was run to identify whether BAC at the time of the first suspension and specific personality features could predict recidivism. A two-step cluster analysis was run to identify recidivist typologies. Results showed that, relative to DUI-NR participants, DUI-R participants had higher BAC at the time of their first conviction and more problematic MMPI-2 profiles, despite the presence of social desirability responding. The best predictors of recidivism were BAC and the scales of Lie (L), Correction (K), Psychopathic Deviate (4-Pd), Hypomania (9-Ma), and Low Self-Esteem (LSE). Two-step cluster analyses identified two recidivist profiles, according to 32 selected MMPI-2 validity, clinical, content, supplementary, and PSY-5 scales. Comparisons with previous research are discussed and ideas for further study are generated.
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Project description:ObjectiveThere is a substantial body of evidence that the recidivism of impaired-driving offenders is reduced while an ignition interlock device (IID) is on their vehicles. This study examines changes in driving behaviors and drinking behaviors used by DWI offenders to manage driving with the IID.MethodsA total of 166 IID participants who completed two surveys covering the period from arrest to IID installation (T1) and during IID use (T2) were examined. Four domains were covered: demographics, driving environments and transportation needs, reported driving activity, and reported drinking activities. Participants were on average 38 years old, 43% were female, 35% completed college, 34% had an income of more than $50,000, and 83% were employed. For those who provided it, the mean blood alcohol content (BAC) at arrest was .184 g/dL, with only 8 (5%) individuals below .08 g/dL, and 93 (56%) at over .18 g/dL. About 45% were repeat DWI offenders.ResultsBetween T1 and T2 there was a slight increase in acknowledging public transportation was available (p=.001), an increase in other individuals driving the interlock-equipped vehicle (p=.002), an increase in the number of vehicles in the household not registered to the DWI offender (p< .001), and an increase in the number of participants who reported that driving was important to their lifestyle (p=.008). Initial (T1) expectations about whether the interlock would be a problem were significantly different from actual experiences reported in T2 (p<.001). With respect to alcohol consumption, 14% reported abstinence at T2 compared to 2% at T1 (p=.001) and the number of drinks per drinking occasion decreased from a mean of 4.90 at T1 to 3.14 at T2 (p=.001), but the number of drinking occasions increased by a third (p=.003). The number of drinking locations (p=.001), the frequency of stopping after work for a drink (p=.001), and drinking at a bar all decreased (p<.001).ConclusionsInterlock users make some adjustments in how they drink, the amount they drink, and where they drink. This finding suggests that there may be methods that can be used to extend the benefits of the IID beyond the sanction period.
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Project description:Smartphones enjoy high adoption rates around the globe. Rarely more than an arm's length away, these sensor-rich devices can easily be repurposed to collect rich and extensive records of their users' behaviors (e.g., location, communication, media consumption), posing serious threats to individual privacy. Here we examine the extent to which individuals' Big Five personality dimensions can be predicted on the basis of six different classes of behavioral information collected via sensor and log data harvested from smartphones. Taking a machine-learning approach, we predict personality at broad domain ([Formula: see text] = 0.37) and narrow facet levels ([Formula: see text] = 0.40) based on behavioral data collected from 624 volunteers over 30 consecutive days (25,347,089 logging events). Our cross-validated results reveal that specific patterns in behaviors in the domains of 1) communication and social behavior, 2) music consumption, 3) app usage, 4) mobility, 5) overall phone activity, and 6) day- and night-time activity are distinctively predictive of the Big Five personality traits. The accuracy of these predictions is similar to that found for predictions based on digital footprints from social media platforms and demonstrates the possibility of obtaining information about individuals' private traits from behavioral patterns passively collected from their smartphones. Overall, our results point to both the benefits (e.g., in research settings) and dangers (e.g., privacy implications, psychological targeting) presented by the widespread collection and modeling of behavioral data obtained from smartphones.
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Project description:Assessments and predictions of driving behavior are very important to improve traffic safety. We hypothesized that there were some patterns of driving behaviors, and these patterns had some correlation with cognitive states and personalities. To test this hypothesis, an evaluation of driving status, based on electroencephalography (EEG) and steering behavior in a simulated driving experiment, was designed and performed. Unity 3D was utilized to design the simulated driving scene. A photoelectric encoder fixed on the steering wheel and the corresponding data collection, transmission, and storage device was developed by Arduino, to acquire the rotation direction, angle, angular velocity, and angular acceleration of the steering wheel. Biopac MP 150 was utilized to collect the EEG data simultaneously during driving. A total of 23 subjects (mean age 23.6 ± 1.3 years, driving years: 2.4 ± 1.6 years, 21 males and two females) participated in this study. The Fuzzy C-means algorithm (FCMA) was utilized to extract patterns of driving behavior and the cognitive state within the window width of 20 s. The behaviors were divided into five kinds, i.e., negative, normal, alert, stress, and violent behavior, respectively, based on the standard deviation of steering wheel data. The cognitive states were divided into four kinds, i.e., negative, calm, alert, and tension, respectively, based on the EEG data. The correlation of these data, together with the personality traits evaluated using Cattell 16 Personality Factor Questionnaire (16PF) were analyzed using multiclass logistic regression. Results indicated the significance of the cognitive state and seven personality traits [apprehension (O), rule consciousness (G), reasoning (B), emotional stability (C), liveliness (F), vigilance (L), and perfectionism (Q3)] in predicting driving behaviors, and the prediction accuracy was 80.2%. The negative and alert cognitive states were highly correlated with dangerous driving, including negative and violent behaviors. Personality traits complicate the relationship with driving behaviors, which may vary across different types of subjects and traffic accidents.
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Project description:Drivers play the most important role in the human-vehicle-environment system and driving behaviors are significantly influenced by the cognitive state of the driver and his/her personality. In this paper, we aimed to explore the correlation among driving behaviors, personality and electroencephalography (EEG) using a simulated driving experiment. A total of 36 healthy subjects participated in the study. The 64-channel EEG data and the driving data, including the real-time position of the vehicle, the rotation angle of the steering wheel and the speed were acquired simultaneously during driving. The Cattell 16 Personality Factor Questionnaire (16PF) was utilized to evaluate the personalities of subjects. Through hierarchical clustering of the 16PF personality traits, the subjects were divided into four groups, i.e., the Inapprehension group, Insensitivity group, Apprehension group and the Unreasoning group, named after their representative personality trait. Their driving performance and turning behaviors were compared and EEG preprocessing, source reconstruction and the comparisons among the four groups were performed using Statistical Parameter Mapping (SPM). The turning process of the subjects can be formulated into two steps, rotating the steering wheel toward the turning direction and entering the turn, and then rotating the steering wheel back and leaving the turn. The bilateral frontal gyrus was found to be activated when turning left and right, which might be associated with its function in attention, decision-making and executive control functions in visual-spatial and visual-motor processes. The Unreasoning group had the worst driving performance with highest rates of car collision and the most intensive driving action, which was related to a higher load of visual spatial attention and decision making, when the occipital and superior frontal areas played a very important role. Apprehension (O) and Tension (Q4) had a positive correlation, and Reasoning (B) had a negative correlation with dangerous driving behaviors. Our results demonstrated the close correlation among driving behaviors, personality and EEG and may be taken as a reference for the prediction and precaution of dangerous driving behaviors in people with specific personality traits.
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