Network models of driver behavior

A network model of affective odor perception

The affective appraisal of odors is known to depend on their intensity (I), familiarity (F), detection threshold (T), and on the baseline affective state of the observer. However, the exact nature of these relations is still largely unknown. We therefore performed an observer experiment in which participants (N = 52) smelled 40 different odors (varying widely in hedonic valence) and reported the intensity, familiarity and their affective appraisal (valence and arousal: V and A) for each odor. Also, we measured the baseline affective state (valence and arousal: BV and BA) and odor detection threshold of the participants. Analyzing the results for pleasant and unpleasant odors separately, we obtained two models through network analysis. Several relations that have previously been reported in the literature also emerge in both models (the relations between F and I, F and V, I and A; I and V, BV and T). However, there are also relations that do not emerge (between BA and V, BV and I, and T and I) or that appear with a different polarity (the relation between F and A for pleasant odors). Intensity (I) has the largest impact on the affective appraisal of unpleasant odors, while F significantly contributes to the appraisal of pleasant odors. T is only affected by BV and has no effect on other variables. This study is a first step towards an integral study of the affective appraisal of odors through network analysis. Future studies should also include other factors that are known to influence odor appraisal, such as age, gender, personality, and culture.

All Happy Emotions Are Alike but Every Unhappy Emotion Is Unhappy in Its Own Way: A Network Perspective to Academic Emotions

Quantitative research into the nature of academic emotions has thus far been dominated by factor analyses of questionnaire data. Recently, psychometric network analysis has arisen as an alternative method of conceptualizing the composition of psychological phenomena such as emotions: while factor models view emotions as underlying causes of affects, cognitions and behavior, in network models psychological phenomena are viewed as arising from the interactions of their component parts. We argue that the network perspective is of interest to studies of academic emotions due to its compatibility with the theoretical assumptions of the control value theory of academic emotions. In this contribution we assess the structure of a Finnish questionnaire of academic emotions using both network analysis and exploratory factor analysis on cross-sectional data obtained during a single course. The global correlational structure of the network, investigated using the spinglass community detection analysis, differed from the results of the factor analysis mainly in that positive emotions were grouped in one community but loaded on different factors. Local associations between pairs of variables in the network model may arise due to different reasons, such as variable A causing variation in variable B or vice versa, or due to a latent variable affecting both. We view the relationship between feelings of self-efficacy and the other emotions as causal hypotheses, and argue that strengthening the students' self-efficacy may have a beneficial effect on the rest of the emotions they experienced on the course. Other local associations in the network model are argued to arise due to unmodeled latent variables. Future psychometric studies may benefit from combining network models and factor models in researching the structure of academic emotions.

Drivers with child passengers: distracted but cautious?

OBJECTIVE

A remarkable portion of children's traffic-related deaths occurred when travelling in as passengers in vehicles, but so far, few studies have focused on crash characteristics and crash risks of drivers with child passengers. It has been assumed that drivers with child passengers drive responsibly, but on the contrary, children in vehicles can distract drivers, increasing crash risks. In this study, we examined fatal crash characteristics and fatal crash risks of drivers with child passengers.

METHODS

Fatal crash data from the U.S. Fatality Analysis Reporting System (FARS) for 1996-2015 were used. Only passenger-vehicle drivers aged 23-46 years old were included in the analysis because they represent the typical age of drivers with 0-9-year-old child passengers in the database. Prevalence of crash characteristics and the odds of being at fault were examined for drivers with only child passengers and compared to drivers with only adult passengers, with no passengers and with both adult and child passengers. Analyses were done separately for intersection crashes and non-junction crashes.

RESULTS

Female drivers were involved in twice as many fatal crashes alone with child passengers compared to male drivers. Drivers with only child passengers were more often reported as being inattentive, but for them, risk-taking behaviours were less typical than for drivers without child passengers. Our results showed that these differences were more evident in non-junction crashes than in intersection crashes. When risk-taking behaviours were controlled, both male and female drivers with only child passengers had higher odds of being at fault than drivers with adult passengers (with or without children) in non-junction crashes, but these differences were not significant in intersection crashes.

CONCLUSIONS

Drivers with child passengers represent a specific driver population. They have a higher tendency to engage in distractions while driving, but they have fewer risk-taking behaviour-related fatal crashes compared to drivers with no child passengers. Our results indicate that the effects of child-passenger-related distractions on fatal crash risks are more relevant outside intersections, presumably because drivers may try to self-regulate their interactions with child passengers and focus on driving in more demanding traffic situations.