Network models of driver behavior

Mattsson, Markus

doi:10.7717/peerj.6119

Cited by 6 publications

(4 citation statements)

References 66 publications

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“…The absence of an edge between two nodes indicates that they are conditionally independent given all other nodes. The GGM has extensively been applied to psychological data [ 106 , 107 , 110 , 128 ].…”

Section: Methodsmentioning

confidence: 99%

“…Hence, network analysis and PLOS ONE visualization can yield new insights into the relations between variables. In psychology, network analysis has recently become a popular alternative for latent variable modelling in exploratory studies of human behavior [101,103,[105][106][107][108][109][110][111]. Psychological networks consist of nodes representing observed variables (e.g., questionnaire items), connected by edges representing the statistical relationships between the variables (their pairwise interactions; [112]).…”

Section: Current Studymentioning

confidence: 99%

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A network model of affective odor perception

Yingxuan¹,

Toet²,

Krone³

et al. 2020

PLoS ONE

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Current Studymentioning

confidence: 99%

A network model of affective odor perception

Yingxuan¹,

Toet²,

Krone³

et al. 2020

PLoS ONE

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“…Mid-to long-term mental care of the victims, the people who support victims and the police officers involved in incidents of aori-unten should be provided in the same manner as in a natural disaster. 4,5 Every country experiencing similar issues of problematic driving should consider implementing such mental health-care measures. International and interdisciplinary collaboration, including an international public-health campaign, will be necessary to devise measures to prevent problematic driving and to establish guidelines for the mental health care of victims and other people affected by this kind of problematic driving.…”

Section: Dear Editormentioning

confidence: 99%

Tailgating (aori-unten): A recent major social issue in Japan

2020

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“…Interestingly, a novel psychometric approach that is compatible with the idea of emotions as emergent phenomena arising from complex interactions among their component parts has been introduced in the recent years. This approach is known as network psychometrics and it has been used to model phenomena ranging from psychopathology (Borsboom, 2017) to personality , driver behavior (Mattsson, 2019), and students' engagement to their studies (Korhonen et al, 2019). A psychometric network model (Epskamp et al, 2018) consists of direct relationships between the observed variables that remain when controlling for the effects of all the other variables in the network.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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.

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Network models of driver behavior

Cited by 6 publications

References 66 publications

A network model of affective odor perception

A network model of affective odor perception

Tailgating (aori-unten): A recent major social issue in Japan

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

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