Personality traits and affective states: Relationships with and without affect induction

Letzring, Tera D.; Adamcik, Lacey A.

doi:10.1016/j.paid.2014.11.011

Cited by 36 publications

(36 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…These results are consistent with previous studies reporting that these two traits are 242 associated with positive affects (Holtgraves, 2011;Letzring & Adamcik, 2015;Ready 243 & Robinson, 2008), and that agreeableness is closely associated with the temporal 244 regions responsible for social information processing (DeYoung et al, 2010;B. W. 245 Haas et al, 2015;Haas, Ishak, Denison, Anderson, & Filkowski, 2015).…”

Section: Test-retest Reliability 200supporting

confidence: 90%

Quantitative Personality Predictions from a Brief EEG Recording

et al. 2019

Preprint

View full text Add to dashboard Cite

The assessment of personality is crucial not only for scientific inquiries but also for real-world applications such as personnel selection. However, most existing ways to quantify personality traits rely on self-reported scales, which are susceptible to biases such as self-presentational concerns. In this study, we propose and evaluate a novel implicit measure of personality that uses machine learning (ML) algorithms to predict an individual's levels in the Big Five personality traits from 5 minutes of electroencephalography (EEG) recordings. Results from a large test sample of 196 volunteers indicated that the personality scores derived from the proposed measure converged significantly with a commonly used questionnaire, predicted behavioral indices and psychological adjustment in a manner similar to self-reported scores, and were relatively stable across time. These evaluations suggest that the proposed measure can serve as a viable alternative to conventional personality questionnaires in practice.

show abstract

Section: Test-retest Reliability 200supporting

confidence: 90%

Quantitative Personality Predictions from a Brief EEG Recording

et al. 2019

Preprint

View full text Add to dashboard Cite

show abstract

“…Personality traits have been linked to the experience of emotional states in a long tradition of psychological research (e.g., Larsen and Ketelaar, 1991;Letzring and Adamcik, 2015). In line with this, anxiety has been found to be predicted by inter-individual differences in personality variables -besides others by depression (Nutt, 1999) and self-evaluations (e.g., Chamorro-Premuzic et al, 2008;Xiao et al, 2014).…”

Section: Anxiety and Trust In Automationmentioning

confidence: 88%

“…Furthermore, personality traits play an important role in the individual tendency to experience emotional states (e.g., Larsen and Ketelaar, 1991;Letzring and Adamcik, 2015). For example, the link between neuroticism and the tendency to experience negative affect is well supported (e.g., Larsen and Ketelaar, 1991).…”

Section: Introductionmentioning

confidence: 99%

Scared to Trust? – Predicting Trust in Highly Automated Driving by Depressiveness, Negative Self-Evaluations and State Anxiety

Kraus¹,

Scholz²,

Meßner

et al. 2020

Front. Psychol.

View full text Add to dashboard Cite

The advantages of automated driving can only come fully into play if these systems are used in an appropriate way, which means that they are neither used in situations they are not designed for (misuse) nor used in a too restricted manner (disuse). Trust in automation has been found to be an essential psychological basis for appropriate interaction with automated systems. Well-balanced system use requires a calibrated level of trust in correspondence with the actual ability of an automated system. As for these far-reaching implications of trust for safe and efficient system use, the psychological processes, in which trust is dynamically calibrated prior and during the use of automated technology, need to be understood. At this point, only a restricted body of research investigated the role of personality and emotional states for the formation of trust in automated systems. In this research, the role of the personality variables depressiveness, self-efficacy, self-esteem, and locus of control for the experience of anxiety before the first experience with a highly automated driving system were investigated. Additionally, the relationship of the investigated personality variables and anxiety to subsequent formation of trust in automation was investigated. In a driving simulator study, personality variables and anxiety were measured before the interaction with an automated system. Trust in the system was measured after participants drove with the system for a while. Trust in the system was significantly predicted by state anxiety and the personality characteristics self-esteem and selfefficacy. The relationships of self-esteem and self-efficacy were mediated by state anxiety as supported by significant specific indirect effects. While for depression the direct relationship with trust in automation was not found to be significant, an indirect effect through the experience of anxiety was supported. Locus of control did not show a significant association to trust in automation. The reported findings support the importance of considering individual differences in negative self-evaluations and anxiety when being introduced to a new automated system for individual differences in trust in automation. Implications for future research as well as implications for the design of automated technology in general and automated driving systems are discussed.

show abstract

“…The learners' emotional state could play such a role. A recent study examined the role of personality traits with regard to responding to emotion induction procedures (Letzring & Adamcik, ). They reported that neuroticism and openness to experience did not affect the strength of their emotion induction.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Emotions and multimedia learning: the moderating role of learner characteristics

Knörzer

Brünken

Park

2016

Computer Assisted Learning

View full text Add to dashboard Cite

The Cognitive-Affective Theory of Learning with Media postulates that affective factors as well as individual learner characteristics impact multimedia learning. The present study investigated how experimentally induced positive and negative emotions influence multimedia learning and how learner characteristics moderated this impact. Results showed that the group with the negative emotion induction outperformed the group with the positive emotion induction with regard to learning outcome. Cognitive resources (working memory capacity, prior knowledge) and openness to experience were significant predictors for learning. In addition, learners with highest prior knowledge or working memory capacity could compensate the emotional impact on learning. Neuroticism enhanced the emotional impact on learning outcome as a moderator.

show abstract

Personality traits and affective states: Relationships with and without affect induction

Cited by 36 publications

References 15 publications

Quantitative Personality Predictions from a Brief EEG Recording

Quantitative Personality Predictions from a Brief EEG Recording

Scared to Trust? – Predicting Trust in Highly Automated Driving by Depressiveness, Negative Self-Evaluations and State Anxiety

Emotions and multimedia learning: the moderating role of learner characteristics

Contact Info

Product

Resources

About