- Titel
- The emotion-performance relationship in safety-critical systems
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:ch1-qucosa2-960049
- Übersetzter Titel (DE)
- Die Emotions-Performanz-Beziehung in sicherheitskritischen Systemen
- Erstveröffentlichung
- 2025
- Datum der Einreichung
- 22.10.2024
- Datum der Verteidigung
- 05.02.2025
- DOI
- https://doi.org/10.60687/2025-0040
- Abstract (EN)
- This dissertation examines the emotion-performance relationship and aims to establish a foundation for diagnosing critical and non-critical emotional states. Emotional states such as anger or fear have been demonstrated to influence performance in human-machine systems, potentially resulting in severe negative outcomes, particularly in safety-critical environments. To address this issue, it is essential to establish a formal description of the relationships between user performance and the key parameters valence, arousal, and task demand. For this purpose, a literature review was conducted, and Article 1 proposes the cubic P-EAT model. An analysis of empirical literature indicated that high performance is associated with positive valence, low arousal, and low task demands. Furthermore, the emotion-performance relationship was examined using a simulated safety-critical environment in an experimental study (N = 50, Article 2). The results revealed interindividual differences: While one cluster of subjects benefited from positive valence and low arousal, a second cluster showed high performance associated with negative valence and low arousal, and a third cluster showed no discernible correlation of emotion and performance. Based on these results, a categorization system of users was proposed, the Affective Response Categories (ARCs). To consider these interindividual differences in safety-critical environments, it was necessary to determine whether they present situational states or stable traits. Article 3 demonstrated overall consistency over time, as was additionally replicated in a second experimental study (N = 17). However, the physiological parameters utilized are complex signals that necessitate cost-intense sensor technology. To design the planned user state diagnosis in a scalable and accessible way, the usage without physiological sensors, relying on self-reported emotions instead, should be feasible. For this purpose, users could be categorized into ARCs based on variations in personality traits. A third experiment (N = 50) showed that the three categories were characterized by differences in the personality traits Neuroticism and Openness to experience (Article 4). A positive valence-performance relationship was associated with higher Neuroticism and lower Openness to experience compared to a negative valence-performance relationship. Based on the results of the present dissertation, a diagnostic component could be developed that first determines ARC membership using a baseline of physiological data or personality self-report. Subsequently, the emotional state could be classified into critical and non-critical using current physiological data or emotional self-report data. For example, states of negative valence would be potentially critical for the cluster characterized by a positive valence-performance relationship. In the future, an affect-adaptive system could optimally support users by addressing critical states with suitable adaptation mechanisms, taking cluster membership into consideration. Thereby, performance decrements could be mitigated or avoided, possibly reducing negative consequences, and increasing safety in high-stakes environments.
- Andere Ausgabe
- Towards enhanced performance: An integrated framework of emotional valence, arousal, and task demand
DOI: https://doi.org/10.1080/00140139.2024.2370440 - Affective response categories - toward personalized reactions in affect-adaptive tutoring systems
DOI: https://doi.org/10.3389/frai.2022.873056 - Emotion-performance relationship in safety-critical human-machine systems
DOI: https://doi.org/10.1016/j.chbr.2023.100364 - Personality traits in the emotion-performance-relationship in intelligent tutoring systems
DOI: https://doi.org/10.1007/978-3-031-34735-1_5 - Freie Schlagwörter (EN)
- Affect-adaptive systems, emotional user state, performance
- Klassifikation (DDC)
- 150
- Normschlagwörter (GND)
- Leistung, Gefühl, Persönlichkeitsfaktor
- GutachterIn
- Prof. Dr. Josef F. Krems
- Prof. Dr. Maria Wirzberger
- BetreuerIn Hochschule / Universität
- Prof. Dr. Josef F. Krems
- BetreuerIn - externe Einrichtung
- Prof. Dr. Maria Wirzberger
- Den akademischen Grad verleihende / prüfende Institution
- Technische Universität Chemnitz, Chemnitz
- Version / Begutachtungsstatus
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:ch1-qucosa2-960049
- Veröffentlichungsdatum Qucosa
- 12.03.2025
- Dokumenttyp
- Dissertation
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
