Individual Differences in the Relationship Between Emotion and Performance in Command-and-Control Environments

Schmitz-Hübsch, Alina; Stasch, Sophie-Marie; Fuchs, Sven

doi:10.1007/978-3-030-77873-6_10

Cited by 3 publications

(6 citation statements)

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“…Cai and Lin ( 2011 ) searched for a sweet spot indicating the optimal emotional user state for the best performance in a driving simulator study. Previous research confirms the existence of sweet spots but also observed important individual differences in the locus of the sweet spot (Schmitz-Hübsch and Fuchs, 2020 ; Schmitz-Hübsch et al, 2021 ). This leads to the second question: How can affect-adaptive systems account for such interindividual differences?…”

Section: Introductionmentioning

confidence: 53%

“…In our investigation of data from a training system for safety-critical tasks, we found a negative association between arousal and performance, where low arousal was associated with high performance (Schmitz-Hübsch et al, 2021 ). Furthermore, the safety-critical task context and potentially fatal consequences of a mistake might result in a certain stress level, which in turn is associated with an increased arousal level.…”

Section: Introductionmentioning

confidence: 88%

“…Emotional arousal, which is physiologically associated with increased pupil diameter (Bradley et al, 2008 ), was recorded using a Tobii Pro Spectrum 300 Hz Eye Tracker. Further arousal measures (heart rate and heart rate variability) were recorded but not further analyzed because of the higher inconsistency in their relationship with performance compared to pupil dilation (Schmitz-Hübsch et al, 2021 ). Performance was assessed via a performance score that considered the priority of the tasks, accuracy, and response time (Becker et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

“…In a further investigation, data from two additional experiments were examined ( N = 24, 19–48 years, M = 32.0, SD = 7.2 and N = 16, 22–49 years, M = 32.3, SD = 8.7; Schmitz-Hübsch et al, 2021 ). In addition to positive and negative valence, another Emotient FACET classifier was added, namely neutral valence.…”

Section: Introductionmentioning

confidence: 99%

“…Following the Circumplex Model of Affect (Russell, 1980 ), emotional arousal was also included in the analysis to more comprehensively portray the emotional state of the user. The arousal dimension was operationalized using three physiological indicators: heart rate, heart rate variability, and pupil diameter (Schmitz-Hübsch et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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Affective Response Categories—Toward Personalized Reactions in Affect-Adaptive Tutoring Systems

Schmitz-Hübsch

Stasch

Becker

et al. 2022

Front. Artif. Intell.

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Affect-adaptive tutoring systems detect the current emotional state of the learner and are capable of adequately responding by adapting the learning experience. Adaptations could be employed to manipulate the emotional state in a direction favorable to the learning process; for example, contextual help can be offered to mitigate frustration, or lesson plans can be accelerated to avoid boredom. Safety-critical situations, in which wrong decisions and behaviors can have fatal consequences, may particularly benefit from affect-adaptive tutoring systems, because accounting for affecting responses during training may help develop coping strategies and improve resilience. Effective adaptation, however, can only be accomplished when knowing which emotions benefit high learning performance in such systems. The results of preliminary studies indicate interindividual differences in the relationship between emotion and performance that require consideration by an affect-adaptive system. To that end, this article introduces the concept of Affective Response Categories (ARCs) that can be used to categorize learners based on their emotion-performance relationship. In an experimental study, N = 50 subjects (33% female, 19–57 years, M = 32.75, SD = 9.8) performed a simulated airspace surveillance task. Emotional valence was detected using facial expression analysis, and pupil diameters were used to indicate emotional arousal. A cluster analysis was performed to group subjects into ARCs based on their individual correlations of valence and performance as well as arousal and performance. Three different clusters were identified, one of which showed no correlations between emotion and performance. The performance of subjects in the other two clusters benefitted from negative arousal and differed only in the valence-performance correlation, which was positive or negative. Based on the identified clusters, the initial ARC model was revised. We then discuss the resulting model, outline future research, and derive implications for the larger context of the field of adaptive tutoring systems. Furthermore, potential benefits of the proposed concept are discussed and ethical issues are identified and addressed.

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Section: Introductionmentioning

confidence: 53%

Section: Introductionmentioning

confidence: 88%