Challenges and Prospects of Emotional State Diagnosis in Command and Control Environments

Schmitz-Hübsch, Alina; Fuchs, Sven

doi:10.1007/978-3-030-50353-6_5

Cited by 3 publications

(3 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: 52%

“…To develop an affect-adaptive training system for safety-critical work environments, we investigated the optimal emotional user state for best possible performance. Previous studies showed important individual differences in the locus of this sweet spot (Schmitz-Hübsch and Fuchs, 2020 ; Schmitz-Hübsch et al, 2021 ). These differences in the emotion-performance relationship pose a challenge for designing affect-adaptive systems since fully individualized adaptation mechanisms would require personal settings.…”

Section: Discussionmentioning

confidence: 90%

“…In a previous investigation ( N = 22, 17–52 years, M = 30.9, SD = 9.7) of emotional state diagnosis, the relationships between positive or negative valence and performance were analyzed on an individual level (Schmitz-Hübsch and Fuchs, 2020 ). The observed experimental task originated from a command-and-control system representing a safety-critical environment.…”

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: 52%

Section: Discussionmentioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

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