Abstract
The present investigation examines the relationship between emotional user states – composed of emotional valence and arousal – and performance in command-and-control environments. The aim is to gain insights into how the integration of the emotional user state into an adaptive instructional human-machine system can take place. Based on literature, a state of neutral valence is expected to be associated with high performance (H1) and high levels of arousal with low performance (H2). However, according to previous investigations, we also assume interindividual differences in the relationship of emotional user state and performance (H3). In two laboratory experiments, subjects performed a command-and-control task in the domain of anti-air warfare. A software for recognition of emotional face expressions (Emotient FACET) assessed emotional valence. Three physiological measures (heart rate, heart rate variability, pupil width) indicated arousal. Performance was operationalized by performance decrements that occurred whenever a subtask was not accomplished in time. For the analyses, data from two experiments (N = 24, 19–48 years, M = 32.0, SD = 7.2; N = 16, 22–49 years, M = 32.3, SD = 8.7) were used. Statistical analyses confirmed H1 and H2 for many subjects, but there were interindividual differences in the relationship of emotional user state and performance that supported H3. These results indicate that individual models are necessary for the analysis of emotional user state in Adaptive Instructional Systems. Future investigations could consider personality traits in the development of individual models. Furthermore, we suggest a multifactorial approach in the detection of emotional arousal.
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References
Picard, R.: Affective computing. The MIT Press, Cambridge (1997)
Picard, R.W., et al.: Affective learning—a manifesto. BT Technol. J. 22(4), 253–269 (2004)
Dolan, R.J.: Emotion, cognition, and behavior. Science 298(5596), 1191–1194 (2002)
Gray, J.: Emotional modulation of cognitive control: approach–withdrawal states double-dissociate spatial from verbal two-back task performance. J. Exp. Psychol. General 130(3), 436–452 (2001)
Tenenbaum, G., et al.: A conceptual framework for studying emotions–cognitions–performance linkage under conditions that vary in perceived pressure. In: Progress in Brain Research: Mind and Motion: The Bidirectional Link between Thought and Action. Elsevier, pp. 159–178 (2009)
Schmitz-Hübsch, A., Fuchs, S.: Challenges and Prospects of Emotional State Diagnosis in Command and Control Environments. In: Schmorrow, D.D., Fidopiastis, C.M. (eds.) HCII 2020. LNCS (LNAI), vol. 12196, pp. 64–75. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50353-6_5
Schmitz-Hübsch, A.: Der Einfluss situationaler affektiver Zustände auf den Zusammenhang zwischen Aufmerksamkeit und Performanz: Evidenz aus einer Command and Control Aufgabe. Master Thesis, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany (2019)
Spector, P., Goh, A.: The role of emotions in the occupation-al stress process. In: Research in Occupational Stress and Well Being, Exploring Theoretical Mechanisms and Perspectives, pp. 195–232. Emerald Group Publishing Limited (2001)
Staal, M. A.: Stress, cognition, and human performance: A literature review and conceptual framework. Citeseer (2004)
Orasanu, J. M., Backer, P.: Stress and military performance. In: Series in Applied Psychology, Stress and Human Performance, pp. 89–125. Lawrence Erlbaum Associates, Inc., Hillsdale (1996)
Cai, H., Lin, Y.: Modeling of operators’ emotion and task performance in a virtual driving environment. Int. J. Hum Comput Stud. 69(9), 571–586 (2011)
de Tjerk, E.G., Henryk, F.R.A., Neerincx, M.A.: Adaptive automation based on an object-oriented task model: implementation and evaluation in a realistic C2 environment. J. Cogn. Eng. Decis. Mak. 4(2), 152–182 (2010)
Yerkes, R.M., Dodson, J.D.: The Relation of Strength of Stimulus to Rapidity of Habit Formation. J. Comp. Neurol. Psychol. 18, 459–482 (1908)
Mulder, B., Rusthoven, H., Kuperus, M., de Rivecourt, M., de Waard, D.: Short-term heart rate measures as indices of momentary changes in invested mental effort. Human Factors Issues (2007)
Bradley, M.M., Miccoli, L., Escrig, M.A., Lang, P.J.: The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology 45(4), 602–607 (2008)
Pedrotti, M., et al.: Automatic stress classification with pupil diameter analysis. Int. J. Hum.-Comput. Interact. 30(3), 220–236 (2014)
Vogel, S., Schwabe, L.: Learning and memory under stress: implications for the classroom. npj Sci Learn. 1(1), 16011 (2016)
Park, O., Lee, J.: Adaptive instructional systems. In: Handbook of Research on Educational Communications and technology, 2nd ed, pp. 651–684. Lawrence Erlbaum Associates Publishers, Mahwah (2004)
Petrovica, S., Anohina-Naumeca, A., Ekenel, H.K.: Emotion recognition in affective tutoring systems: collection of ground-truth data. Procedia Comput. Sci. 104, 437–444 (2017)
Woolf, B., Burleson, W., Arroyo, I., Dragon, T., Cooper, D., Picard, R.: Affect-aware tutors: recognizing and responding to student affect. IJLT 4, 129–164 (2009)
Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)
Hudlicka, E., McNeese, M.D.: Assessment of user affective and belief states for interface adaptation: application to an air force pilot task. User Model. User-Adap. Inter. 12(1), 1–47 (2002)
Shun, M.C.Y., Yan, M.C., Bo, A., Cyril, L.: Modeling learner's emotions with PAD. In: 2015 IEEE 15th International Conference on Advanced Learning Technologies, Hualien, Taiwan, vol. 72015, pp. 49–51 (2014)
Hasan, M.A., Noor, N.F.M., Rahman, S.S.B.A., Rahman, M.M.: The transition from intelligent to affective tutoring system: a review and open issues. IEEE Access 8, 204612–204638 (2020)
Lazarus, R.S.: Emotion and Adaptation. Oxford University Press, Oxford (1991)
Blascovich, J., Tomaka, J.: The biopsychosocial model of arousal regulation. Adv. Exp. Soc. Psychol. 28, 1–51 (1996)
Blascovich, J.: Challenge and threat. Psychology Press, New York (2008)
van Reekum, C.M., Scherer, K.R.: Levels of processing in emotion-antecedent appraisal. In: Advances in Psychology: Cognitive Science Perspectives on Personality and Emotion, pp. 259–300 (1997)
Matthews, G., Derryberry, D., Siegle, G.J.: Personality and emotion: cognitive science perspectives. In: Advances in Personality Psychology, vol. 1, pp. 199–237. Psychology Press, New York (2000)
Roseman, I.J.: A model of appraisal in the emotion system. In: Appraisal Processes in Emotion: Theory, Methods, Research, pp. 68–91 (2001)
iMotions: iMotions facial expressions analysis pocket guide (2016)
Tong, E.M., et al.: The role of the Big Five in appraisals. Pers. Individ. Differ. 41(3), 513–523 (2006)
Kuppens, P., Tong, E.M.W.: An appraisal account of individual differences in emotional experience. Soc. Pers. Psychol. Compass 4(12), 1138–1150 (2010)
Penley, J.A., Tomaka, J.: Associations among the Big Five, emotional responses, and coping with acute stress. Pers. Individ. Differ. 32(7), 1215–1228 (2002)
Dennis, M., Masthoff, J., Mellish, C.: Adapting progress feedback and emotional support to learner personality. Int. J. Artif. Intell. Educ. 26(3), 877–931 (2015). https://doi.org/10.1007/s40593-015-0059-7
Nardelli, M., Valenza, G., Greco, A., Lanata, A., Scilingo, E.P.: Recognizing emotions induced by affective sounds through heart rate variability. IEEE Trans. Affect. Comput. 6(4), 385–394 (2015)
Mauss, I.B., Robinson, M.D.: Measures of emotion: a review. Cogn. Emot. 23(2), 209–237 (2009)
Lacey, J. I.: Somatic response patterning and stress: some revisions of activation theory. In: Psychological Stress: Issues in Research, pp. 14–37. Appleton-Century-Crofts, New York (1967)
Libby, W.L., Jr., Lacey, B.C., Lacey, J.I.: Pupillary and cardiac activity during visual attention. Psychophysiology 10(3), 270–294 (1973)
Schwarz, J., Fuchs, S., Flemisch, F.: Towards a more holistic view on user state assessment in adaptive human-computer interaction. In: 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), San Diego, CA, USA, vol. 102014, pp. 1228–1234 (2009)
Bota, P.J., Wang, C., Fred, A.L.N., Da Placido Silva, H.: A review, current challenges, and future possibilities on emotion recognition using machine learning and physiological signals. IEEE Access 7, 140990–141020 (2019)
Pacific Science & Engineering Group: Warship Commander 4.4. San Diego, CA (2003)
Schwarz, J., Fuchs, S.: Validating a ‘‘real-time assessment of multi-dimensional user state’’ (RASMUS) for Adaptive Human-Computer Interaction. IEEE (2018)
Wu, C.-H., Huang, Y.-M., Hwang, J.-P.: Review of affective computing in education/learning: trends and challenges. Br. J. Educ. Technol. 47(6), 1304–1323 (2016)
Stemmler, G.: Physiological processes during emotion. In: The Regulation of Emotion, pp. 33–70. Lawrence Erlbaum Associates Publishers, Mahwah (2004)
Mehrabian, A., Russell, J.A.: An Approach to Environmental Psychology. The MIT Press, Cambridge (1974)
Hudlicka, E.: Computational modeling of cognition–emotion interactions: theoretical and practical relevance for behavioral healthcare. In: Emotions and Affect in Human Factors and Human-Computer Interaction, pp. 383–436 (2017)
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Schmitz-Hübsch, A., Stasch, SM., Fuchs, S. (2021). Individual Differences in the Relationship Between Emotion and Performance in Command-and-Control Environments. In: Sottilare, R.A., Schwarz, J. (eds) Adaptive Instructional Systems. Adaptation Strategies and Methods. HCII 2021. Lecture Notes in Computer Science(), vol 12793. Springer, Cham. https://doi.org/10.1007/978-3-030-77873-6_10
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