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Projective testing of diurnal collective emotion

Published: 13 September 2014 Publication History

Abstract

Projective tests are personality tests that reveal individuals' emotions (e.g., Rorschach inkblot test). Unlike direct question-based tests, projective tests rely on ambiguous stimuli to evoke responses from individuals. In this paper we develop one such test, designed to be delivered automatically, anonymously and to a large community through public displays. Our work makes a number of contributions. First, we develop and validate in controlled conditions a quantitative projective test that can reveal emotions. Second, we demonstrate that this test can be deployed on a large scale longitudinally: we present a four-week deployment in our university's public spaces where 1431 tests were completed anonymously by passers-by. Third, our results reveal strong diurnal rhythms of emotion consistent with results we obtained independently using the Day Reconstruction Method (DRM), literature on affect, well-being, and our understanding of our university's daily routine.

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References

[1]
Ashby, F. G., Isen, A. M. and Turken, A. U. A neuropsychological theory of positive affect and its influence on cognition. Psychological Review 106, 3 (1999), 529.
[2]
Atkinson, A. P., Tunstall, M. L. and Dittrich, W. H. Evidence for distinct contributions of form and motion information to the recognition of emotions from body gestures. Cognition 104, 1 (2007), 59--72.
[3]
Aula, A. and Surakka, V. Auditory Emotional Feedback Facilitates Human-Computer Interaction. In People and Computers XVI - Memorable Yet Invisible, Springer (2002), 337--349.
[4]
Bollen, J., Mao, H. and Zeng, X. Twitter mood predicts the stock market. Journal of Computational Science 2, 1 (2011), 1--8.
[5]
Bradley, M. M. and Lang, P. J. Measuring emotion: the self-assessment manikin and the semantic differential. Journal of Behavior Therapy and Experimental Psychiatry 25, 1 (1994), 49--59.
[6]
Brown, B., Reeves, S. and Sherwood, S. Into the wild: challenges and opportunities for field trial methods. Proc. CHI'11, ACM (2011), 1657--1666.
[7]
Church, K., Cherubini, M. and Oliver, N. A Large-scale Study of Daily Information Needs Captured in Situ. ACM Transactions on Computer-Human Interaction 21, 2 (2014), 10:1--10:46.
[8]
Cordón, L. A. Popular psychology: an encyclopedia. Greenwood Press, Westport, Conn., 2005.
[9]
Ekman, P. and Friesen, W. V. Constants across cultures in the face and emotion. Journal of Personality and Social Psychology 17, 2 (1971), 124.
[10]
Elfenbein, H. A., Foo, M. D., White, J., Tan, H. H. and Aik, V. C. Reading your Counterpart: The Benefit of Emotion Recognition Accuracy for Effectiveness in Negotiation. Journal of Nonverbal Behavior 31, 4 (2007), 205--223.
[11]
Exner Jr, J. E., Erdberg, P., Weiner, I. B., Lichtenberger, E. O., et al. The Rorschach, A Comprehensive System, Volume 1, Basic Foundations and Principles of Interpretation (2003).
[12]
Fortson, K. N. Diurnal Pattern of On-the-Job Injuries, The. Monthly Lab. Rev 127 (2004), 18.
[13]
de Gelder, B. Towards the neurobiology of emotional body language. Nature Reviews Neuroscience 7, 3 (2006), 242--249.
[14]
Golder, S. A. and Macy, M. W. Diurnal and Seasonal Mood Vary with Work, Sleep, and Daylength Across Diverse Cultures. Science 333, 6051 (2011), 1878--1881.
[15]
Goncalves, J., Ferreira, D., Hosio, S., Liu, Y., Rogstadius, J., Kukka, H., Kostakos, V. Crowdsourcing on the spot: altruistic use of public displays, feasibility, performance, and behaviours. Proc. Ubicomp'13, ACM (2013), 753--762.
[16]
Goncalves, J., Hosio, S., Ferreira, D. and Kostakos, V. Game of Words: Tagging Places through Crowdsourcing on Public Displays. Proc. DIS'14, ACM (2014), 705--714.
[17]
Goncalves, J., Hosio, S., Liu, Y. and Kostakos, V. Eliciting Situated Feedback: A Comparison of Paper, Web Forms and Public Displays. Displays 35, 1 (2014), 27--37.
[18]
Gross, R. and Shi, J. The CMU Motion of Body (MoBo) Database. (2001).
[19]
Hernandez, J., Hoque, M., Drevo, W. and Picard, R. W. Mood Meter: Counting Smiles in the Wild. Proc. Ubicomp'12, ACM (2012), 301--310.
[20]
Hosio, S., Goncalves, J., Kostakos, V. and Riekki, J. Exploring Civic Engagement on Public Displays. In S. Saeed (Ed.), User-Centric Technology Design for Nonprofit and Civic Engagements. Springer International Publishing (2014), 91--111.
[21]
Hosio, S., Goncalves, J., Lehdonvirta, V., Ferreira, D. and Kostakos, V. Situated Crowdsourcing Using a Market Model. Proc. UIST'14, ACM (2014).
[22]
Izard, C. E. Human emotions. New York: Plenum Press, 1977.
[23]
Kahneman, D., Krueger, A. B., Schkade, D. A., Schwarz, N. and Stone, A. A. A survey method for characterizing daily life experience: the day reconstruction method. Science 306, 5702 (2004), 1776--1780.
[24]
Kirschbaum, C. and Hellhammer, D. H. Salivary cortisol in psychobiological research: an overview. Neuropsychobiology 22, 3 (1989), 150--169.
[25]
Kukka, H., Oja, H., Kostakos, V., Goncalves, J. and Ojala, T. What makes you click: exploring visual signals to entice interaction on public displays. Proc. CHI'13, ACM (2013), 1699--1708.
[26]
Lang, P. J., Bradley, M. M. and Cuthbert, B. N. International affective picture system (IAPS): Affective ratings of pictures and instruction manual. Technical Report A-8. 2008.
[27]
Lewalter, D. Cognitive strategies for learning from static and dynamic visuals. Learning and Instruction 13, 2 (2003), 177--189.
[28]
Mandryk, R. L. and Atkins, M. S. A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies. International Journal of Human-Computer Studies 65, 4 (2007), 329--347.
[29]
McNeil, J. K., Stones, M. J., Kozma, A., Andres, D. Age Differences in Mood: Structure, Mean Level, and Diurnal Variation. Canadian Journal on Aging/La Revue Canadienne du Vieillissement 13, 02 (1994), 201--220.
[30]
Montepare, J. M., Goldstein, S. B. and Clausen, A. The identification of emotions from gait information. Journal of Nonverbal Behavior 11, 1 (1987), 33--42.
[31]
Partonen, T., Haukka, J., Pirkola, S., Isometsä, E. and Lönnqvist, J. Time patterns and seasonal mismatch in suicide. Acta Psychiatrica Scandinavica 109, 2 (2004), 110--115.
[32]
Pepe, A. and Bollen, J. Between conjecture and memento: shaping a collective emotional perception of the future. eprint arXiv:0801.3864, 2008.
[33]
Roether, C. L., Omlor, L., Christensen, A. and Giese, M. A. Critical features for the perception of emotion from gait. Journal of Vision 9, 6 (2009), 15.
[34]
Rui, H., Liu, Y. and Whinston, A. Whose and what chatter matters? The effect of tweets on movie sales. Decision Support Systems 55, 4 (2013), 863--870.
[35]
Scherer, K. R., Wranik, T., Sangsue, J., Tran, V. and Scherer, U. Emotions in everyday life: Probability of occurrence, risk factors, appraisal and reaction patterns. Social Science Information 43, 4 (2004), 499--570.
[36]
Schmidt, K. L. and Cohn, J. F. Human facial expressions as adaptations: Evolutionary questions in facial expression research. American Journal of Physical Anthropology 116, S33 (2001), 3--24.
[37]
Smolensky, M. H. and D'alonzo, G. E. Medical chronobiology: concepts and applications. American Review of Respiratory Disease 147, (1993), S2--S2.
[38]
Stone, A. A., Schwartz, J. E., Schkade, D., Schwarz, N., Krueger, A., Kahneman, D. A population approach to the study of emotion: diurnal rhythms of a working day examined with the Day Reconstruction Method. Emotion 6, 1 (2006), 139--49.
[39]
Stone, A. A., Smyth, J. M., Pickering, T. and Schwartz, J. Daily Mood Variability: Form of Diurnal Patterns and Determinants of Diurnal Patterns. Journal of Applied Social Psychology 26, 14 (1996), 1286--1305.
[40]
Theorell, T., Ahlberg-Hulten, G., Jodko, M., Sigala, F. and De La Torre, B. Influence of job strain and emotion on blood pressure in female hospital personnel during workhours. Scandinavian Journal of Work, Environment & Health 19, 5 (1993), 313--318.
[41]
Visidon • Home. http://visidon.fi/en/Home, retrieved 14/05/2014.
[42]
Wallbott, H. G. Bodily expression of emotion. European journal of Social Psychology 28, 6 (1998), 879--896.
[43]
Wang, L., Tan, T., Ning, H. and Hu, W. Silhouette analysis-based gait recognition for human identification. Pattern Analysis and Machine Intelligence, IEEE Transactions on 25, 12 (2003), 1505--1518.
[44]
Wood, C. and Magnello, M. E. Diurnal Changes in Perceptions of Energy and Mood. Journal of the Royal Society of Medicine 85, 4 (1992), 191--194.

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    cover image ACM Conferences
    UbiComp '14: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing
    September 2014
    973 pages
    ISBN:9781450329682
    DOI:10.1145/2632048
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 13 September 2014

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    Author Tags

    1. crowdsourcing
    2. diurnal rhythms of emotion
    3. emotion detection
    4. projective tests
    5. public displays

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    UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing
    September 13 - 17, 2014
    Washington, Seattle

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