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Subjective and Objective Measures

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Emotional Design in Human-Robot Interaction

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

One of the greatest challenges in the study of emotions and emotional states is their measurement. The techniques used to measure emotions depend essentially on the authors’ definition of the concept of emotion. Currently, two types of measures are used: subjective and objective. While subjective measures focus on assessing the conscious recognition of one’s own emotions, objective measures allow researchers to quantify and assess the conscious and unconscious emotional processes. In this sense, when the objective is to evaluate the emotional experience from the subjective point of view of an individual in relation to a given event, then subjective measures such as self-report should be used. In addition to this, when the objective is to evaluate the emotional experience at the most unconscious level of processes such as the physiological response, objective measures should be used. There are no better or worse measures, only measures that allow access to the same phenomenon from different points of view. The chapter’s main objective is to make a survey of the main measures of evaluation of the emotions and emotional states more relevant in the current scientific panorama.

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References

  • Ahani A, Wahbeh H, Nezamfar H, Miller M, Erdogmus D, Oken B (2014) Quantitative change of EEG and respiration signals during mindfulness meditation. J Neuroeng Rehabil 11(1):87. https://doi.org/10.1186/1743-0003-11-87

    Article  Google Scholar 

  • Bartlett MS, Viola PA, Sejnowski TJ, Golomb BA, Larsen J, Hager JC, Ekman P (1996) Classifying facial action. In: Advances in neural information processing systems, pp 823–829

    Google Scholar 

  • Berger H (1929) Über das elektrenkephalogramm des menschen. Archiv für Psychiatrie und Nervenkrankheiten 87(1):527–570

    Article  Google Scholar 

  • Betella A, Verschure PFMJ (2016) The affective slider: a digital self-assessment scale for the measurement of human emotions. PLoS ONE 11(2):1–11. https://doi.org/10.1371/journal.pone.0148037

    Article  Google Scholar 

  • Bucci P, Cang XL, Valair A, Marino D, Tseng L, Jung M, Rantala J, Schneider OS, MacLean KE (2017) Sketching cuddleBits: coupled prototyping of body and behaviour for an affective robot pet. In: Proceedings of the 2017 CHI conference on human factors in computing systems—CHI’17, pp 3681–3692. https://doi.org/10.1145/3025453.3025774

  • Bradley MM, Lang PJ (1994) Measuring emotion: the self-assessment manikin and the semantic differential. J Behav Ther Exp Psychiatry 25(1):49–59. https://doi.org/10.1016/0005-7916(94)90063-9

    Article  Google Scholar 

  • Calvo RA, D’Mello S (2010) Affect detection: an interdisciplinary review of models, methods, and their applications. IEEE Trans Affect Comput 1(1):18–37. https://doi.org/10.1109/T-AFFC.2010.1

    Article  Google Scholar 

  • Damásio A (2003) Looking for Spinoza: joy, sorrow, and the feeling brain. Boston: Mariner Books

    Google Scholar 

  • Desmet P, Hekkert P, Jacobs JJ (2000) When a car makes you smile: development and application of an instrument to measure product emotions. Adv Consum Res 27:111–117

    Google Scholar 

  • Dodge R, Cline TS (1901) The angle velocity of eye movements. Psychol Rev 8:145–157. https://doi.org/10.1037/h0076100

    Article  Google Scholar 

  • Ekman P, Friesen W (1978) Facial action coding system: a technique for the measurement of facial movement. Consulting Psychologists Press, Palo Alto

    Google Scholar 

  • Ekman P, Friesen W, Hager J (2002) The facial action coding system. Research Nexus, Salt Lake City

    Google Scholar 

  • Ekman P, Levenson RW, Friesen WV (1983) Autonomic nervous system activity distinguishes among emotions. Science 221:1208–1210. https://doi.org/10.1126/science.6612338

    Article  Google Scholar 

  • Gambi E, Agostinelli A, Belli A, Burattini L, Cippitelli E, Fioretti S, Spinsante S (2017) Heart rate detection using microsoft kinect: validation and comparison to wearable devices. Sensors 17(8):E1776. https://doi.org/10.3390/s17081776

    Article  Google Scholar 

  • Giorgetta C, Grecucci A, Bonini N, Coricelli G, Demarchi G, Braun C, Sanfey AG (2013) Waves of regret: a meg study of emotion and decision-making. Neuropsychologia 51(1):38–51. https://doi.org/10.1016/j.neuropsychologia.2012.10.015

    Article  Google Scholar 

  • Goldberg J, Wichansky A (2003) Eye tracking in usability evaluation. In: Hyönä J, Radach R, Deubel H (Eds), The mind’s eyes: cognitive and applied aspects of eye movements. Oxford: Elsevier Science, pp 493–516

    Chapter  Google Scholar 

  • Goleman D (1995) Emotional intelligence. Bantam Books, New York

    Google Scholar 

  • Haag A, Goronzy S, Schaich P, Williams J (2004) Emotion recognition using bio-sensors: first steps towards an automatic system. In: Tutorial and research workshop on affective dialogue systems. Springer, Berlin, Heidelberg, pp 36–48

    Google Scholar 

  • Hartridge H, Thompson LC (1948) Methods of investigating eye movements. Br J Ophthalmol 32:581–591. https://doi.org/10.1136/bjo.32.9.581

    Article  Google Scholar 

  • Hui L, Ting LH, See SL, Chan PY (2015) Use of Electroglottograph (EGG) to find a relationship between pitch, emotion and personality. Procedia Manufact 3:1926–1931

    Article  Google Scholar 

  • Izard CE (1979) The maximally discriminative facial Movement Coding System (MAX). Instructional Recourses Centre, University of Delaware, Newark, DL, Newark

    Google Scholar 

  • Javal E (1878) Essai sur la physiologie de la lecture. Annales d’Oculistique 82:242–253

    Google Scholar 

  • Johnston S, Linden DEJ, Healy D, Goebel R, Habes I, Boehm SG (2011) Upregulation of emotion areas through neurofeedback with a focus on positive mood. Cogn Affect Behav Neurosci 11(1):44–51. https://doi.org/10.3758/s13415-010-0010-1

    Article  Google Scholar 

  • Josephson S, Holmes ME (2002) Visual attention to repeated Internet images: testing the scan path theory on the world wide web. In: Proceedings of the eye tracking research & applications symposium 2002. New York: ACM, pp 43–49

    Google Scholar 

  • Juslin P, Scherer K (2005) Vocal expression of affect. In: Harrigan J, Rosenthal R, Scherer K (eds) The new handbook of methods in nonverbal behavior research. Oxford University Press, New York, pp 65–135

    Google Scholar 

  • Kandel ER, Schwartz JH, Jessell TM, Siegelbaum SA, Hudspeth AJ (Eds) Principles of neural science. 5th. McGraw-Hill, 2013. ISBN 978-0- 07-181001-2

    Google Scholar 

  • Kim J, Bee N, Wagner J, André E (2004) Emote to win: affective interactions with a computer game agent. GI Jahrestagung 1:159–164

    Google Scholar 

  • Lang P (1995) The emotion probe: studies of motivation and attention. Am Psychol 50:372–385. https://doi.org/10.1037/0003-066X.50.5.372

    Article  Google Scholar 

  • Lang P, Bradley M, Cuthbert B (1997) Motivated attention: affect, activation, and action. In: Lang P, Simons R, Balaban M (eds) Attention and orienting: sensory and motivational processes. Lawrence Erlbaum Associates, New Jersey, pp 97–135

    Google Scholar 

  • Lang P, Greenwald M, Bradley M, Hamm A (1993) Looking at pictures: affective, facial, visceral, and behavioral reactions. Psychophysiology 30:261–273. https://doi.org/10.1111/j.1469-8986.1993.tb03352.x

    Article  Google Scholar 

  • Lera E De, Garreta-Domingo M (2007) Ten emotion heuristics: guidelines for assessing the user’s affective dimension easily and cost-effectively 2(September):7–10

    Google Scholar 

  • Lewin K (1935) A dynamic theory of personality. McGraw-Hill, New York

    Google Scholar 

  • Levine EL, Xu X, Yang L-Q, Ispas D, Pitariu HD, Bian R, Ding D, Capotescu R, Che H, Musat S (2011) Cross-national explorations of the impact of affect at work using the state-trait emotion measure: a coordinated series of studies in the United States, China, and Romania. Hum Perform 24:405–442. https://doi.org/10.1080/08959285.2011.614302

    Article  Google Scholar 

  • Loza D, Marcos S, Zalama E, Jaime G (2013) Application of the FACS in the design and construction of a mechatronic head with realistic appearance. J Phys Agents 7(1):31–38

    Google Scholar 

  • Martin DM, Teng JZ, Lo TY, Alonzo A, Goh T, Iacoviello BM, Loo CK (2018) Clinical pilot study of transcranial direct current stimulation combined with Cognitive Emotional Training for medication resistant depression. J Affect Disord 232:89–95. https://doi.org/10.1016/j.jad.2018.02.021

    Article  Google Scholar 

  • Marrufo MV, Vaquero E, Cardoso MJ, Gomez CM (2001) Temporal evolution of α and β bands during visual spatial attention. Cogn Brain Res 12(2):315–320. https://doi.org/10.1016/S0926-6410(01)00025-8

    Article  Google Scholar 

  • Mussakhojayeva S, Kalidolda N, Sandygulova A (2017) Adaptive strategies for multi-party interactions with robots in public spaces. In: International conference on social robotics. Springer, Cham, pp 749–758

    Chapter  Google Scholar 

  • Mutlu B, Yamaoka F, Kanda T, Ishiguro H, Hagita, N. (2009) Nonverbal leakage in robots: communication of intentions through seemingly unintentional behavior. In: Proceedings of the 4th ACM/IEEE international conference on human robot interaction, p 69. https://doi.org/10.1145/1514095.1514110

  • Nishitani S, Shinohara K (2013) NIRS as a tool for assaying emotional function in the prefrontal cortex. Front Hum Neurosci 7:770. https://doi.org/10.3389/fnhum.2013.00770

    Article  Google Scholar 

  • Norman D (2004) Emotional design: why we love (or hate) everyday things. Basic books, New York

    Book  Google Scholar 

  • Notzon S, Steinberg C, Zwanzger P, Junghöfer M (2017) Modulating emotion perception–opposing effects of inhibitory and excitatory prefrontal cortex stimulation. Biol Psychiatry Cogn Neurosci Neuroimaging 3(4):329–336. https://doi.org/10.1016/j.bpsc.2017.12.007

    Article  Google Scholar 

  • Phan KL, Wager T, Taylor SF, Liberzon I (2002) Functional neuroanatomy of emotion: a meta-analysis of emotion activation studies in PET and fMRI. Neuroimage 16(2):331–348. https://doi.org/10.1006/nimg.2002.1087

    Article  Google Scholar 

  • Poole A, Ball L (2006) Eye tracking in HCI and usability research. In: Ghaoui C (ed) Encyclopedia of human computer interaction. Idea Group Reference, USA, pp 211–219

    Chapter  Google Scholar 

  • Reeder RW, Pirolli P, Card SK (2001) WebEyeMapper and WebLogger: tools for analyzing eye tracking data collected in web-use studies. Extended abstracts of the conference on human factors in computing systems, CHI 2001. ACM Press, New York, pp 19–20

    Google Scholar 

  • Reuderink B, Mühl C, Poel M (2013) Valence, arousal and dominance in the EEG during game play. Int J Auton Adapt Commun Syst 6(1):45–62

    Article  Google Scholar 

  • Ribeiro AS, Salvado D, Evans G, Augusto JS, Ferreira HA (2014) A multimodal low-cost platform for acquisition of electrophysiological signals interfacing with portable devices. In: PhyCS, pp 63–70

    Google Scholar 

  • Russel JA, Barret LF (1999) Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant. J Pers Soc Psychol 76:805–819. https://doi.org/10.1037/0022-3514.76.5.805

    Article  Google Scholar 

  • Scherer KR, Wallbott HG (1994) Evidence for universality and cultural variation of differential emotion response patterning. J Pers Soc Psychol 66:310–328

    Article  Google Scholar 

  • Setz C, Schumm J, Lorenz C, Arnrich B, Tröster G (2009) Using ensemble classifier systems for handling missing data in emotion recognition from physiology: one step towards a practical system. In: 3rd international conference on affective computing and intelligent interaction and workshops, 2009. ACII 2009. IEEE, pp 1–8

    Google Scholar 

  • Spielberger CD (1988) Manual for the State-Trait Anger Expression Inventory (STAXI). Psychological Assessment Resources, Odessa, FL

    Google Scholar 

  • Spielberger CD (1994) State Trait Anger Expression Inventory (STAXI–2). OS Giunti, Firenze, Italy

    Google Scholar 

  • Stemmler G, Heldmann M, Pauls C, Scherer T (2001) Constraints for emotion specificity in fear and anger: the context counts. Psychophysiology 38:275–291

    Article  Google Scholar 

  • Tracy J, Robins R (2004) Show your pride: evidence for a discrete emotion expression. Psychol Sci 15:194–197. https://doi.org/10.1111/j.0956-7976.2004.01503008.x

    Article  Google Scholar 

  • Tutsoy O, Gongor F, Barkana DE, Kose H (2017) An emotion analysis algorithm and implementation to Nao humanoid robot. In: The Eurasia proceedings of science, technology, engineering & mathematics, vol 1, pp 316–330. Accessed from https://www.researchgate.net/profile/Fatma_Guengoer2/publication/320417231_AN_EMOTION_ANALYSIS_ALGORITHM_AND_IMPLEMENTATION_TO_NAO_HUMANOID_ROBOT/links/59e48102aca2724cbfe908e0/AN-EMOTION-ANALYSIS-ALGORITHM-AND-IMPLEMENTATION-TO-NAO-HUMANOID-ROBOT.pdf

  • Watson D, Clark L, Tellegen A (1988) Development and validation of brief measures of positive and negative affect: the PANAS scales. J Pers Soc Psychol 54:1063–1070. https://doi.org/10.1037/0022-3514.54.6.1063

    Article  Google Scholar 

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Acknowledgements

Hugo A. Ferreira acknowledges the financial support for writing this chapter from “CAMELOT—C2 Advanced Multi-domain Environment and Live Observation Technologies” Project ID: 740736, funded under the European Commission grants

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Authors and Affiliations

  1. Faculdade de Ciências, Instituto de Biofísica e Engenharia Biomédica, Universidade de Lisboa, Lisboa, Portugal

    Hugo Alexandre Ferreira

  2. Centro de Investigação e Intervenção Social (CIS-IUL), Instituto Universitário de Lisboa (ISCTE-IUL), Lisbon, Portugal

    Magda Saraiva

Authors
  1. Hugo Alexandre Ferreira
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  2. Magda Saraiva
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Correspondence to Hugo Alexandre Ferreira .

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Editors and Affiliations

  1. IADE – Faculdade de Design, Tecnologia e Comunicação, Universidade Europeia, Lisbon, Portugal

    Hande Ayanoğlu

  2. IADE – Faculdade de Design, Tecnologia e Comunicação, Universidade Europeia, Lisbon, Portugal

    Emília Duarte

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Ferreira, H.A., Saraiva, M. (2019). Subjective and Objective Measures. In: Ayanoğlu, H., Duarte, E. (eds) Emotional Design in Human-Robot Interaction. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-96722-6_9

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  • DOI: https://doi.org/10.1007/978-3-319-96722-6_9

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