Cảm biến đi bộ đa mô hình và cá nhân hóa sẽ chuyển đổi hiểu biết của chúng ta về cảm xúc

Affective Science - Tập 4 - Trang 480-486 - 2023
Katie Hoemann1, Jolie B. Wormwood2, Lisa Feldman Barrett3,4,5, Karen S. Quigley3
1Department of Psychology, KU Leuven, Leuven, Belgium
2Department of Psychology, University of New Hampshire, Durham, USA
3Department of Psychology, Northeastern University, Boston, USA
4Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Cambridge, USA
5Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, USA

Tóm tắt

Cảm xúc vốn phức tạp – nằm trong não bộ nhưng bị ảnh hưởng bởi điều kiện bên trong cơ thể và bên ngoài thế giới – dẫn đến sự biến đổi đáng kể trong trải nghiệm. Tuy nhiên, hầu hết các nghiên cứu không được thiết kế để thu thập một cách đầy đủ sự biến đổi này. Trong bài báo này, chúng tôi thảo luận về những điều có thể được khám phá nếu cảm xúc được nghiên cứu một cách có hệ thống trong bối cảnh cá nhân 'trong thế giới thực', thông qua phương pháp lấy mẫu trải nghiệm được kích hoạt sinh học: một phương pháp cảm biến đi bộ đa phương thức và sâu sắc về cá nhân, liên kết cơ thể và tâm trí qua các ngữ cảnh và theo thời gian. Chúng tôi nêu rõ lý do cho phương pháp này, thảo luận về các thách thức trong việc triển khai và áp dụng rộng rãi, đồng thời chỉ ra những cơ hội đổi mới do các công nghệ mới nổi mang lại. Việc thực hiện những đổi mới này sẽ làm phong phú thêm phương pháp và lý thuyết tại biên giới của khoa học cảm xúc, thúc đẩy nghiên cứu cảm xúc trong bối cảnh vào tương lai.

Từ khóa

#cảm xúc #cảm biến đi bộ #nghiên cứu cá nhân #công nghệ mới nổi #khoa học cảm xúc

Tài liệu tham khảo

Barrett, L. F. (2017). The theory of constructed emotion: An active inference account of interoception and categorization. Social Cognitive and Affective Neuroscience, 12(1), 1–23. https://doi.org/10.1093/scan/nsw154 Barrett, L. F. (2022). Context reconsidered: Complex signal ensembles, relational meaning, and population thinking in psychological science. American Psychologist, 77(8), 894–920. https://doi.org/10.1037/amp0001054 Barrett, L. F., Adolphs, R., Marsella, S., Martinez, A. M., & Pollak, S. D. (2019). Emotional expressions reconsidered: Challenges to inferring emotion from human facial movements. Psychological Science in the Public Interest, 20(1), 1–68. https://doi.org/10.1177/1529100619832930 Barrett, L. F., & Simmons, W. K. (2015). Interoceptive predictions in the brain. Nature Reviews Neuroscience, 16(7), 419–429. https://doi.org/10.1038/nrn3950 Berntson, G. G., Cacioppo, J. T., & Quigley, K. S. (1994). Autonomic cardiac control, I: Estimation and validation from pharmacological blockades. Psychophysiology, 31(6), 572–585. https://doi.org/10.1111/j.1469-8986.1994.tb02350.x Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. Blanke, E. S., Brose, A., Kalokerinos, E. K., Erbas, Y., Riediger, M., & Kuppens, P. (2020). Mix it to fix it: Emotion regulation variability in daily life. Emotion, 20(3), 473–485. https://doi.org/10.1037/emo0000566 Blei, D. M., & Jordan, M. I. (2006). Variational inference for Dirichlet process mixtures. Bayesian Analysis, 1(1), 121–143. https://doi.org/10.1214/06-ba104 Bleichner, M. G., & Debener, S. (2017). Concealed, unobtrusive ear-centered EEG acquisition: CEEGrids for transparent EEG. Frontiers in Human Neuroscience, 11, 163. https://doi.org/10.3389/fnhum.2017.00163 Bradley, M. M., & Lang, P. J. (2000). Measuring emotion: Behavior, feeling, and physiology. In R. D. Lane & L. Nadel (Eds.), Cognitive neuroscience of emotion (pp. 242–276). Oxford University Press. Coco, M. I., Mønster, D., Leonardi, G., Dale, R., & Wallot, S. (2021). Unidimensional and multidimensional methods for recurrence quantification analysis with crqa. The R Journal, 13(1), 143–165. https://doi.org/10.32614/RJ-2021-062 Dillen, N., Ilievski, M., Law, E., Nacke, L. E., Czarnecki, K., & Schneider, O. (2020). Keep calm and ride along: Passenger comfort and anxiety as physiological responses to autonomous driving styles. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3313831.3376247 Doyle, C. M., Lane, S. T., Brooks, J. A., Wilkins, R. W., Gates, K. M., & Lindquist, K. A. (2022). Unsupervised classification reveals consistency and degeneracy in neural network patterns of emotion. Social Cognitive and Affective Neuroscience, 17(11), 995–1006. https://doi.org/10.1093/scan/nsac028 Durán, J. I., & Fernández-Dols, J.-M. (2021). Do emotions result in their predicted facial expressions? A meta-analysis of studies on the co-occurrence of expression and emotion. Emotion, 21(7), 1550–1569. https://doi.org/10.1037/emo0001015 Fan, M., Chou, C.-A., Yen, S.-C., & Lin, Y. (2019). A network-based multimodal data fusion approach for characterizing dynamic multimodal physiological patterns (arXiv:1901.00877). arXiv. http://arxiv.org/abs/1901.00877 . Accessed 4 Mar 2023. Ganzel, B. L., Morris, P. A., & Wethington, E. (2010). Allostasis and the human brain: Integrating models of stress from the social and life sciences. Psychological Review, 117(1), 134–174. Ghaffari, R., Yang, D. S., Kim, J., Mansour, A., Wright, J. A., Model, J. B., Wright, D. E., Rogers, J. A., & Ray, T. R. (2021). State of sweat: Emerging wearable systems for real-time, noninvasive sweat sensing and analytics. ACS Sensors, 6(8), 2787–2801. https://doi.org/10.1021/acssensors.1c01133 Giurgiu, M., Niermann, C., Ebner-Priemer, U., & Kanning, M. (2020). Accuracy of sedentary behavior–triggered ecological momentary assessment for collecting contextual information: Development and feasibility study. JMIR MHealth and UHealth, 8(9), e17852. https://doi.org/10.2196/17852 Gordon, A. M., & Mendes, W. B. (2021). A large-scale study of stress, emotions, and blood pressure in daily life using a digital platform. Proceedings of the National Academy of Sciences, 118(31), e2105573118. https://doi.org/10.1073/pnas.2105573118 Haddad, M., Hermassi, S., Aganovic, Z., Dalansi, F., Kharbach, M., Mohamed, A. O., & Bibi, K. W. (2020). Ecological validation and reliability of hexoskin wearable body metrics tool in measuring pre-exercise and peak heart rate during shuttle run test in professional handball players. Frontiers in Physiology, 11, 957. https://doi.org/10.3389/fphys.2020.00957 Harari, G. M., Müller, S. R., Aung, M. S., & Rentfrow, P. J. (2017). Smartphone sensing methods for studying behavior in everyday life. Current Opinion in Behavioral Sciences, 18, 83–90. https://doi.org/10.1016/j.cobeha.2017.07.018 Haslbeck, J. M. B., & Waldorp, L. J. (2020). mgm: Estimating time-varying mixed graphical models in high-dimensional data. Journal of Statistical Software, 93(8), 1–46. https://doi.org/10.18637/jss.v093.i08 Hoemann, K., Khan, Z., Feldman, M. J., Nielson, C., Devlin, M., Dy, J., Barrett, L. F., Wormwood, J. B., & Quigley, K. S. (2020). Context-aware experience sampling reveals the scale of variation in affective experience. Scientific Reports, 10, 12459. https://doi.org/10.1038/s41598-020-69180-y Hoemann, K., Khan, Z., Kamona, N., Dy, J., Barrett, L. F., & Quigley, K. S. (2021). Investigating the relationship between emotional granularity and cardiorespiratory physiological activity in daily life. Psychophysiology, 58(6), e13818. https://doi.org/10.1111/psyp.13818 Hoemann, K., Nielson, C., Yuen, A., Gurera, J. W., Quigley, K. S., & Barrett, L. F. (2021). Expertise in emotion: A scoping review and unifying framework for individual differences in the mental representation of emotional experience. Psychological Bulletin, 147(11), 1159–1183. https://doi.org/10.1037/bul0000327 Hoemann, K., Lee, Y., Kuppens, P., Gendron, M., & Boyd, R. L. (2023). Emotional granularity is associated with daily experiential diversity. Affective Science. https://doi.org/10.1007/s42761-023-00185-2 Ibanez, A. (2022). The mind’s golden cage and cognition in the wild. Trends in Cognitive Sciences, 26(12), 1031–1034. https://doi.org/10.1016/j.tics.2022.07.008 Kalokerinos, E. K., Erbas, Y., Ceulemans, E., & Kuppens, P. (2019). Differentiate to regulate: Low negative emotion differentiation is associated with ineffective use but not selection of emotion-regulation strategies. Psychological Science, 30(6), 863–879. https://doi.org/10.1177/0956797619838763 Kanning, M., Niermann, C., Ebner-Primer, U., & Giurgiu, M. (2021). The context matters - not all prolonged sitting bouts are equally related to momentary affective states: An ambulatory assessment with sedentary-triggered E-diaries. International Journal of Behavioral Nutrition and Physical Activity, 18(1), 106. https://doi.org/10.1186/s12966-021-01170-3 Kaplan, H. S., & Zimmer, M. (2020). Brain-wide representations of ongoing behavior: A universal principle? Current Opinion in Neurobiology, 64, 60–69. https://doi.org/10.1016/j.conb.2020.02.008 Kappeler-Setz, C., Gravenhorst, F., Schumm, J., Arnrich, B., & Tröster, G. (2013). Towards long term monitoring of electrodermal activity in daily life. Personal and Ubiquitous Computing, 17(2), 261–271. https://doi.org/10.1007/s00779-011-0463-4 Kleckner, I. R., Zhang, J., Touroutoglou, A., Chanes, L., Xia, C., Simmons, W. K., Quigley, K. S., Dickerson, B. C., & Barrett, L. F. (2017). Evidence for a large-scale brain system supporting allostasis and interoception in humans. Nature Human Behaviour, 1(5), 0069. https://doi.org/10.1038/s41562-017-0069 Kuppens, P., Van Mechelen, I., Smits, D. J., & De Boeck, P. (2003). The appraisal basis of anger: Specificity, necessity and sufficiency of components. Emotion, 3(3), 254–269. https://doi.org/10.1037/1528-3542.3.3.254 Kyriakou, K., Resch, B., Sagl, G., Petutschnig, A., Werner, C., Niederseer, D., Liedlgruber, M., Wilhelm, F. H., Osborne, T., & Pykett, J. (2019). Detecting moments of stress from measurements of wearable physiological sensors. Sensors, 19(17), Article 17. https://doi.org/10.3390/s19173805 Liao, Y., & Schembre, S. (2018). Acceptability of continuous glucose monitoring in free-living healthy individuals: Implications for the use of wearable biosensors in diet and physical activity research. JMIR MHealth and UHealth, 6(10), e11181. https://doi.org/10.2196/11181 Lodewyckx, T., Tuerlinckx, F., Kuppens, P., Allen, N. B., & Sheeber, L. (2011). A hierarchical state space approach to affective dynamics. Journal of Mathematical Psychology, 55(1), 68–83. https://doi.org/10.1016/j.jmp.2010.08.004 Ma, Q., Mermelstein, R. J., & Hedeker, D. (2022). A shared-parameter location-scale mixed model to link the responsivity in self-initiated event reports and the event-contingent Ecological Momentary Assessments. Statistics in Medicine, 41(10), 1780–1796. https://doi.org/10.1002/sim.9328 Mesquita, B. (2022). Between us: How cultures create emotions. Norton. Monti, A., Porciello, G., Panasiti, M. S., & Aglioti, S. M. (2021). Gut markers of bodily self-consciousness (p. 2021.03.05.434072). bioRxiv. https://doi.org/10.1101/2021.03.05.434072 Nabian, M., Yin, Y., Wormwood, J., Quigley, K. S., Barrett, L. F., & Ostadabbas, S. (2018). An open-source feature extraction tool for the analysis of peripheral physiological data. IEEE Journal of Translational Engineering in Health and Medicine, 6, 1–11. https://doi.org/10.1109/JTEHM.2018.2878000 Nahum-Shani, I., Smith, S. N., Spring, B. J., Collins, L. M., Witkiewitz, K., Tewari, A., & Murphy, S. A. (2018). Just-in-time adaptive interventions (JITAIs) in mobile health: Key components and design principles for ongoing health behavior support. Annals of Behavioral Medicine, 52(6), 446–462. https://doi.org/10.1007/s12160-016-9830-8 Obrist, P. A., Webb, R. A., Sutterer, J. R., & Howard, J. L. (1970). The cardiac-somatic relationship: Some reformulations. Psychophysiology, 6(5), 569–587. https://doi.org/10.1111/j.1469-8986.1970.tb02246.x Rahman, M. M., Xu, X., Nathan, V., Ahmed, T., Ahmed, M. Y., McCaffrey, D., Kuang, J., Cowell, T., Moore, J., Mendes, W. B., & Gao, J. A. (2022). Detecting physiological responses using multimodal earbud sensors. 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 01–05. https://doi.org/10.1109/EMBC48229.2022.9871569 Rominger, C., & Schwerdtfeger, A. R. (2022). Feelings from the heart part II: Simulation and validation of static and dynamic HRV decrease-trigger algorithms to detect stress in firefighters. Sensors, 22(8), Article 8. https://doi.org/10.3390/s22082925 Schneider, S., Junghaenel, D. U., Smyth, J. M., Fred Wen, C. K., & Stone, A. A. (2023). Just-in-time adaptive ecological momentary assessment (JITA-EMA). Behavior Research Methods, 1–19. https://doi.org/10.3758/s13428-023-02083-8 Scholz, L., Ortiz Perez, A., Bierer, B., Eaksen, P., Wöllenstein, J., & Palzer, S. (2017). Miniature low-cost carbon dioxide sensor for mobile devices. IEEE Sensors Journal, 17(9), 2889–2895. https://doi.org/10.1109/JSEN.2017.2682638 Schwerdtfeger, A. R., & Rominger, C. (2021). Feelings from the heart: Developing HRV decrease-trigger algorithms via multilevel hyperplane simulation to detect psychosocially meaningful episodes in everyday life. Psychophysiology, 58(11), e13914. https://doi.org/10.1111/psyp.13914 Sennesh, E., Theriault, J., Brooks, D., van de Meent, J.-W., Barrett, L. F., & Quigley, K. S. (2022). Interoception as modeling, allostasis as control. Biological Psychology, 167, 108242. https://doi.org/10.1016/j.biopsycho.2021.108242 Shaffer, C., Westlin, C., Quigley, K. S., Whitfield-Gabrieli, S., & Barrett, L. F. (2022). Allostasis, action, and affect in depression: Insights from the theory of constructed emotion. Annual Review of Clinical Psychology, 18(1), 553–580. https://doi.org/10.1146/annurev-clinpsy-081219-115627 Siegel, E. H., Sands, M. K., Van den Noortgate, W., Condon, P., Chang, Y., Dy, J., Quigley, K. S., & Barrett, L. F. (2018). Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories. Psychological Bulletin, 144(4), 343–393. https://doi.org/10.1037/bul0000128 Snippe, E., Smit, A. C., Kuppens, P., Burger, H., & Ceulemans, E. (2023). Recurrence of depression can be foreseen by monitoring mental states with statistical process control. Journal of Psychopathology and Clinical Science, 132, 145–155. https://doi.org/10.1037/abn0000812 Sterling, P. (2012). Allostasis: A model of predictive regulation. Physiology and Behavior, 106(1), 5–15. https://doi.org/10.1016/j.physbeh.2011.06.004 Sterling, P., & Laughlin, S. (2015). Principles of neural design. MIT Press. Tian, Y. E., Di Biase, M. A., Mosley, P. E., Lupton, M. K., Xia, Y., Fripp, J., Breakspear, M., Cropley, V., & Zalesky, A. (2023). Evaluation of brain-body health in individuals with common neuropsychiatric disorders. JAMA Psychiatry. https://doi.org/10.1001/jamapsychiatry.2023.0791 Tsai, J. L., Levenson, R. W., & McCoy, K. (2006). Cultural and temperamental variation in emotional response. Emotion, 6(3), 484–497. https://doi.org/10.1037/1528-3542.6.3.484 Van Halem, S., Roekel, E., Kroencke, L., Kuper, N., & Denissen, J. (2020). Moments that matter? On the complexity of using triggers based on skin conductance to sample arousing events within an experience sampling framework. European Journal of Personality, 34(5), 794–807. https://doi.org/10.1002/per.2252 Wake, S., Wormwood, J., & Satpute, A. B. (2020). The influence of fear on risk taking: A meta-analysis. Cognition and Emotion, 34(6), 1143–1159. https://doi.org/10.1080/02699931.2020.1731428 Westlin, C., Theriault, J. E., Katsumi, Y., Nieto-Castanon, A., Kucyi, A., Ruf, S. F., Brown, S. M., Pavel, M., Erdogmus, D., Brooks, D. H., Quigley, K. S., Whitfield-Gabrieli, S., & Barrett, L. F. (2023). Improving the study of brain-behavior relationships by revisiting basic assumptions. Trends in Cognitive Sciences, 27(3), 246–257. https://doi.org/10.1016/j.tics.2022.12.015 Wilhelm, F. H., & Grossman, P. (2010). Emotions beyond the laboratory: Theoretical fundaments, study design, and analytic strategies for advanced ambulatory assessment. Biological Psychology, 84(3), 552–569. https://doi.org/10.1016/j.biopsycho.2010.01.017 Wilson-Mendenhall, C. D., Barrett, L. F., & Barsalou, L. W. (2015). Variety in emotional life: Within-category typicality of emotional experiences is associated with neural activity in large-scale brain networks. Social Cognitive and Affective Neuroscience, 10(1), 62–71. https://doi.org/10.1093/scan/nsu037 Wrzus, C., & Neubauer, A. B. (2022). Ecological momentary assessment: A meta-analysis on designs, samples, and compliance across research fields. Assessment, 30(3), 825–846. https://doi.org/10.1177/10731911211067538