Emotional Component of Time Concepts in Adults: Linguo-Psychological Approach

Abstract

Background. Time is a multifaceted and interdisciplinary category. A psychological level of analysis allows for a complementary consideration of time with concepts reflected in a person's specific semantic space. Considering the emotional component of the time concept allows for a glimpse into its subjectively significant aspects.

Objective. To identify the features of the emotional component of the adults' time concepts.

Study Participants. The study involved 760 respondents aged 18 to 70 years (205 men; 555 women).

Methods. A limited-choice associative experiment was used to collect data. The study implemented L.G. Babenko's linguo-psychological methodology in which the denotative-ideographic groups and functional-semantic categories serve as the units of analysis corresponding to the associated words included in the emotive thesaurus. Data processing utilized frequency analysis and rank calculations, identifying the core and peripheral levels of the emotional component of the time concept.

Results. The study allowed for identifying the emotional component in the semantic field of associations to the word "time" and analyze its characteristics. Denotative-ideographic groups of emotive vocabulary, reflecting adults' concepts of time, are represented unevenly. Of the entire set of denotative-ideographic groups, only three core groups are represented in the semantic field: love, joy, and grief. Functional-semantic categories are also represented unevenly: "emotional characterization" is represented to the greatest extent, while "external expression of emotions" and "person as the center and bearer of emotions" are represented to the least.

Conclusions. The emotional component of the time concept in adults is dichotomous, with the positive pole being more prominent. The positive pole contains emotions of love, joy, and happiness, while the negative pole contains grief and cruelty.

References

Alarcão, S.M., Fonseca, M.J. (2019). Emotions recognition using EEG signals: A survey. IEEE Transactions on Affective Computing, 10(3), 374–393. https://doi.org/10.1109/TAFFC.2017.2714671

Babenko, L.G. (2021). Alphabet of emotions. Ekaterinburg, Moscow: Armchair scholar Publ. (In Russ.)

Bota, P.J., Wang, C., Fred, A.L.N., Plácido da Silva, H.A. (2019). A review, current challenges, and future possibilities on emotion recognition using machine learning and physiological signals. IEEE Access, (7), 140990–141020. https://doi.org/10.1109/ACCESS.2019.2944001

Carretié, L., Tapia, M., López-Martín, S., Albert, J. (2019). EmoMadrid: An emotional pictures database for affect research. Motivation and Emotion, 43(6), 929–939. https://doi.org/10.1007/s11031-019-09780-y

Dodds, P.S., Harris, K.D., Kloumann, I.M., Bliss, C.A., Danforth, C.M. (2011). Temporal patterns of happiness and information in a global social network: Hedonometrics and Twitter. PLoS ONE, 6(12), e26752. https://doi.org/10.1371/journal.pone.0026752

Dorzhieva, G.S., Shcherbakova, A.A. (2019). Psycholinguistic reasons for the nomination of the winds. Lomonosov Linguistics and Intercultural Communication Journal, (4), 122–132. (In Russ.)

Ferrara, E., Yang, Z. (2015). Quantifying the effect of sentiment on information diffusion in social media. PeerJ Computer Science, (1), e26. https://doi.org/10.7717/peerj-cs.26

Gerdes, A.B.M., Wieser, M.J., Alpers, G.W. (2014). Emotional pictures and sounds: a review of multimodal interactions of emotion cues in multiple domains. Frontiers in Psychology, (5), 1351. https://doi.org/10.3389/fpsyg.2014.01351

Guedes, D., Prada, M., Garrido, M.V., Lamy, E. (2023). The taste and affect music database: Subjective rating norms for a new set of musical stimuli. Behavior Research Methods, 55(3), 1121–1140. https://doi.org/10.3758/s13428-022-01862-z

Haberkamp, A., Glombiewski, J.A., Schmidt, F., Barke, A. (2017). The DIsgust-RelaTed-Images (DIRTI) database: Validation of a novel standardized set of disgust pictures. Behaviour Research and Therapy, (89), 86–94. https://doi.org/10.1016/j.brat.2016.11.010

Harmon-Jones, E. (2006). Unilateral right-hand contractions cause contralateral alpha power suppression and approach motivational affective experience. Psychophysiology, (43), 598–603. https://doi.org/10.1111/j.1469-8986.2006.00465.x

Izard, C.E. (1991). The psychology of emotions. New York: PlenumPress.

Jacobs, G.D., Snyder, D. (1996). Frontal brain asymmetry predicts affective style in men. Behavioral Neuroscience, (110), 3–6. https://doi.org/10.1037//0735-7044.110.1.3

Jiang, Y., Li, W., Hossain, M.S., Chen, M., Alelaiwi, A., Al-Hammadi, M. (2020). A snapshot research and implementation of multimodal information fusion for data-driven emotion recognition. Information Fusion, (53), 209–221. https://doi.org/10.1016/j.inffus.2019.06.019

Johnson, K.T., Narain, J., Quatieri, T., Maes, P., Picard, R.W. (2023). ReCANVo: A database of real-world communicative and affective nonverbal vocalizations. Scientific Data, (10), 523. https://doi.org/10.1038/s41597-023-02405-7

Kamantseva, O. (2021). Emotive vocabulary from the perspective of an interdisciplinary approach. Vestnik filologicheskikh nauk = Bulletin of Philological Sciences, 1(4), 19–26. (In Russ.)

Kapucu, A., Kılıç, A., Özkılıç, Y., Sarıbaz, B. (2021). Turkish emotional word norms for arousal, valence, and discrete emotion categories. Psychological Reports, 124(1), 188–209. https://doi.org/10.1177/0033294118814722

Karpov, A.V. (2023). Psyche and time: in 2 vol. Vol. I. History. Methodology. Structure. Yaroslavl: Filigran' Publ. (In Russ.)

Kensinger, E.A., Schacter, D.L. (2006). Processing emotional pictures and words: Effects of valence and arousal. Cognitive, Affective, and Behavioral Neuroscience, (6), 110–126. https://doi.org/10.3758/CABN.6.2.110

Kleinberg, B., van der Vegt, I., Mozes, M. (2020). Measuring emotions in the COVID-19 Real World Worry Dataset. https://doi.org/10.48550/arXiv.2004.04225

Melehin, A.I., Kireeva, Z.A. (2016). The specificity of the cognitive representations of time in elderly. Klinicheskaya i spetsial'naya psikhologiya = Clinical Psychology and Special Education, 5(3), 95–115. (In Russ.). https://doi.org/10.17759/cpse.2016050307

Patsakula, I.I., Zaychikova, I.V., Gudovskaya, O.S. (2015). Psychological analysis beliefs about subjective time personality. Sovremennye problemy nauki i obrazovaniya = Modern Problems of Science and Education, (2–2). (In Russ.). URL: https://science-education.ru/ru/article/view?id=22826 (accessed: 07.02.2026).

Pérez-Sánchez, M.Á., Stadthagen-Gonzalez, H., Guasch, M., Hinojosa, J.A., Fraga, I., Marín, J., Ferré, P. (2021). EmoPro — Emotional prototypicality for 1286 Spanish words: Relationships with affective and psycholinguistic variables. Behavior Research Methods, (53), 1857–1875. https://doi.org/10.3758/s13428-020-01519-9

Petrenko, V.F., Mitina, O.V., Suprun, A.P. (2021). Conscious and unconscious cognition in psychosemantics. Psikhologiya. Zhurnal Vysshei shkoly ehkonomiki = Psychology. Journal of the Higher School of Economics, 18(4), 930–943. (In Russ.). https://doi.org/10.17323/1813-8918-2021-4-930-943

Stella, M., Restocchi, V., De Deyne, S. (2020). Lockdown: network-enhanced emotional profiling in the time of COVID-19. Big Data and Cognitive Computing, 4(2), 14. https://doi.org/10.3390/bdcc4020014

Tomarken, A.J., Davidson, R.J., Wheeler, R.E., Doss, R. (1992). Individual differences in anterior brain asymmetry and fundamental dimensions of emotion. Journal of Personality and Social Psychology, 62(4), 676–687. https://doi.org/10.1037/0022-3514.62.4.676

Vasileva, I.V., Chumakov, M.V. (2022). Students' perceptions of volunteering. Yaroslavskii pedagogicheskii vestnik = Yaroslavl Pedagogical Bulletin, (5), 161–169. (In Russ.). https://doi.org/10.20323/1813-145X-2022-5-128-161-169

Vasileva, I.V., Chumakov, M.V. (2023). Emotional component of the image of volunteering in the perceptions of students in psychology and education. Lomonosov Psychology Journal, 46(4), 248–271. (In Russ.). https://doi.org/10.11621/LPJ-23-47

Vasileva, I.V., Chumakov, M.V. (2024). University students' perceptions regarding the "Physical education" training course. Chelovek. Sport. Meditsina = Human. Sport. Medicine, 24(2), 118–124. (In Russ.). https://doi.org/10.14529/hsm240215

Vasileva, I.V., Chumakov, M.V., Chumakova, D.M. (2023). Students' perceptions of well-being. Ehkologiya cheloveka = Human Ecology, 30(12), 909–918. (In Russ.). https://doi.org/10.17816/humeco627147

Wang, Y., Song, W., Tao, W., Liotta, A., Yang, D., Li, X., Gao, S., Sun, Y., Ge, W., Zhang, W., Zhang, W. (2022). A systematic review on affective computing: emotion models, databases, and recent advances. Information Fusion, (83–84), 19–52. https://doi.org/10.1016/j.inffus.2022.03.009

Xu, X., Li, J., Chen, H. (2022). Valence and arousal ratings for 11,310 simplified Chinese words. Behavior Research Methods, (54), 26–41. https://doi.org/10.3758/s13428-021-01607-4

Zhao, S., Yao, X., Yang, J., Jia, G., Ding, G., Chua, T-S., Schuller, B.W., Keutzer, K. (2022). Affective image content analysis: Two decades review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10), 6729–6751. https://doi.org/10.1109/TPAMI.2021.3094362