Abstract
Affective experiences and related cognitive and motivational processes unfold within individuals over time. Vital information is inherently embedded in the time scale, shape, and context of affective processes’ temporal dynamics. Thus, time itself may serve as a useful proxy for various underlying causal processes that researchers can identify and model. Considering the role(s) of time in theoretical conceptualizations and including time-derived variables in statistical models is likely to significantly improve the understanding of affect dynamics and their place among other dynamic processes. In this chapter, we delineate three sets of factors to be addressed in the study of affect-related temporal dynamics: The first set concerns the time scale in which the target system’s core processes unfold. The second set concerns the shape of temporal (co)variation within the target system—that is, the trends, cycles, and discrete phenomena involved. The third set concerns the sources of within-individual variation in the target system across time and context. Although many of these themes have already been spelled out in the affect dynamics literature, their incorporation into research remains limited. Facing recent concerns regarding the robustness of affect dynamics findings and renewed interest in psychological theory development, thorough consideration of temporal dynamics becomes crucial.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adam, E. K., Quinn, M. E., Tavernier, R., McQuillan, M. T., Dahlke, K. A., & Gilbert, K. E. (2017). Diurnal cortisol slopes and mental and physical health outcomes: A systematic review and meta-analysis. Psychoneuroendocrinology, 83, 25–41. https://doi.org/10.1016/j.psyneuen.2017.05.018
Adolf, J. K., Loossens, T., Tuerlinckx, F., & Ceulemans, E. (2021). Optimal sampling rates for reliable continuous-time first-order autoregressive and vector autoregressive modeling. Psychological Methods.
Albers, C. J., & Bringmann, L. F. (2020). Inspecting gradual and abrupt changes in emotion dynamics with the time-varying change point autoregressive model. European Journal of Psychological Assessment.https://doi.org/10.1027/1015-5759/a000589
Beal, D. J., & Ghandour, L. (2011). Stability, change, and the stability of change in daily workplace affect. Journal of Organizational Behavior, 32, 526–546. https://doi.org/10.1002/job.713
Boker, S. M., Molenaar, P., & Nesselroade, J. R. (2009). Issues in intraindividual variability: Individual differences in equilibria and dynamics over multiple time scales. Psychology and Aging, 24, 858–862. https://doi.org/10.1037/a0017912
Borsboom, D., van der Maas, H. L., Dalege, J., Kievit, R. A., & Haig, B. D. (2021). Theory construction methodology: A practical framework for building theories in psychology. Perspectives on Psychological Science. https://doi.org/10.1177/1745691620969647
Bos, E. H., de Jonge, P., & Cox, R. F. (2019). Affective variability in depression: Revisiting the inertia–instability paradox. British Journal of Psychology, 110, 814–827. https://doi.org/10.1111/bjop.12372
Bringmann, L. F., Ferrer, E., Hamaker, E. L., Borsboom, D., & Tuerlinckx, F. (2018). Modeling nonstationary emotion dynamics in dyads using a time-varying vector-autoregressive model. Multivariate Behavioral Research, 53, 293–314. https://doi.org/10.1080/00273171.2018.1439722
Bringmann, L. F., Hamaker, E. L., Vigo, D. E., Aubert, A., Borsboom, D., & Tuerlinckx, F. (2017). Changing dynamics: Time-varying autoregressive models using generalized additive modeling. Psychological Methods, 22, 409–425. https://doi.org/10.1037/met0000085
Brose, A., Schmiedek, F., Koval, P., & Kuppens, P. (2015). Emotional inertia contributes to depressive symptoms beyond perseverative thinking. Cognition and Emotion, 29, 527–538. https://doi.org/10.1080/02699931.2014.916252
Butler, E. A. (2011). Temporal interpersonal emotion systems: The “TIES” that form relationships. Personality and Social Psychology Review, 15, 367–393. https://doi.org/10.1177/1088868311411164
Butler, E. A. (2015). Interpersonal affect dynamics: It takes two (and time) to tango. Emotion Review, 7, 336–341. https://doi.org/10.1177/1754073915590622
Butler, E. A., & Randall, A. K. (2013). Emotional coregulation in close relationships. Emotion Review, 5, 202–210. https://doi.org/10.1177/1754073912451630
Cabrieto, J., Tuerlinckx, F., Kuppens, P., Hunyadi, B., & Ceulemans, E. (2018). Testing for the presence of correlation changes in a multivariate time series: A permutation based approach. Scientific Reports, 8, 1–20. https://doi.org/10.1038/s41598-017-19067-2
Clark, L. A., Watson, D., & Leeka, J. (1989). Diurnal variation in the positive affects. Motivation and Emotion, 13, 205–234. https://doi.org/10.1007/BF00995536
Dejonckheere, E., Mestdagh, M., Houben, M., Rutten, I., Sels, L., Kuppens, P., & Tuerlinckx, F. (2019). Complex affect dynamics add limited information to the prediction of psychological well-being. Nature Human Behaviour, 3, 478–491. https://doi.org/10.1038/s41562-019-0555-0
Dejonckheere, E., Mestdagh, M., Kuppens, P., & Tuerlinckx, F. (2020). Reply to: Context matters for affective chronometry. Nature Human Behaviour, 1–4. https://doi.org/10.1038/s41562-020-0860-7
Dejonckheere, E., Mestdagh, M., Verdonck, S., Lafit, G., Ceulemans, E., Bastian, B., & Kalokerinos, E. K. (2021). The relation between positive and negative affect becomes more negative in response to personally relevant events. Emotion, 21, 326–336. https://doi.org/10.1037/emo0000697
Dormann, C., & Griffin, M. A. (2015). Optimal time lags in panel studies. Psychological Methods, 20, 489–505. https://doi.org/10.1037/met0000041
Ebner-Priemer, U. W., & Sawitzki, G. (2007). Ambulatory assessment of affective instability in borderline personality disorder: The effect of the sampling frequency. European Journal of Psychological Assessment, 23, 238–247. https://doi.org/10.1027/1015-5759.23.4.238
Ebner-Priemer, U. W., Kuo, J., Kleindienst, N., Welch, S. S., Reisch, T., Reinhard, I., Lieb, K., Linehan, M. M., & Bohus, M. (2007). State affective instability in borderline personality disorder assessed by ambulatory monitoring. Psychological Medicine, 37(7), 961–970. https://doi.org/10.1017/S0033291706009706
Eisele, G., Vachon, H., Lafit, G., Kuppens, P., Houben, M., Myin-Germeys, I., & Viechtbauer, W. (2020). The effects of sampling frequency and questionnaire length on perceived burden, compliance, and careless responding in experience sampling data in a student population. Assessment. https://doi.org/10.1177/1073191120957102
Erbas, Y., Ceulemans, E., Lee Pe, M., Koval, P., & Kuppens, P. (2014). Negative emotion differentiation: Its personality and well-being correlates and a comparison of different assessment methods. Cognition and Emotion, 28, 1196–1213. https://doi.org/10.1080/02699931.2013.875890
Erbas, Y., Ceulemans, E., Kalokerinos, E. K., Houben, M., Koval, P., Pe, M. L., & Kuppens, P. (2018). Why I don’t always know what I’m feeling: The role of stress in within-person fluctuations in emotion differentiation. Journal of Personality and Social Psychology, 115, 179–191. https://doi.org/10.1037/pspa0000126
Erbas, Y., Kalokerinos, E. K., Kuppens, P., van Halem, S., & Ceulemans, E. (2021). Momentary emotion differentiation: The derivation and validation of an index to study within-person fluctuations in emotion differentiation. Assessment. https://doi.org/10.1177/1073191121990089
Eronen, M. I., & Bringmann, L. F. (2021). The theory crisis in psychology: How to move forward. Perspectives on Psychological Science.https://doi.org/10.1177/1745691620970586
Falkenström, F., Finkel, S., Sandell, R., Rubel, J. A., & Holmqvist, R. (2017). Dynamic models of individual change in psychotherapy process research. Journal of Consulting and Clinical Psychology, 85, 537–549. https://doi.org/10.1037/ccp0000203
Farage, M. A., Osborn, T. W., & MacLean, A. B. (2008). Cognitive, sensory, and emotional changes associated with the menstrual cycle: A review. Archives of Gynecology and Obstetrics, 278, 299–307. https://doi.org/10.1007/s00404-008-0708-2
Fisher, A. J. (2015). Toward a dynamic model of psychological assessment: Implications for personalized care. Journal of Consulting and Clinical Psychology, 83, 825–836. https://doi.org/10.1037/ccp0000026
Fisher, A. J., & Newman, M. G. (2016). Reductions in the diurnal rigidity of anxiety predict treatment outcome in cognitive behavioral therapy for generalized anxiety disorder. Behaviour Research and Therapy, 79, 46–55. https://doi.org/10.1016/j.brat.2016.02.006
Fisher, A. J., Bosley, H. G., Fernandez, K. C., Reeves, J. W., Soyster, P. D., Diamond, A. E., & Barkin, J. (2019). Open trial of a personalized modular treatment for mood and anxiety. Behaviour Research and Therapy, 116, 69–79. https://doi.org/10.1016/j.brat.2019.01.010
Fisher, A. J., & Bosley, H. G. (2020). Identifying the presence and timing of discrete mood states prior to therapy. Behaviour Research and Therapy, 128, 103596. https://doi.org/10.1016/j.brat.2020.103596
Fisher, A. J., Medaglia, J. D., & Jeronimus, B. F. (2018). Lack of group-to-individual generalizability is a threat to human subjects research. Proceedings of the National Academy of Sciences, 115, E6106–E6115. https://doi.org/10.1073/pnas.1711978115
Fried, E. I. (2020). Lack of theory building and testing impedes progress in the factor and network literature. Psychological Inquiry, 31, 271–288. https://doi.org/10.1080/1047840X.2020.1853461
Fisher, A. J., Reeves, J. W., Lawyer, G., Medaglia, J. D., & Rubel, J. A. (2017). Exploring the idiographic dynamics of mood and anxiety via network analysis. Journal of Abnormal Psychology, 126, 1044–1056. https://doi.org/10.1037/abn0000311
Galili-Weinstock, L., Lazarus, G., Atzil-Slonim, D., Bar-Kalifa, E., Rafaeli, E., & Peri, T. (2020). Self-compassion among psychotherapy clients is in the details of negative, not positive, emotions. The Journal of Positive Psychology, 1–10. https://doi.org/10.1080/17439760.2019.1627396
Golder, S. A., & Macy, M. W. (2011). Diurnal and seasonal mood vary with work, sleep, and daylength across diverse cultures. Science, 333, 1878–1881. https://doi.org/10.1126/science.1202775
Hamaker, E. L., & Wichers, M. (2017). No time like the present: Discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science, 26, 10–15. https://doi.org/10.1177/0963721416666518
Haslbeck, J. M. B., Ryan, O., Robinaugh, D., Waldorp, L., & Borsboom, D. (2019, December 10). Modeling psychopathology: From data models to formal theories. https://doi.org/10.31234/osf.io/jgm7f.
Hengartner, M. P., Kruger, T. H., Geraedts, K., Tronci, E., Mancini, T., Ille, F., et al. (2017). Negative affect is unrelated to fluctuations in hormone levels across the menstrual cycle: Evidence from a multisite observational study across two successive cycles. Journal of Psychosomatic Research, 99, 21–27. https://doi.org/10.1016/j.jpsychores.2017.05.018
Hollenstein, T. (2015). This time, it’s real: Affective flexibility, time scales, feedback loops, and the regulation of emotion. Emotion Review, 7, 308–315. https://doi.org/10.1177/1754073915590621
Houben, M., Mestdagh, M., Dejonckheere, E., Obbels, J., Sienaert, P., van Roy, J., & Kuppens, P. (2020). The statistical specificity of emotion dynamics in borderline personality disorder. Journal of Personality Disorders.https://doi.org/10.1177/1754073915590621
Houben, M., Van Den Noortgate, W., & Kuppens, P. (2015). The relation between short-term emotion dynamics and psychological well-being: A meta-analysis. Psychological Bulletin, 141, 901–930. https://doi.org/10.1037/a0038822
Jacobson, N. C., Chow, S. M., & Newman, M. G. (2019). The Differential Time-Varying Effect Model (DTVEM): A tool for diagnosing and modeling time lags in intensive longitudinal data. Behavior Research Methods, 51, 295–315. https://doi.org/10.3758/s13428-018-1101-0
Jebb, A. T., Tay, L., Wang, W., & Huang, Q. (2015). Time series analysis for psychological research: Examining and forecasting change. Frontiers in Psychology, 6, 727. https://doi.org/10.3389/fpsyg.2015.00727
Kalokerinos, E. K., Résibois, M., Verduyn, P., & Kuppens, P. (2017). The temporal deployment of emotion regulation strategies during negative emotional episodes. Emotion, 17, 450–458. https://doi.org/10.1037/emo0000248
Koval, P., & Kuppens, P. (2012). Changing emotion dynamics: Individual differences in the effect of anticipatory social stress on emotional inertia. Emotion, 12, 256–267. https://doi.org/10.1037/a0024756
Koval, P., Pe, M. L., Meers, K., & Kuppens, P. (2013). Affect dynamics in relation to depressive symptoms: Variable, unstable or inert? Emotion, 13, 1132. https://doi.org/10.1037/a0033579
Kuppens, P. (2015). It’s about time: A special section on affect dynamics. Emotion Review, 7, 297–300. https://doi.org/10.1177/1754073915590947
Kuppens, P., Allen, N. B., & Sheeber, L. B. (2010a). Emotional inertia and psychological maladjustment. Psychological Science, 21, 984–991. https://doi.org/10.1177/0956797610372634
Kuppens, P., Oravecz, Z., & Tuerlinckx, F. (2010b). Feelings change: Accounting for individual differences in the temporal dynamics of affect. Journal of Personality and Social Psychology, 99, 1042–1060. https://doi.org/10.1037/a0020962
Lapate, R. C., & Heller, A. S. (2020). Context matters for affective chronometry. Nature Human Behaviour, 4, 688–689. https://doi.org/10.1038/s41562-020-0860-7
Larsen, R. J. (1987). The stability of mood variability: A spectral analytic approach to daily mood assessments. Journal of Personality and Social Psychology, 52, 1195–1204.
Lazarus, G., Atzil-Slonim, D., Bar-Kalifa, E., Hasson-Ohayon, I., & Rafaeli, E. (2019). Clients’ emotional instability and therapists’ inferential flexibility predict therapists’ session-by-session empathic accuracy. Journal of Counseling Psychology, 66, 56–69. https://doi.org/10.1037/cou0000310
Liu, Y., & West, S. G. (2016). Weekly cycles in daily report data: An overlooked issue. Journal of Personality, 84, 560–579. https://doi.org/10.1111/jopy.12182
Mneimne, M., Fleeson, W., Arnold, E. M., & Furr, R. M. (2018). Differentiating the everyday emotion dynamics of borderline personality disorder from major depressive disorder and bipolar disorder. Personality Disorders, 9, 192–196. https://doi.org/10.1037/per0000255
Molenaar, P. C. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement, 2, 201–218. https://doi.org/10.1207/s15366359mea0204_1
Molenaar, P. C., & Campbell, C. G. (2009). The new person-specific paradigm in psychology. Current Directions in Psychological Science, 18, 112–117. https://doi.org/10.1111/j.1467-8721.2009.01619.x
Murray, G., Nicholas, C. L., Kleiman, J., Dwyer, R., Carrington, M. J., Allen, N. B., & Trinder, J. (2009). Nature’s clocks and human mood: The circadian system modulates reward motivation. Emotion, 9, 705–716. https://doi.org/10.1037/a0017080
Neubauer, A. B., & Schmiedek, F. (2020). Studying within-person variation and within-person couplings in intensive longitudinal data: Lessons learned and to be learned. Gerontology, 66, 332–339. https://doi.org/10.1159/000507993
Pascual-Leone, A., & Greenberg, L. S. (2007). Emotional processing in experiential therapy: Why “the Only Way Out Is Through”. Journal of Consulting and Clinical Psychology, 75, 875–887. https://doi.org/10.1037/0022-006X.75.6.875
Piccirillo, M. L., & Rodebaugh, T. L. (2019). Foundations of idiographic methods in psychology and applications for psychotherapy. Clinical Psychology Review, 71, 90–100. https://doi.org/10.1016/j.cpr.2019.01.002
Ram, N., Chow, S.-M., Bowles, R. P., Wang, L., Grimm, K., Fujita, F., & Nesselroade, J. R. (2005). Examining interindividual differences in cyclicity of pleasant and unpleasant affects using spectral analysis and item response modeling. Psychometrika, 70, 773–790. https://doi.org/10.1007/s11336-001-1270-5
Santangelo, P. S., Limberger, M. F., Stiglmayr, C., Houben, M., Coosemans, J., Verleysen, G., Kuppens, P., Tuerlinckx, F., Vanpaemel, W., & Ebner-Priemer, U. W. (2016). Analyzing subcomponents of affective dysregulation in borderline personality disorder in comparison to other clinical groups using multiple e-diary datasets. Borderline Personality Disorder and Emotion Dysregulation, 3, 1–13. https://doi.org/10.1186/s40479-016-0039-z
Scargle, J. D. (1982). Studies in astronomical time series analysis. II-Statistical aspects of spectral analysis of unevenly spaced data. The Astrophysical Journal, 263, 835e853.
Song, J., Howe, E., Oltmanns, J. R., & Fisher, A. J. (2021). Examining the predictive validity of single items in ecological momentary assessments. Department of Psychology, University of California, Berkeley.
Thompson, R. J., Mata, J., Jaeggi, S. M., Buschkuehl, M., Jonides, J., & Gotlib, I. H. (2012). The everyday emotional experience of adults with major depressive disorder: Examining emotional instability, inertia, and reactivity. Journal of Abnormal Psychology, 121, 819–829. https://doi.org/10.1037/a0027978
Trull, T. J., & Ebner-Priemer, U. W. (2020). Ambulatory assessment in psychopathology research: A review of recommended reporting guidelines and current practices. Journal of Abnormal Psychology, 129, 56–63. https://doi.org/10.1037/abn0000473
Trull, T. J., Lane, S. P., Koval, P., & Ebner-Priemer, U. W. (2015). Affective dynamics in psychopathology. Emotion Review, 7(4), 355–361. https://doi.org/10.1177/1754073915590617
van de Maat, R., Lataster, J., & Verboon, P. (2020). Why and how to deal with diurnal cyclic patterns in ambulatory assessment of emotions. European Journal of Psychological Assessment.https://doi.org/10.1027/1015-5759/a000579
van der Gucht, K., Dejonckheere, E., Erbas, Y., Takano, K., Vandemoortele, M., Maex, E., Raes, F., & Kuppens, P. (2019). An experience sampling study examining the potential impact of a mindfulness-based intervention on emotion differentiation. Emotion, 19, 123–131. https://doi.org/10.1037/emo0000406
Verduyn, P., Delvaux, E., Van Coillie, H., Tuerlinckx, F., & Van Mechelen, I. (2009). Predicting the duration of emotional experience: Two experience sampling studies. Emotion, 9, 83–91. https://doi.org/10.1037/a0014610
Wang, L. P., & Maxwell, S. E. (2015). On disaggregating between-person and within-person effects with longitudinal data using multilevel models. Psychological Methods, 20, 63–83. https://doi.org/10.1037/met0000030
Wehr, T. A. (2018). Bipolar mood cycles and lunar tidal cycles. Molecular Psychiatry, 23, 923–931. https://doi.org/10.1038/mp.2016.263
Wendt, L. P., Wright, A. G., Pilkonis, P. A., Woods, W. C., Denissen, J. J., Kühnel, A., & Zimmermann, J. (2020). Indicators of affect dynamics: Structure, reliability, and personality correlates. European Journal of Personality, 34, 1060–1072. https://doi.org/10.1002/per.2277
Wright, A. G. C., & Zimmermann, J. (2019). Applied ambulatory assessment: Integrating idiographic and nomothetic principles of measurement. Psychological Assessment, 31, 1467–1480. https://doi.org/10.1037/pas0000685
Wright, A. G., & Woods, W. C. (2020). Personalized models of psychopathology. Annual Review of Clinical Psychology, 16, 49–74. https://doi.org/10.1146/annurev-clinpsy-102419-125032
Yarkoni, T. (2020). The generalizability crisis. Behavioral and Brain Sciences, 1–37. https://doi.org/10.1017/S0140525X20001685
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Lazarus, G., Song, J., Crawford, C.M., Fisher, A.J. (2021). A Close Look at the Role of Time in Affect Dynamics Research. In: Waugh, C.E., Kuppens, P. (eds) Affect Dynamics. Springer, Cham. https://doi.org/10.1007/978-3-030-82965-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-82965-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-82964-3
Online ISBN: 978-3-030-82965-0
eBook Packages: Behavioral Science and PsychologyBehavioral Science and Psychology (R0)