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Emotional Inertia: On the Conservation of Emotional Momentum

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Affect Dynamics

Abstract

Emotional inertia refers to the tendency for emotions/affective states to be resistant to change over time. In this chapter, we review a burgeoning literature documenting how emotional inertia differs between individuals and correlates with individual differences in personality, well-being and psychopathology; how inertia is (causally) related to other psychological and biological processes; and how emotional inertia can itself change over time within individuals. We begin with a brief overview of the historical origins of emotional inertia, before outlining how inertia is operationalized statistically, and how it relates to other indices of affect dynamics. Next, we provide a selective review of empirical research on emotional inertia, focusing especially on studies published in the past several years. In light of the empirical evidence, we discuss the plausibility of several distal and proximal explanatory mechanisms underlying emotional inertia at biological/neural and psychological levels. Finally, we conclude with a discussion of open questions and future directions for research on emotional inertia.

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Notes

  1. 1.

    Although some scholars distinguish between “affect” and “emotion” (e.g., Gross, 2015), we use the terms interchangeably in line with common practice in studies on “emotional inertia” and the “affect/emotion dynamics” literature more broadly (e.g., Houben et al., 2015; Kuppens, 2015; Kuppens & Verduyn, 2017).

  2. 2.

    Although relatively rare in the context of emotion, it is possible to obtain negative autocorrelations (cf. Rovine & Walls, 2006), which indicate an oscillatory process wherein high levels of the outcome are preceded by low levels and vice versa. This tends to produce positive autocorrelations at lag 2 (i.e., the correlation among scores at t and t − 2; Box et al., 2008).

  3. 3.

    AR slopes can fall outside the −1 to +1 range, indicating a non-stationary process wherein short-term fluctuations do not revert to a stable equilibrium but accumulate over time producing trends (Box et al., 2008). This highlights the need to detect and potentially control for trends when modeling inertia.

  4. 4.

    The within-person part of the multilevel AR(1) model is shown in Eq. (4.2) and the between-person part in Eqs. (4.3)–(4.4). The lagged predictor emotiont − 1i is centered around each person i ‘s mean emotion (\( \overline{{\mathrm{emotion}}_i} \)). Although this is standard and ensures that all between-person variance is removed from the lagged predictor, this downwardly biases estimates of the AR slope (Hamaker & Grasman, 2015).

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  1. Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, VIC, Australia

    Peter Koval, Patrick T. Burnett & Yixia Zheng

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    Peter Kuppens

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Koval, P., Burnett, P.T., Zheng, Y. (2021). Emotional Inertia: On the Conservation of Emotional Momentum. In: Waugh, C.E., Kuppens, P. (eds) Affect Dynamics. Springer, Cham. https://doi.org/10.1007/978-3-030-82965-0_4

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