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Updated: Oct 6, 2026

Psychophysiological Assessment of the Effectiveness of Emotion Regulation Strategies in Childhood
Published on: February 11, 2017
Emotional inertia is state-dependent: Capturing asymmetry in resistance to change
Stefan Schneider1, Doerte U Junghaenel1, Raymond Hernandez1
1Dornsife Center for Self-Report Science, University of Southern California.
Abstract:
Emotional inertia, the resistance to change in feelings, is a central construct in emotion dynamics. However, standard inertia models rely on a simplified assumption of symmetry: They assume that the temporal carryover of affect is identical regardless of whether an individual is feeling better or worse than usual. This masks potentially critical individual differences, as theories of adaptive affective functioning propose rapid recovery from worsened affective states alongside sustained persistence of desirable affective states as hallmarks of healthy affect regulation. To capture individual differences in this asymmetry, we introduce Tilt, a curvilinear autoregressive parameter that models inertia not as a fixed constant but as a slope that varies depending on the individual's momentary deviation from baseline. Applying a Bayesian multilevel model to daily diary data collected in 2018-2019 (N = 485; 25 days), we examined Tilt in positive affect and explored its association with neuroticism as an illustrative example. Replicating established findings, neuroticism predicted lower mean levels and greater variability in positive affect. Neuroticism was unrelated to standard linear inertia but was significantly associated with Tilt. While the average participant exhibited a convex Tilt (high persistence during peaks, rapid recovery from troughs), individuals high in neuroticism showed a concave Tilt, a "dampening" pattern where positive deviations decayed rapidly while drops below baseline became entrenched. These findings suggest that modeling the state dependence of affective persistence reveals individual differences in asymmetric affective dynamics that linear models miss. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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