Nonlinearity in Affect Dynamics Persists After Accounting for the Valence of Daily-Life Events

In recent years, increased attention has gone to studying nonlinear characteristics of affective time series. An example of such nonlinear features is multimodality—the presence of more than one mode in an affective time series—which might mark the presence of discrete-like transitions between one and another affective state. In an attempt to capture these nonlinear features, Loossens et al. (2020) proposed the Affective Ising Model (AIM) as a model of affect dynamics. This modelwas validated on daily-life data, but these data did not contain any information on potential environmental factors that might have influenced a participant’s affective state. Unfortunately, this omission may have led to erroneously concluding that nonlinearity is a defining characteristic of the affective system, even when it is solely driven by extrinsic influences. To accommodate this limitation, we applied the AIM on daily-life data in which the valence of such external events was measured. Overall, we found that nonlinearity persisted after accounting for the valence of daily-life events, suggesting that nonlinearity is a defining characteristic of affect and should thus be accounted for. Interestingly, this effect was more pronounced for composite compared to single-item measures of affect. While in line with previous research, these results should be replicated in a larger, more representative sample.