What ugly and beautiful photographs reveal about COVID-19 lockdown experiences, everyday aesthetics and photography aesthetics.

Against the backdrop of the COVID-19 pandemic, which restricted our daily (visual) experiences, we asked people to take an ugly and beautiful photograph from within their homes. In total, 284 photographs (142 ugly and 142 beautiful) and accompanying statements were submitted and brought to light an intimate portrait of how participants were experiencing their (lockdown) home environment. Results revealed an aesthetic preference for (living) nature. Beauty and ugliness were also connected to good versus bad views, mess versus cosiness, unflattering versus flattering portraits and positive versus negative (COVID-19) emotions. In terms of photography strategies, editing and colour were important for beautiful photographs, whereas a lack of effort and sharpness showed up relatively more in ugly photographs. A follow-up study revealed that other viewers' (n = 86) aesthetic judgements of the photographs were largely in line with the original submissions, and confirmed several of the themes. Overall, our study provides a unique photographic window on our everyday aesthetic experiences at home during the COVID-19 lockdown.

Assessing Temporal Emotion Dynamics Using Networks.

Multivariate psychological processes have recently been studied, visualized, and analyzed as networks. In this network approach, psychological constructs are represented as complex systems of interacting components. In addition to insightful visualization of dynamics, a network perspective leads to a new way of thinking about the nature of psychological phenomena by offering new tools for studying dynamical processes in psychology. In this article, we explain the rationale of the network approach, the associated methods and visualization, and illustrate it using an empirical example focusing on the relation between the daily fluctuations of emotions and neuroticism. The results suggest that individuals with high levels of neuroticism had a denser emotion network compared with their less neurotic peers. This effect is especially pronounced for the negative emotion network, which is in line with previous studies that found a denser network in depressed subjects than in healthy subjects. In sum, we show how the network approach may offer new tools for studying dynamical processes in psychology.

A network approach to psychopathology: new insights into clinical longitudinal data.

In the network approach to psychopathology, disorders are conceptualized as networks of mutually interacting symptoms (e.g., depressed mood) and transdiagnostic factors (e.g., rumination). This suggests that it is necessary to study how symptoms dynamically interact over time in a network architecture. In the present paper, we show how such an architecture can be constructed on the basis of time-series data obtained through Experience Sampling Methodology (ESM). The proposed methodology determines the parameters for the interaction between nodes in the network by estimating a multilevel vector autoregression (VAR) model on the data. The methodology allows combining between-subject and within-subject information in a multilevel framework. The resulting network architecture can subsequently be analyzed through network analysis techniques. In the present study, we apply the method to a set of items that assess mood-related factors. We show that the analysis generates a plausible and replicable network architecture, the structure of which is related to variables such as neuroticism; that is, for subjects who score high on neuroticism, worrying plays a more central role in the network. Implications and extensions of the methodology are discussed.