2026/03/11 by Aleksandra Piejka, Antonin Fourcade, Michael Gaebler +1 · 1 voice
Arts and Humanities · Neuroscience · Psychology · #Affect (linguistics) #Bivariate analysis #Depressive symptoms #Experience sampling method #Functional Brain Connectivity Studies #Loneliness #Mental Health Research Topics #Mental Health and Psychiatry #Operationalization #Rumination #Sadness
paper · doi:10.31234/osf.io/ke9gb_v1
openalex publication_date 2026/03/11 · openalex created_date 2026/03/13 · openalex updated_date 2026/07/14
Introduction. The dynamics of daily emotional experience are closely linked to mental health, and multiple facets of emotional complexity have been proposed to capture clinically relevant variation. However, established metrics measure fluctuation amplitude (variability), temporal unpredictability (instability), and discriminative precision (granularity) but not how restricted one's emotional repertoire is, a key symptom in many psychopathologies. Here we propose emotional bandwidth, a novel metric which considers emotions as multidimensional states rather than labels or averages, and quantifies how many different states a person experiences over time. Given established links between emotion and depression, we investigated whether this association extends to emotional bandwidth and whether emotional complexity metrics relate to broader transdiagnostic risk factors, such as loneliness.Methods. 176 participants from a non-clinical sample completed an experience sampling study including repeated momentary ratings of positive and negative affect (six items each). For both affect categories, we extracted granularity, instability, variability, and a novel metric of emotional bandwidth derived from information theory (Shannon entropy) and operationalized as the fraction of the possible emotional configurations effectively covered. General levels of depressive symptoms and loneliness were assessed using standardized questionnaires. Results. The negative, but not positive affect metrics demonstrated significant bivariate associations with depressive symptoms and loneliness. When all negative emotional complexity metrics were entered into a regression model, granularity, instability, and variability showed no unique contributions to either outcome. Only emotional bandwidth remained significantly associated with depressive symptoms, even after controlling for mean negative affect. However, bandwidth did not directly predict loneliness once mean affect was accounted for. Mediation analysis revealed that emotional bandwidth's relationship with loneliness operated indirectly through depressive symptoms, with a non-significant direct path.Discussion. Emotional bandwidth for negative affect emerged as the only emotional complexity metric uniquely associated with psychosocial outcomes. Our findings suggest that the diversity of emotional states one’s experience could hold clinical significance beyond commonly studied constructs such as moment-to-moment fluctuations in averaged affect or the ability to label emotions precisely.