(PS10-D90) Computational Modeling of the “mood Brightening Effect” in Depression and Anxiety

Background: Depressed patients exhibit larger reductions in negative affect following a positive event, relative to healthy individuals (Bylsma et al., 2011), an effect that is also seen in generalized anxiety disorder (Khazanov et al., 2019). Regression analyses can detect the presence of this “mood brightening” effect, but why it occurs remains puzzling. Recently, cognitive psychologists have developed Context Maintenance and Retrieval (CMR) models that offer a mechanistic explanation of how people process the emotional context of their surroundings (Cohen & Kahana, in press; Talmi, Lohnas, & Daw, 2019). These models suggest that heightened emotional context sensitivity, modeled as the more rapid integration of new emotional context, could explain the mood brightening effect. However, these models have not yet been tested in clinical samples or real-world settings. We applied CMR models to real-world data to generate a new mechanistic account of emotional processing in depression and anxiety.

Method: Using ecological momentary assessment, we assessed emotional responding to daily events in a clinical group with major depressive disorder and/or generalized anxiety disorder (n = 98) and healthy controls (n = 42). Participants were administered a clinical interview (ADIS-5) and assigned clinical severity ratings (0–8) for depression and anxiety. Then, for one week, participants were signaled nine times per day at 90-minute intervals. At each signal, participants first reported their current levels of positive and negative emotion. Then, they identified the most significant event experienced since the last signal and rated the emotional valence of the event on a scale from -5 (very negative) to 5 (very positive). 

Modeling: We applied two CMR models to these data. Model 1 generated a metric of global emotion context sensitivity (ECS), ranging 0.0–1.0, reflecting each participant’s rate of integrating new emotional context. Model 2 modeled ECS separately for negative and positive emotional context. Identifying the best-fitting model thus tested whether ECS in depression and anxiety is heightened for any emotional context, or differs for negative and positive emotional contexts. We then assessed whether heightened levels of CMR-modeled ECS correlated with depression and anxiety severity.  

           

Results: Model 2 outperformed Model 1, ΔBIC > 10. Negative ECS correlated positively with depression and anxiety severity (rs = .31-.34, ps < .001) and was higher in the clinical group than the control group (M = .28 vs. .11), t(138) = 4.03, p < .001. By contrast, positive ECS did not correlate with depression or anxiety severity (rs = .12-.15, ps > .148) and did not differ between groups, t(138) = 1.21, p = .229. 

Discussion: Depression and anxiety are associated with heightened sensitivity to negative emotional context, but not reduced sensitivity to positive emotional context. This may explain the mood brightening effect: positive events evoke more rapid (downward) updating in negative emotion for depressed or anxious individuals, but positive emotional context may accrue at a separate rate.  CMR equations suggest that behavioral interventions to alleviate negative mood may benefit from prioritizing events that maximally shift patients’ contexts.