This is the first study of which we are aware to show a relationship between mood symptoms and affective forecasting problems. Findings support what we call a dysphoric forecasting bias
– the tendency of individuals in dysphoric states to overpredict negative emotional reactions to future events. The dysphoric forecasting bias was robust across ratings of positive and negative affect, forecasts for pleasant and unpleasant scenarios, continuous and categorical operationalizations of dysphoria, and three time points of observation. Furthermore, dysphoric symptoms were independently associated with biased forecasts in analyses controlling for anxiety symptoms and lifetime hypomanic symptoms. In order to elucidate the dysphoric forecasting bias, we built upon the strengths and limitations of prior studies (Werhan, 2009
; Yuan & Kring, 2009) and used theory to guide hypotheses (Allan et al., 2007
; Alloy & Abramson, 1988
; Beck, 1976
Our findings suggest that cognitive theory (Beck, 1976
) better accounted for the dysphoric forecasting bias than did depressive realism theory (Allan et al., 2007
; Alloy & Abramson, 1988
). This reinforces therapeutic efforts to address cognitive-emotional distortions in depression, and raises the question of whether bias-reduction strategies that have shown promise in basic affective forecasting research (e.g., Hoerger et al., 2009
) might also have practical benefit for treating aspects of clinical depression. Given that depressive realism theory has garnered support in other research contexts, particularly those involving self-evaluation and risk perception (B. D. Dunn et al., 2007
), the theory’s explanatory power may be greater for task domains involving rational judgments than those involving intuitive emotional processes, such as affective forecasting. Finally, our findings could differ from past investigations of affective forecasting in dysphoria (Werhan, 2009
; Yuan & Kring, 2009) due to methodological differences, including the emotional events, sample characteristics, or other moderators.
Findings also contribute more broadly to our understanding of dysphoria in affective forecasting. Consistent with prior research (E. W. Dunn & Laham, 2006
; Gilbert et al., 1998
; Hoerger et al., 2009
; Hoerger & Quirk, 2010
), participants were found to overpredict positive emotional reactions to a future pleasant event (having a date) and overpredict negative reactions to an unpleasant event (not having a date). However, these normative biases are not universal, as dysphoria was associated with negatively biased expectations for pleasant and unpleasant events. Thus, findings support the importance of further research on dysphoria as well as other symptoms of psychopathology in affective forecasting, and provide continued support for the relevance of basic emotion research in differentiating between various forms of psychopathology (Gruber et al., 2011
Several limitations of the present investigation can be noted. Results were based on a sample of young, primarily white, college students, assessed using self-report measures rather than a structured clinical interview, and our hypomania measure tapped total lifetime symptoms rather than just state hypomania. The generalizability of findings to older adults, diverse participants, community samples, and clinical samples warrants further study. Further, findings were based on affective forecasting for a particular target event. Situational moderators, such as interpersonal or achievement salience, could differentially impact the activation of dysphoric schemas, influencing observed effects.
The study was also balanced by several strengths. One, the study maximized power. Two, measurement reliability was strong. Multiple observations of affective forecasting were aggregated, and we incorporated multiple indicators of dysphoria. Three, the study included measures of anxiety symptoms and lifetime hypomanic symptoms to test for potential confounds. Finally, study hypotheses were grounded in existing theories of information processing in depression.
Future studies on forecasting biases can examine mediators, decisional consequences, and other domains of psychopathology symptoms. Foremost, drawing upon cognitive theory (Beck, 1976
; Rinck & Becker, 2005
), researchers can begin by investigating information processing constructs that explain the relationship between dysphoria and biased forecasts. For example, affective forecasting research indicates that people often rely upon their memory of emotional reactions to prior personal experiences when predicting reactions to relevant future events (E. W. Dunn & Laham, 2006
). It could be that negativistic biases in autobiographical memory or selective attention perpetuate affective forecasting problems for individuals in dysphoric states (Joormann & Gotlib, 2010
). Second, the decisional consequences of the dysphoric forecasting bias also warrant further attention. For example, cognitive theory emphasizes that negatively biased future expectations lead to behaviors that maintain depression, such as rumination or social withdrawal (Rinck & Becker, 2005
). Furthermore, in contexts that are normatively highly emotional, such as healthcare decision-making, people often regulate negative anticipatory emotions through decisional avoidance. Thus, researchers might examine whether individuals in dysphoric states are more avoidant of proactive behaviors or behavioral intentions, such as seeking genetic screening for hereditary disorders, getting tested for HIV, or inquiring about palliative care in the end of life. Finally, a potentially fruitful line of research would involve examining affective forecasting within the context of other domains of psychopathology symptoms. For example, symptoms of social avoidance may be more related to affective forecasts than actual hedonic experience (Quirk, Subramanian, & Hoerger, 2007
). Further, among individuals with suicidal ideation, changes in affective forecasting could increase risk of suicide attempts. Thus, findings suggest the need for a network of related studies on psychopathology and affective forecasting.
In closing, this study provides compelling evidence for a dysphoric forecasting bias, consistent with the cognitive theory of depression. These findings contribute to the generalizability of research on affective forecasting problems, demonstrate that forecasting biases vary substantially across individuals, and implicate the need for further research on the role of psychopathology in affective forecasting.