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Positive and Negative Affective Forecasting in Remitted Individuals with Bipolar I Disorder, and Major Depressive Disorder, and Healthy Controls

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Positive and Negative Affective Forecasting in Remitted Individuals with Bipolar I Disorder, and Major Depressive Disorder, and Healthy Controls Cogn Ther Res DOI 10.1007/s10608-017-9840-2 BRIEF REPORT Positive and Negative Affective Forecasting in Remitted Individuals with Bipolar I Disorder, and Major Depressive Disorder, and Healthy Controls Renee J. Thompson1 · Aleksandr Spectre2 · Philip S. Insel3 · Douglas Mennin4 · Ian H. Gotlib5 · June Gruber6 © Springer Science+Business Media New York 2017 Abstract Although emotional disturbances characterize Keywords Emotion · Affective forecasting · Bipolar mood disorders, little is known about the affective forecasts disorder · Depression of these individuals. We examined forecasted intensity and accuracy for negative affect (NA) and positive affect (PA) among two remitted clinical groups: individuals with Bipo- When making decisions, people often simulate how they lar I (BD; n = 31) and Major Depressive Disorder (MDD; will feel during future events (Gilbert and Wilson 2007). n = 21), and healthy controls (CTL; n = 32). We also exam- Some choose a movie because they think it will make them ined whether each group’s forecasting accuracy varied by feel happy. Some choose to go on a vacation because they valence. At the lab, participants forecasted their short-term think they will regret not going. Predicting how one will (next day) and long-term (next week) NA and PA; then feel guides decision-making behaviors. In the laboratory, they completed a week of experience sampling. The MDD healthy adults have generally been found to be accurate in group forecasted lower PA and higher NA than the CTL forecasting specific emotions (Robinson and Clore 2001). group; the BD group’s forecasts varied across time frames. In everyday life, however, people tend to overestimate There were no group differences in forecasting accuracies. both the duration (Gilbert and Wilson 2000) and intensity Regarding within group forecasting accuracy, the CTL of future affective experiences (e.g., Wilson et al. 2004, group was more accurate in PA than NA; the BD group was 2000). This affective overestimation may be driven by peo- similarly accurate across valence, and the MDD group’s ple’s tendency to forecast pleasant events as more pleasant accuracy varied based on the time frame. and negative events as more negative than the events prove to be (Liberman et al. 2002). We know even less about affective forecasting for indi- * June Gruber viduals who experience disturbances in emotion. Dis- [email protected] turbances in positive and negative affective experiences have been posited to be important for understanding the 1 Department of Psychology, Washington University, Saint Louis, MO, USA etiology and course of different forms of psychopathol- ogy, including Bipolar Disorder (BD) and Major Depres- 2 Department of Psychology, University of Cambridge, Cambridge, UK sive Disorder (MDD; e.g., Berenbaum et al. 2003; Kring and Bachorowski 1999). We examined affective forecast- 3 Department of Veteran Affairs, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA ing across mood-disordered populations—BD and MDD adults whose episodes are in remission (i.e., are not in 4 Department of Psychology, Hunter College, City University of New York, New York, NY, USA a manic, depressed, or mixed mood episode)—as well as healthy adults with no psychiatric history. If people with 5 Department of Psychology, Stanford University, Stanford, CA, USA mood disorders show greater affective forecasting biases, such as under-estimating positive affect (PA) or greatly 6 Department of Psychology and Neuroscience, University of Colorado, 345 UCB Muenzinger D321C, Boulder, overestimating NA, this would likely affect their motiva- CO 80309-0345, USA tion, behaviors and even decision-making. For example, Vol.:(0123456789)1 3 Cogn Ther Res people with depression may start avoiding social events, negative automatic thoughts include evaluating clients’ which could weaken their relationships and social support, overestimation of their levels of negative emotions in MDD and thereby further sustain or worsen their depression. For (e.g., Beck 2011), as well as, overly positive and ambitious clinical populations, this could even influence whether they future-oriented cognitions in BD (e.g., Johnson 2005). seek treatment. Even though these psychological treatments address biases in affective forecasting, little research has examined affec- tive forecasting in BD and MDD primarily characterized by Affective Disturbances in BD and MDD disturbances in emotion. To our knowledge, only one study has examined affec- Bipolar Disorder tive forecasting relevant to BD. In a large non-clinical student sample, Hoerger et al. (2012) found that biases in BD is a severe and chronic psychiatric disorder associated affective forecasting for negative and positive emotions with profound social, functional, and occupational impair- surrounding Valentine’s Day were unrelated to lifetime ment (Coryell et al. 1993). Diagnostic criteria for BD cen- symptoms of hypomania. As no work to date has examined trally features abnormally elevated, expansive, or irritable affective forecasts among a sample of individuals diag- mood (American Psychiatric Association 2013). Emerging nosed with BD, additional research using clinical samples research suggests that BD is associated with increased posi- spanning a larger age range is needed to better understand tive emotional reactivity (Gruber et al. 2008; M’Bailara the role of affective forecasting in BD. et al. 2009; Meyer et al. 2001) and difficulty with regulating In contrast to BD, several investigators have examined emotions (Johnson et al. 2007; Phillips and Vieta 2007). the relation between emotional forecasting and depression, For example, BD is associated with a increased positive both symptoms and diagnoses. Consistent with cognitive emotion reactivity both in response to (i.e., liking), and in theories of depression (e.g., Beck 1976; Alloy et al. 1992), anticipation of (i.e., wanting), pleasant stimuli (e.g., Alloy but not of depressive realism (e.g., Moore and Fresco et al. 2009; Gruber 2011a). People diagnosed with BD have 2012), higher levels of depressive symptoms in student trouble regulating both positive (Farmer et al. 2006; Gruber samples were related to less accurate estimates of negative et al. 2011; Johnson et al. 2008) and negative (e.g., Gruber mood (Wenze et al. 2012) and negative affective reactions et al. 2011; Johnson et al. 2008) emotion intensity as well. to a future event (Hoerger et al. 2012). Both studies found that higher levels of depressive symptoms were associated Major Depressive Disorder with overestimations of negative affect (Hoerger et al. 2012; Wenze et al. 2012). In addition, Hoerger et al. (2012) found MDD is another mood disorder characterized by distur- that higher levels of depressive symptoms were associated bances in emotion that is associated with great costs at with less accurate estimates of PA, with higher levels of both individual and societal levels. A diagnosis of MDD depressive symptoms related to underestimations of PA. In requires the presence of depressed mood and/or anhedonia a study with a clinical sample, compared to nondepressed (American Psychiatric Association 2013). Individuals with controls, depressed individuals anticipated fewer future MDD exhibit other aberrations in emotion besides those positive experiences (MacLeod and Salaminiou 2001). encompassed by these diagnostic criteria, including but Importantly, these forecasting biases appear to be unique to not limited to elevated affective instability (e.g., Thompson symptoms of depression; they are not significantly related et al. 2012). Contemporary models of MDD highlight core to symptoms of anxiety (Wenze et al. 2012; Macleod and deficits in the experience of positive emotion help differ- Salaminiou 2001) or, as noted above, to symptoms of hypo- entiate MDD from other forms of psychopathology (e.g., mania (Hoerger et al. 2012). Kring and Sloan 2009). Research is needed to continue to Other affective forecasting research has provided less elucidate factors that help to explain aberrations in emo- support for cognitive theories of depression. For example, tion that characterize BD and/or MDD. In this context, one Yuan and Kring (2009) found that, compared to a nondys- promising direction is examining individuals’ beliefs about phoric group, dysphoric participants were less accurate in future emotional experiences or their affective forecasting. their affective forecasts of happiness during a gambling task, overestimating their levels of happiness. Similarly, Wenze et al. (2012) found that participants generally over- Affective Forecasting in BD and MDD estimated PA, but, inconsistent with Yuan and Kring’s findings, those with higher levels of depressive symptoms The notion that people diagnosed with mood disorders were more accurate in their (over)estimations of PA. Given are poor at affective forecasting is inherent in many cog- that the little research that has examined affective forecast- nitive behavioral treatments. For example, assessments of ing and MDD has had mixed findings, additional research 1 3 Cogn Ther Res is needed. Further, research on those with MDD has been higher PA levels than both the CTL and MDD groups, limited to those who are currently in episode. Examining and the CTL group would forecast higher PA levels than the affective forecasting of individuals with MDD in remis- the MDD group (Hypothesis 1a). Individuals with MDD sion will elucidate more trait-like patterns of affective fore- have been found to predict
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