Computational modeling of pessimistic future views in individuals with depressive symptoms: dynamics in affective forecasts
Researchers
Description
Negative beliefs about the future are proposed to be a key component of depressive cognition. Bias in imagining and forecasting affective experiences, or overestimating future negative affect and underestimating positive affect, is thought to guide maladaptive emotion regulation, such as experiential avoidance. These biased beliefs, therefore, prolong negative affective experiences and cause individuals to feel hopeless. Theories and empirical evidence suggest that the negative future beliefs are not a mere symptom of depression but a vulnerability factor that interacts with stress to develop depressive symptoms. Despite extensive research in the field, it remains unclear how people with depressive symptoms, or those vulnerable to depression, come to believe that they will never feel better. Although existing descriptive models propose that specific psychological constructs (e.g., schemas) prompt the negative beliefs, these models are often too vague to formulate a psychometrically precise model. Given that the descriptive models, and current behavioral experiments that assess negative cognitive biases, have limited predictive validity, a novel approach is warranted to clarify how biased cognition is generated and what dysfunction specifically contributes to such bias and depressive symptoms. The focus of the proposed project is to (a) model process details in generating negative affective forecasts by using computational modeling and (b) systematically test individual differences in these forecasting processes linked to depressive symptoms. To this end, we will test the original hypothesis that depressive symptoms are associated with difficulty in updating beliefs about emotional changes, that is, holding old beliefs that negative affect will continue and ignoring a new observation that negative affect is decreasing. This belief-updating model is formulated in the framework of a statistical filter. We will test and validate the model on data collected under laboratory and daily life (experience sampling) settings to increase ecological validity. Additionally, we will evaluate the predictive power on depressive symptomatology at a six-month follow-up. In sum, this project aims to provide fundamental new insights into the mechanisms that generate negative future beliefs in depression at the theoretical and methodological levels. Our innovative computational approach is likely to refine existing theories of depressive cognition and increase the predictive validity of biased cognition as a vulnerability factor for depression. Knowing the process details that generate negative beliefs is also relevant for clinical practices, as specifying the root of biased cognition is an important prerequisite for developing effective intervention and cognitive training.
Related publications from our group
- Takano, K., Van Grieken, J., Raes, F. (2019). Difficulty in updating positive beliefs about negative cognition is associated with increased depressed mood. Journal of Behavior Therapy and Experimental Psychiatry, 64, 22-30.
- Iijima, Y., Takano, K., Boddez, Y., Raes, F., & Tanno, Y. (2017). Stuttering thoughts: Negative self-referent thinking is less sensitive to aversive outcomes in people with higher levels of depressive symptoms. Frontiers in Psychology, 8, 1333.