Computational dissociation of the causes of cognitive rigidity in depression

Depression distorts perceptions of the past and future, manifesting in symptoms such as hopelessness and anhedonia, along with increasingly rigid patterns of decision-making. However, the mechanistic link between important prognostic symptoms in depression and depressive rigidity remains poorly understood. In large part, this is because we lack validated computational models that explain how appraisals of the past and views of the future can mechanistically contribute to cognitive rigidity or flexibility. Here, we construct and test a mechanistic model that will allow us to quantify the impact of depressive symptoms on cognitive flexibility and open new avenues for early diagnosis and intervention. Previous work modeling decision-making in depression with reinforcement learning (RL) has shed light on how depressive symptoms like anhedonia alter reward-based judgements. However, standard RL lacks the validity needed to explain depressive rigidity. Here, we develop a novel model from another powerful computational framework: foraging theory. We designed this model with depressive symptoms in mind to explicitly link learning from the past and estimating the future to cognitive flexibility. The central hypothesis is that learning from past rewards and estimating future rewards are dissociable mechanisms that control cognitive flexibility. The specific aims of this proposal are to (1) Determine how appraisals of the past and future shape cognitive flexibility and (2) Examine how variations in the environment constrain cognitive flexibility. To accomplish these aims, we will characterize how judgements about the past and future influence decision-making rigidity and respond to environmental changes through model simulation and analysis. We will determine how individual differences in these cognitive processes are learned from the environment and if they predict rigidity by administering a flexible decision-making task to clinical depression and large online samples. Collecting depressive symptom inventories along with task data will allow us to interrogate the mechanisms by which depressive symptoms like anhedonia lead to rigidity. To test cross-species validity for preclinical work, we apply this model to a previously collected mouse behavioral dataset. Innovation: Our novel model will determine how views of the past and future, and their responses to the environment, contribute to cognitive rigidity, and how they are impacted by depressive symptoms in an online sample. The results will guide future hypothesis-driven research into the algorithmic basis of depressive rigidity in patients. Specifically, a future R01 application will test the model’s utility for predicting depression subtypes and treatment outcomes in a clinical population. This model will also enable us to study the physiological bases of these computational processes in animal models and humans undergoing invasive and non-invasive neurophysiolgoical studies.