Inhibition and control in depression (original) (raw)
Related papers
2007
ABSTRACT Depression, like many psychiatric disorders, is a disorder of affect. Over the past decades, a large number of affective issues in depression have been characterised, both in human experiments and animal models of the disorder. Over the same period, experimental neuroscience, helped by computational theories such as reinforcement learning, has provided detailed descriptions of the psychology and neurobiology of affective decisions making.
Psychological Medecine, 2022
Backgrounds. Value-based decision-making impairment in depression is a complex phenomenon: while some studies did find evidence of blunted reward learning and reward-related signals in the brain, others indicate no effect. Here we test whether such reward sensitivity deficits are dependent on the overall value of the decision problem. Methods. We used a two-armed bandit task with two different contexts: one 'rich', one 'poor' where both options were associated with an overall positive, negative expected value, respectively. We tested patients (N = 30) undergoing a major depressive episode and age, gender and socioeconomically matched controls (N = 26). Learning performance followed by a transfer phase, without feedback, were analyzed to distangle between a decision or a value-update process mechanism. Finally, we used computational model simulation and fitting to link behavioral patterns to learning biases. Results. Control subjects showed similar learning performance in the 'rich' and the 'poor' contexts, while patients displayed reduced learning in the 'poor' context. Analysis of the transfer phase showed that the context-dependent impairment in patients generalized, suggesting that the effect of depression has to be traced to the outcome encoding. Computational model-based results showed that patients displayed a higher learning rate for negative compared to positive outcomes (the opposite was true in controls). Conclusions. Our results illustrate that reinforcement learning performances in depression depend on the value of the context. We show that depressive patients have a specific trouble in contexts with an overall negative state value, which in our task is consistent with a negativity bias at the learning rates level.
The Neurophysiological Substrate of the Decision-Making Process in Depressed Patients
2020
Background: The neural circuits involved in the decision-making process and social emotion participate in the same circuits seen in major depressive disorder. This study aimed to investigate in depressed patients, the decision making process in risk/reward situations using neurophysiological methods for a better assessment of functional aspects related to decision making deficit that are seen in major depression. Methods: Forty patients were studied, 20 with depression and 20 without. After applied the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID-I) and Hamilton Depression scale (HAM-D), the Iowa Gambling Task (IGT) was applied to analyze the risk/ reward decision-making behavior. The Skin Conductance Response (SCR) was recorded to analyze the emotional anticipatory learning effect during the IGT. Besides, an EEG was recorded to measure the Frontal Alpha Asymmetry Index (FAAI). Results: Depressed patients presented a lower Net score and a deficit in anticipatory l...
From reinforcement learning models to psychiatric and neurological disorders
2011
Over the last decade and a half, reinforcement learning models have fostered an increasingly sophisticated understanding of the functions of dopamine and cortico-basal ganglia-thalamo-cortical (CBGTC) circuits. More recently, these models, and the insights that they afford, have started to be used to understand important aspects of several psychiatric and neurological disorders that involve disturbances of the dopaminergic system and CBGTC circuits.