Psychiatry: Insights into depression through normative decision-making models (original) (raw)

Decision Making under Risk in Patients Suffering from Schizophrenia or Depression

Brain Sciences, 2021

Studies have reported difficulties in decision making for patients with schizophrenia or depression. Here, we investigated whether there are differences between schizophrenia patients, depressed patients, and healthy individuals (HC) when decisions are to be made under risk and cognitive flexibility is required. We were also interested in the relationships between decision making, cognitive functioning, and disease severity. Thirty HC, 28 schizophrenia patients, and 28 depressed patients underwent structured clinical assessments and were assessed by the Positive and Negative Syndrome Scale or Hamilton Rating Scale. They performed the Probability-Associated Gambling (PAG) Task and a neuropsychological test battery. Both patient groups obtained lower scores than HC in memory and executive function measures. In the PAG task, relative to HC, depressed patients made slower decisions but showed a comparable number of advantageous decisions or strategy flexibility. Schizophrenia patients w...

Decision making and neuropsychiatry

2001

Abnormal decision making is a central feature of neuropsychiatric disorders. Recent investigations of the neural substrates underlying decision making have involved qualitative assessment of the cognition of decision making in clinical lesion studies (in patients with frontal lobe dementia) and neuropsychiatric disorders such as mania, substance abuse and personality disorders.

Anhedonia and anxiety underlying depressive symptomatology have distinct effects on reward-based decision-making

PloS one, 2017

Depressive pathology, which includes both heightened negative affect (e.g., anxiety) and reduced positive affect (e.g., anhedonia), is known to be associated with sub-optimal decision-making, particularly in uncertain environments. Here, we use a computational approach to quantify and disambiguate how individual differences in these affective measures specifically relate to different aspects of learning and decision-making in reward-based choice behavior. Fifty-three individuals with a range of depressed mood completed a two-armed bandit task, in which they choose between two arms with fixed but unknown reward rates. The decision-making component, which chooses among options based on current expectations about reward rates, is modeled by two different decision policies: a learning-independent Win-stay/Lose-shift (WSLS) policy that ignores all previous experiences except the last trial, and Softmax, which prefers the arm with the higher expected reward. To model the learning componen...

Contextual influence of reinforcement learning performance of depression: evidence for a negativity bias

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.