Abstract
The neurobiological literature implicates chronic stress induced decision-making deficits as a major contributor to depression and anxiety. Given that females are twice as likely to suffer from these disorders, we hypothesized the existence of sex difference in the effects of chronic stress on decision-making. Here employing a decision-making paradigm that relies on reinforcement learning of probabilistic predictive relationships, we show female volunteers with a high level of perceived stress in the past month are more likely to make suboptimal choices than males. Computational characterizations of this sex difference suggest that while under high stress, females and males differ in their weighting but not learning of the expected uncertainty in the predictive relationships. These findings provide a mechanistic account of the sex difference in decision-making under chronic stress and may have important implications for the epidemiology of sex difference in depression and anxiety.
Highlights
The neurobiological literature implicates chronic stress induced decision-making deficits as a major contributor to depression and anxiety
We found a sex difference in choice performance under high stress in a sample of young adults: females choose about 20% less correct options compared to males
This sex difference is primarily driven by enhanced performance in males rather than impaired performance in females by chronic stress
Summary
The neurobiological literature implicates chronic stress induced decision-making deficits as a major contributor to depression and anxiety. Computational characterizations of this sex difference suggest that while under high stress, females and males differ in their weighting but not learning of the expected uncertainty in the predictive relationships These findings provide a mechanistic account of the sex difference in decision-making under chronic stress and may have important implications for the epidemiology of sex difference in depression and anxiety. RL-based decision-making involves two independent computational processes, an initial RL of the probabilistic cue-outcome contingencies followed by a subsequent weighting of the learned probability or uncertainty This uncertainty is known as “expected uncertainty” or “irreducible uncertainty” because the uncertainty or unreliability of predictive relationships is expected and not reduced by gathering more experience[20,21,22]. Recent research suggests that learning and weighting of expected uncertainty involve different cognitive and neural computations in the b rain[20,23] and successful decision-making requires the proper implementation of both of them
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