Quantifying the immediate computational effects of preceding outcomes on subsequent risky choices

Forty years ago, prospect theory introduced the notion that risky options are evaluated relative to their recent context, causing a significant shift in the study of risky monetary decision-making in psychology, economics, and neuroscience. Despite the central role of past experiences, it remains unclear whether, how, and how much past experiences quantitatively influence risky monetary choices moment-to-moment in a nominally learning-free setting. We analyzed a large dataset of risky monetary choices with trial-by-trial feedback to quantify how past experiences, or recent events, influence risky choice behavior and the underlying processes. We found larger recent outcomes both negatively influence subsequent risk-taking and positively influence the weight put on potential losses. Using a hierarchical Bayesian framework to fit a modified version of prospect theory, we demonstrated that the same risks will be evaluated differently given different past experiences. The computations underlying risky decision-making are fundamentally dynamic, even if the environment is not.

Repetitive negative thinking following exposure to a natural stressor prospectively predicts altered stress responding and decision-making in the laboratory

Repetitive negative thinking (RNT) represents a transdiagnostic risk factor for affective disorders, and stress is theorized to exacerbate this vulnerability. One mechanism by which stress may influence individual differences in psychiatric symptoms is through altered decision-making, and loss aversion in particular. The present study uses multiple methods to investigate the relationships between RNT, stress, and decision-making. We measured RNT in young adults (N = 90) recently exposed to a natural stressor, Hurricane Irma, and tested the influence of RNT on changes in affect, cortisol, and decision-making during a laboratory stress induction two months later. Post-hurricane RNT predicted greater increases in loss averse decision-making (β = 0.30 [0.14, 0.47], p < .001; rp² = 0.079) and negative affect (β = 0.59 [0.37, 0.81], p < .001; rp² = 0.319) during the early-phase response to the laboratory stressor, as well as poorer cortisol recovery (β = 0.32, [0.10, 0.54], p = .005; rp² = 0.095) in the late-phase stress response. Results highlight the role of loss aversion and stress in understanding RNT as an affective vulnerability factor.

Decomposing loss aversion from gaze allocation and pupil dilation

Loss-averse decisions, in which one avoids losses at the expense of gains, are highly prevalent. However, the underlying mechanisms remain controversial. The prevailing account highlights a valuation bias that overweighs losses relative to gains, but an alternative view stresses a response bias to avoid choices involving potential losses. Here we couple a computational process model with eye-tracking and pupillometry to develop a physiologically grounded framework for the decision process leading to accepting or rejecting gambles with equal odds of winning and losing money. Overall, loss-averse decisions were accompanied by preferential gaze toward losses and increased pupil dilation for accepting gambles. Using our model, we found gaze allocation selectively indexed valuation bias, and pupil dilation selectively indexed response bias. Finally, we demonstrate that our computational model and physiological biomarkers can identify distinct types of loss-averse decision makers who would otherwise be indistinguishable using conventional approaches. Our study provides an integrative framework for the cognitive processes that drive loss-averse decisions and highlights the biological heterogeneity of loss aversion across individuals.

Reinforcement sensitivity, depression and anxiety: A meta-analysis and meta-analytic structural equation model

Reinforcement Sensitivity Theory (RST) posits that individual differences in reward and punishment processing predict differences in cognition, behavior, and psychopathology. We performed a quantitative review of the relationships between reinforcement sensitivity, depression and anxiety, in two separate sets of analyses. First, we reviewed 204 studies that reported either correlations between reinforcement sensitivity and self-reported symptom severity or differences in reinforcement sensitivity between diagnosed and healthy participants, yielding 483 effect sizes. Both depression (Hedges' g = .99) and anxiety (g = 1.21) were found to be high on punishment sensitivity. Reward sensitivity negatively predicted only depressive disorders (g = -.21). More severe clinical states (e.g., acute vs remission) predicted larger effect sizes for depression but not anxiety. Next, we reviewed an additional 39 studies that reported correlations between reinforcement sensitivity and both depression and anxiety, yielding 156 effect sizes. We then performed meta-analytic structural equation modeling to simultaneously estimate all covariances and control for comorbidity. Again we found punishment sensitivity to predict depression (β = .37) and anxiety (β = .35), with reward sensitivity only predicting depression (β = -.07). The transdiagnostic role of punishment sensitivity and the discriminatory role of reward sensitivity support a hierarchical approach to RST and psychopathology.

Affect and Decision Making: Insights and Predictions from Computational Models

In recent years interest in integrating the affective and decision sciences has skyrocketed. Immense progress has been made, but the complexities of each field, which can multiply when combined, present a significant obstacle. A carefully defined framework for integration is needed. The shift towards computational modeling in decision science provides a powerful basis and a path forward, but one whose synergistic potential will only be fully realized by drawing on the theoretical richness of the affective sciences. Reviewing research using a popular computational model of choice (the drift diffusion model), we discuss how mapping concepts to parameters reduces conceptual ambiguity and reveals novel hypotheses.