Decision-Making and Behavioral Economics

Participants learned base rates from decision-making without feedback by updating their beliefs using counterfactual confidence representations, improving accuracy and confidence.

The paper develops a hybrid computational model of the inverse base rate effect and uses fMRI to show that dissimilarity-based processing contributes to the effect, with activation patterns in left rostrolateral prefrontal cortex tracking model predictions.

The paper investigates how contextual and cue information influence value-based choices using EEG and a computational model, showing that contextual loss-related attributes drive behavior through gamma frequency neural activity.

This study shows that monkeys exhibit flexible risk attitudes, with varying utility and probability weighting functions across tasks, suggesting dynamic construction of reward value rather than fixed traits.

This paper investigates how outcome variance affects decision-making and confidence in uncertain value-based choices, proposing a stochastic satisficing model to explain confidence ratings.

The paper investigates how conflict negativity (CN) correlates with decision-making in financial contexts, showing it predicts indecision and is influenced by subjective value perceptions.

This study investigates how dopamine D2/D3 receptor antagonism affects probability weighting during risky decision-making in healthy individuals and pathological gamblers.

The paper applies foraging theory to economic choice, framing decisions as evolved psychological processes similar to foraging, with implications for self-control and binary choice mechanisms.

The paper shows that decision-making with multiple attributes is best modeled by using all attributes, representing them individually, and incorporating recency effects, rather than relying on heuristics or whole-stimulus associations.

The paper uses reinforcement learning to analyze why humans probability match instead of maximize in a learning task, finding that pattern search, exploration, and recency significantly influence behavior.

The paper introduces a fast heuristic algorithm to estimate time-varying decision thresholds from choice and reaction times without assuming their shape, outperforming conventional methods under time constraints.

The paper investigates individual differences in confidence reporting by analyzing how people estimate confidence under uncertainty, finding that some individuals use perceived probability while others use Fisher information.

This study investigates the relationship between implicit probabilistic sequence learning and risky decision making, identifying clusters of participants with distinct risk-taking behaviors and sensitivity to statistical regularities.

The paper shows that reminders of past choices influence reward-based decisions by biasing the use of individual past experiences over averaged rewards.

The paper investigates gain/loss framing biases in decision-making using fMRI data, finding that biased decisions correlate with default brain states rather than emotional processes.