Decision-Making and Behavioral Economics

Contextual Influences on Value-based Choice

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.

On the flexibility of basic risk attitudes in monkeys

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.

Stochastic satisficing account of confidence in uncertain value-based decisions

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 conflict negativity: A neural correlate of value conflict and indecision during financial decision making

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.

Dopaminergic drug effects on probability weighting during risky decision-making

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

The foraging perspective on economic choice

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.

Multi-attribute Decision-making is Best Characterized by an Attribute-Wise Reinforcement Learning Model

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.

Exploration and recency as the main proximate causes of probability matching: a reinforcement learning analysis

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.

Estimation of time-varying decision thresholds from the choice and reaction times with no assumption on the shape

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 idiosyncratic nature of confidence

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.

Sensitivity to sequential regularities in risky decision making

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.

Reminders of past choices bias decisions for reward in humans.

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

Reason’s Enemy Is Not Emotion: Engagement of Cognitive Control Networks Explains Biases in Gain/Loss Framing

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.