Decision-Making and Behavioral Economics

This paper investigates choice-induced preference change (CIPC) and challenges the cognitive dissonance theory by proposing it results from value representation refinement during decision-making, supported by behavioral experiments.

This study decomposes loss aversion into valuation and response biases using computational modeling and physiological biomarkers like gaze and pupil dilation.

The paper shows that saccade vigor correlates with subjective value of visual stimuli, but only partially predicts choice behavior.

This study finds that temporal discounting correlates with directed exploration but not with random exploration in decision-making tasks.

GLAMbox provides a Python toolbox for analyzing the relationship between gaze allocation and decision behavior using Bayesian models.

The paper examines how autistic traits affect information sampling strategies in a probabilistic inference task, finding trait-related differences in cost and evidence prioritization during decision-making.

This study finds that chronic cocaine users show altered sensitivity to gain information during risky decision-making, leading to increased risk-seeking in unfavorable scenarios.

The paper investigates how low-level neural processes and high-level cognitive processes interact to influence context-dependent choice in humans, finding that both contribute to distinct behavioral patterns.

CHaRTr is an R toolbox that allows users to implement and test various decision-making models, including modern extensions of the diffusion decision model.

The paper derives the optimal policy for multi-alternative decisions and shows how it can be implemented by a neural circuit with normalization and urgency signals.

This study applies Prospect Theory to rats, showing they exhibit probability distortion and diminishing sensitivity similar to humans, suggesting shared neural mechanisms.

The paper investigates how humans evaluate complex risky options by showing subjects use integrated value functions for individual outcomes and differential weighting based on reward magnitude or probability for overall value construction.

The paper investigates individual differences in the link between gaze behavior and choice behavior using a computational model applied to individual participants.

This study finds adolescents do not take more risks than children or adults in a novel uncertain situation, challenging the idea of increased risk-taking during adolescence.

The paper examines human behavior in a simplified explore-exploit task and finds deviations from optimal strategies, revealing a suboptimal switching rule and sequence-level variability.

The paper proposes an information-theoretic model of subjective value coding that explains variability in value reports and preference choices, and predicts a new preference illusion validated with empirical data.

This study shows that preferences change during decision-making, with neural activity and eye-tracking predicting these changes and their behavioral relevance at re-evaluation.

The paper develops a drift-diffusion model for sequential decision-making, showing how prior information affects future decisions and optimizing reward rates in correlated trial sequences.

This study shows that decision-making biases in monkeys can be explained by rational satisficing strategies rather than suboptimal learning or physical limitations.

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.

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.

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 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 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.

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.