Decision-Making and Behavioral Economics

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

The paper re-analyzes six temporal discounting datasets using hierarchical Bayesian methods, finding consistent reductions in discounting with medium effect sizes from episodic future thinking.

The paper models decision-making under limited capacity, showing that optimal strategies shift from breadth to depth as capacity increases.

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

The paper investigates whether cognitive tasks linked to future thinking can reduce impulsivity in delay discounting but finds no clear evidence of effectiveness and highlights task repetition effects.

The paper shows humans use computationally cheap exploration strategies, with value-free random exploration controlled by noradrenaline.

The paper models decision-making in a hierarchical neural network, showing how excitatory-inhibitory balance affects decision strategies under different uncertainty levels.

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.

This study examines the association between COMT Val158Met polymorphism and susceptibility to framing effects in older adults, finding a significant link that diminishes after adjusting for demographic factors.

The paper tests whether learning bias stems from positivity or choice-confirmation, finding stronger positive prediction errors in free-choice conditions.

The paper identifies a psychiatric symptom dimension linked to decision-making performance through exploration strategies in a healthy population.

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.

The paper investigates base-rate neglect in probabilistic inference, showing it arises from neural computations in the orbitofrontal and medial prefrontal cortices that weight information sources.

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 context influences probability estimation and identifies brain regions involved in this process.

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 responds to Zhang et al. (2018) by reanalyzing mouse-tracking data to assess differences in attribute processing speeds during dietary self-control decisions.

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 introduces a model of causal inference that explains how people perceive their causal power through counterfactual reasoning, showing it better explains behavior than alternative models.

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.

The paper uses PCA to identify three latent components of risk-taking behavior from self-reports and behavioral tasks in young adults.