Decision-Making and Behavioral Economics

This study shows that primate anterior insular cortex encodes economic decision variables as described by Prospect Theory, including reference points and asymmetric value functions.

The paper extends the drift-diffusion model to include value certainty, showing it better explains choice consistency and response time in empirical data.

This study investigates choice history effects in mice and humans, showing they are adaptive and linked to optimal foraging strategies, and introduces a new reinforcement learning model that better predicts behavior.

The paper investigates suboptimal decision-making in sequential tasks using a dynamic programming model and physiological measures.

The paper investigates how humans and monkeys make choices between multi-component rewards, showing that preferences can be represented as single-dimensional trade-offs using indifference curves and validated by machine learning.

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.

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.

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

The paper investigates how context influences probability estimation and identifies brain regions involved in this process.

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

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.

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.

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 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.

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.