Decision-Making and Behavioral Economics

The paper introduces a method to monitor decision boundaries and identifies a consistency bias in evidence integration that improves decision robustness.

This paper trains a deep neural network to predict human decisions in a value-based task and uses cognitive models to explain the model's behavior, revealing task-irrelevant decision patterns.

The paper shows that having control over when to stop sampling improves decision accuracy, parietal decision signals, and EEG encoding of numerical information in human decisions from experience.

The paper shows that asymmetric learning, where beliefs about winners or losers are updated selectively, improves transitive inference in humans and aligns with cognitive distortions observed in psychophysics and behavioral economics.

The paper tests how exposure to appetitive, aversive, and neutral visual cues affects temporal discounting and physiological arousal in humans, finding no significant increase in discounting despite increased arousal.

This study examines how the aversive value of pain influences human decision-making in the context of monetary rewards and electric shocks.

This paper develops a task to quantify the subjective cost of self-control by measuring how much people are willing to pay to avoid exerting self-control in tempting situations.

The paper applies a multi-attribute drift diffusion model to dietary choices, showing how health and taste attributes influence decision-making processes and can be targeted by interventions.

The paper shows that evaluating options shifts their perceived value and attributes to favor the chosen option, increasing confidence, regardless of whether the choice is explicit or not.

This study investigates how initial experience influences risky decisions after a change point, finding that recent experiences adjust behavior while initial experience still has an impact.

This paper introduces a computational model of metacognitive control in value-based decision-making, linking mental effort, confidence, and preference reversals through an effort-confidence tradeoff.

This study tests new virtual navigation tasks to compare human and rodent decision-making, finding shared behavioral correlates of deliberation and links to BMI but not smoking.

This study assesses the reliability of temporal discounting tasks in VR environments, finding good to excellent reliability for discount rate parameters but mixed results for other measures.

The paper develops a decision-making model that explains human fixation behavior by showing how attention modulates information reliability and optimizes cognitive resource allocation.

This study compares pruning behaviors in depressed and healthy individuals during multi-step decision-making but finds no group differences or relationship between pruning and depressive symptoms.

The paper shows the brain automatically encodes representational uncertainty in probability judgments, linking it to probability distortion behavior.

The paper shows that confirmation bias can be adaptive when paired with strong metacognition, improving decision-making through selective information processing.

This study investigates decision-making in mice by analyzing how cost and benefit parameters influence rationality, finding that cost parameters and sunk cost effects significantly contribute to suboptimal choices.

The paper shows that pupillary indices correlate with decision-making biases, with low neural gain linked to strong biases and high gain linked to reduced biases but increased indecisiveness.

The paper introduces a dual-decisions method to infer confidence without explicit valuation, showing human confidence levels are discrete rather than continuous.

This study tests whether value certainty, not just value difference, affects decision-making accuracy, confidence, and response time.

This paper introduces a Bounded Log-Odds Model to explain probability and relative frequency distortions in decision-making and shows it outperforms alternative models.

This study investigates how people gather information about trustworthiness, finding that participants avoid appearing suspicious by sampling less when the trustee learns about their inquiries.

This study investigates endogenous default options in economic choices, revealing two distinct biases: natural preference for certain options and learned defaults from past choices.

This study evaluates a Bayesian adaptive design optimization method for measuring delay discounting, showing higher reliability, precision, and efficiency compared to a conventional staircase method across three populations.

The paper examines how individuals update beliefs and make choices in foraging scenarios, finding that they exhibit biased belief updating leading to suboptimal decisions.

This study investigates how the timing and weighting of reward attributes influence decision-making using a time-varying drift diffusion model and experimental data.

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.