Decision-Making and Behavioral Economics

The paper investigates how an unavailable distractor influences multiattribute choice, revealing both positive and negative effects and proposing a model to explain these phenomena.

The paper investigates learning asymmetry in reinforcement learning by showing that initial value expectations influence how positive and negative prediction errors affect value updates and choices.

The paper examines context-dependent choice in real-world settings using restaurant rating data, finding that valuations and choices are influenced by the context of available options.

The paper extends geometric time-discounting theory to ternary choices and shows magnitude-sensitive reaction times in humans and slime moulds, with geometric discounting as the main explanation.

This study shows that humans prefer choices even when they don't affect outcomes, driven by a need for personal control rather than rewards or exploration.

The paper shows that individual risk aversion in financial decisions stems from noisy perception of numerical magnitudes rather than subjective valuation.

The paper tests if incorporating individual attribute information into computational models improves their ability to predict value-based decision-making patterns.

The paper extends the drift-diffusion model to account for choice-induced preference change by allowing drift rates to vary during decision-making based on attended attributes.

The paper investigates how mood fluctuations, influenced by incidental feedback, affect choice behavior through neural activity in the vmPFC and daIns.

This study uses computational modeling to analyze risky decision-making in a general population sample, finding that decision parameters correlate with age and gender but not mental health symptoms.

The paper examines how motor response vigour and eye movements correlate with subjective value during intertemporal choice, finding that higher rewards are discounted less and are associated with stronger responses.

The paper evaluates a correction method for slow drifts in decision criterion, showing it fails for common updating strategies and proposes a model-based approach to better disambiguate drifts from systematic updates.

This study investigates choice biases in economic decisions under sequential offers in rhesus monkeys, identifying distinct neural stages responsible for accuracy, order bias, and preference bias.

The paper shows humans actively seek confirming evidence for their choices, leading to biased decision-making when revising earlier decisions.

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

The paper develops a Bayesian computational model to predict individual differences in non-reinforced preference changes during Cue-Approach Training.

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 shows that single-neuron activity in monkeys' reward circuitry reflects prospect theory parameters for risky gambles, with distributed coding across brain regions.

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

The paper tests a model proposing that sunk cost sensitivity in foraging tasks is due to task performance mechanics, finding it incompatible with data and supporting actual sunk cost sensitivity.

The paper investigates how the framing of social advice influences decision-making, finding that appetitive framing increases trust and learning compared to aversive framing.

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.

This study compares human and rodent decision-making under budget constraints for different reward types, finding that irrational choices occur with non-food rewards.

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 develops a method to compare interpersonal utility using neural activity, finding that brain signals correlate with expected utility and validate an optimal decision rule for well-being aggregation.

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.