Xiangjuan Ren

This paper introduces compressive learning, a framework that uses higher-order network structure to improve human knowledge acquisition by embedding node-degree inhomogeneity into pre-learning trajectories.

This study investigates the motivational mechanisms behind children's third-party altruistic behaviors using the motive cocktail model, revealing gender differences in moral aversion and intervention tendencies.

The paper develops a computational framework to explain decision anomalies through bounded rationality, showing it outperforms existing models in fitting human behavior.

The paper identifies a 'motive cocktail' of seven socioeconomic motives that explain altruistic behaviors through experimental data and computational modeling.

The paper shows human brains compute temporal statistical relationships from image sequences, revealing lower-order transition probabilities and higher-order community structures that predict behavior.

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

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