Angela J. Yu

Adaptive experiments in high-dimensional feature spaces: A particle filtering approach

This paper introduces HOPE, a method for efficiently estimating high-dimensional psychometric functions by adaptively selecting stimuli, reducing uncertainty faster than traditional methods.

Multiple and subject-specific roles of uncertainty in reward-guided decision-making

This study identifies multiple individual and heuristic-based roles of uncertainty in human reward-guided decision-making using a novel bandit task.

Understanding double descent through the lens of principal component regression

The paper analyzes the double-descent phenomenon in linear regression, showing equivalence between second-descent and principal component regression and conditions under which second-descent outperforms first-descent models.

Distinct motivations to seek out information in healthy and addicted 1 individuals

This study compares information-seeking motivations between healthy individuals and problem gamblers, finding distinct patterns in novelty-seeking and uncertainty-reduction.

Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation

The paper introduces a neural approximation algorithm, EXP, that outperforms existing models in approximating Bayesian prediction performance while maintaining computational efficiency through a persistent prior mechanism.

Beauty-in-averageness and its contextual modulations: A Bayesian statistical account

This paper proposes a Bayesian statistical model to explain context-dependent facial attractiveness judgments, showing how statistical typicality and attentional mechanisms influence perception.

Computational modeling of social face perception in humans: Leveraging the active appearance model

This paper develops a computational model to predict human social trait judgments from facial features using an active appearance model and linear trait axes, achieving state-of-the-art predictive accuracy.