Brad Wyble

The paper introduces the 2f-ST2 model to explain temporal feature conjunctions and distractor intrusion errors in perception, showing it can simulate behavioral and electrophysiological results without parameter changes.

The paper introduces a model of working memory that encodes and retrieves visual knowledge through latent representations, showing efficient encoding of familiar items and storage of novel patterns.

The paper uses a computational model to explain an ERP pattern associated with proactive suppression in visual search, offering a mechanistic account distinct from prior interpretations.

This paper introduces a model of reflexive attention that simulates neural attractor states and their role in visual processing, linking them to behavioral and neural correlates like accuracy and EEG components.

The paper investigates how temporal autocorrelation in visual stimuli affects attentional latency, finding that smooth changes increase selection lag compared to random changes.

The paper demonstrates how overfitting through hyperparameter tuning can lead to non-generalizable results in machine learning, despite cross-validation, and suggests safeguards to prevent this.

The paper shows that conceptual content in images rapidly triggers covert spatial attention, as evidenced by behavioral and electrophysiological responses.