Similarity Judgment

Similarity judgment research investigates how humans perceive and quantify the resemblance between stimuli, aiming to understand the underlying cognitive processes and build computational models that mirror human perception. Current research focuses on leveraging deep neural networks and large language models to predict human similarity judgments, often employing techniques like attention mechanisms and embedding alignment to improve accuracy and interpretability. These efforts are significant because they advance our understanding of human cognition and enable the development of more human-aligned AI systems across diverse applications, including image retrieval, recommendation systems, and concept learning.