Psychophysical Studies of Recognition for Psychology and Computer Vision

Recognition is a core process in both human cognition and Computer Vision. Psychophysical studies provide a powerful framework for understanding visual recognition by precisely measuring behavioral responses to controlled stimuli. These experiments deepen our understanding of perception and categorization in psychology, while also offering valuable benchmarks for evaluating computational models in Computer Vision and Machine Learning.

This dissertation covers topics ranging from the use of human perceptual data in machine learning contexts to studying perception for theoretical insight, including comparisons with models to assess how well they capture human recognition behavior. We begin by exploring how human perceptual responses can guide improvements in classification models. We then analyze human eye gaze during face recognition tasks to examine attention and feature importance. Finally, we study how people perceive category boundaries under different stimulus distributions, comparing their behavior to models based on Extreme Value Theory (EVT) and Gaussian assumptions. Together, these studies emphasize the value of psychophysical insights for understanding recognition in both humans and Computer Vision models.