We want to understand the neural mechanisms responsible for visual cognition, and we want to instantiate these mechanisms into computational algorithms that resemble and perhaps even surpass human performance. In order to build such biologically inspired visually intelligent machines, we first need to define visual cognition capabilities at the behavioral level. What types of shapes can be recognized, and when and how? Under what conditions do people make mistakes during visual processing? How much experience and what type of experience with the world is required to learn to see? To answer these questions, we need to quantify human performance under well-controlled visual tasks. A discipline with the picturesque and attractive name of psychophysics aims to rigorously characterize, quantify, and understand behavior during cognitive tasks.