Insights from Machine Learning Applied to Human Visual Classification

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Felix A. Wichmann, Arnulf Graf


We attempt to understand visual classification in humans using both psy- chophysical and machine learning techniques. Frontal views of human faces were used for a gender classification task. Human subjects classi- fied the faces and their gender judgment, reaction time and confidence rating were recorded. Several hyperplane learning algorithms were used on the same classification task using the Principal Components of the texture and shape representation of the faces. The classification perfor- mance of the learning algorithms was estimated using the face database with the true gender of the faces as labels, and also with the gender es- timated by the subjects. We then correlated the human responses to the distance of the stimuli to the separating hyperplane of the learning algo- rithms. Our results suggest that human classification can be modeled by some hyperplane algorithms in the feature space we used. For classifica- tion, the brain needs more processing for stimuli close to that hyperplane than for those further away.