We explore the connections between machine learning and human learning in one form of semi-supervised classification. 22 human subjects completed a novel 2-class categorization task in which they were first taught to categorize a single labeled example from each category, and subsequently were asked to categorize, without feedback, a large set of additional items. Stimuli were visually complex and unrecognizable shapes. The unlabeled examples were sampled from a bimodal distribution with modes appearing either to the left (left-shift condition) or right (right-shift condition) of the two labeled examples. Results showed that, although initial decision boundaries were near the middle of the two labeled examples, after exposure to the unlabeled examples, they shifted in different directions in the two groups. In this respect, the human behavior conformed well to the predictions of a Gaussian mixture model for semi-supervised learning. The human behavior differed from model predictions in other interesting respects, suggesting some fruitful avenues for future inquiry.
human behavior,gaussian mixture model,semi-supervised classification,human learning,human subject,unlabeled example,left-shift condition,right-shift condition,semi-supervised learning,model prediction,machine learning,semi supervised learning,human performance
Categorization,Semi-supervised learning,Pattern recognition,Computer science,Human learning,Unsupervised learning,Artificial intelligence,Machine learning,Mixture model