When we perceive a visual object, we implicitly or explicitly 
associate it with an object category we know. Previous 
research has shown that the visual system can use local, 
informative image fragments of a given object, rather than 
the whole object, to classify it into a familiar category. 
How we acquire informative fragments has remained unclear, 
in part because of the difficulty of experimentally creating 
unfamiliar categories that are also naturalistic and not 
based on imposed rules. Here we show that human observers 
acquire informative fragments during the initial learning 
of unfamiliar categories. We created new, but naturalistic, 
classes of visual objects using a novel 'virtual phylogenesis' 
(VP) algorithm that simulates key aspects of how biological 
categories evolve. Subjects were trained to distinguish two 
of these classes using randomly drawn whole exemplar objects, 
not fragments. We hypothesized that if the visual system 
learns informative object fragments during category learning, 
then subjects must be able to perform the newly learned 
categorization using only the fragments as opposed to whole 
objects. We found that subjects were able to successfully 
perform the classification task using each of the informative 
fragments by itself, indicating that the visual system learns 
the fragments even when the task does not explicitly require 
fragment learning. Subjects performed at chance levels using 
comparable, but uninformative, fragments. Our results not only 
reveal that novel categories can be learned by discovering 
informative fragments, but also introduce and illustrate the 
use of VP as a versatile tool for category learning research.