Learning in the Context of Partial Information

In our everyday lives, there are often more aspects of the environment than we can reasonably attend. As a consequence, we selectively attend to some aspects of the environment -- usually those aspects which are most relevant to our goals -- and ignore aspects that are deemed irrelevant. It follows then, that using selective attention can limit a learner's impression of an environment, because the information that is stored in memory is only a biased sample or partially encoded version of that environment. However, many classic models of category learning make a simplifying assumption that dimensions of information are perfectly encoded. Here, we investigate the merits of this assumption by evaluating categorization and memory performance in a categorization paradigm designed to discern learning strategies and partially encoded representations. We demonstrate how particular learning strategies and corresponding representations can influence generalization to novel stimuli presented in a testing phase. We build upon existing models of categorization to illustrate how partial encoding can account for differences in learning.