Studies of category learning have revealed individual differences in decision-making, such that the same stimulus may be categorized differently across individuals. Modeling accounts have explained these differences in terms of how attention weights are distributed across stimulus dimensions that distinguish between category responses. These weights are typically assumed to reflect an individual’s beliefs about which dimensions are most relevant to their goals. The current work investigates the possibility that instead of being purely strategic, attention weights are constrained by what was encoded into memory during learning. Participants (N=120, age 18-25) completed a category learning task while gaze was recorded as an exogenous measure of attention. Model-based analyses using gaze to predict behavior revealed that accounting for partially-encoded representations was necessary for predicting individual differences in feature memory and categorization.