Category learning often involves selective attention to category relevant information, which may result in learned inattention to category irrelevant information. This learned inattention is a cost of selective attention. In the current research, the cost of attention was used as an indicator of category learning. Participants were given a category learning task, and the amount of supervision given to them was manipulated. Along with behavioral data, recorded eye movements during the task showed signature patterns of learning via a cost of attention. In addition, a simple neural network (perceptron) was able to use these eye-tracking data to predict success in learning. Thus, the observed attentional pattern – the cost of selective attention – was proposed as an indicator of category learning.