Effects of Image Presentation Highlighting and Accuracy on Target Category Learning

Abstract

This study alters various exemplar presentation parameters to determine their effects on human online category learning for a future system that combines humans and computer vision (CV). Online category learning is necessary in this system because we envision that humans will need to provide input to assist CV modules in determining category labels without reducing throughput and without necessarily having expert knowledge of each category. In our study, subjects participated in a Rapid Serial Visual Presentation paradigm in which they were asked to determine the target category from highlighted exemplar images interspersed among distractor images. In Experiment 1, the highlighting method was varied among four options and a negative (no-label) and positive (explicit, text-based) control. In Experiment 2, label accuracy was altered by incorrectly labeling some distractor and exemplar images. In both experiments, there were three levels of difficulty that varied the similarity between distractor and exemplar images. The results show that most highlighting methods resulted in equivalent accuracy to the positive control, but certain modalities were more effective at varying difficulty levels. In addition, the subject accuracy was sensitive to distractors highlighted as targets, but not to nonhighlighted exemplars. Our results indicate that human online category learning can be optimized for human–system interaction.