Target Class Induction through Image Feedback Manipulation in Rapid Serial Visual Presentation Experiments

Prior research has shown the utility of labeling images by rapidly displaying them to humans via a Rapid Serial Visual Presentation (RSVP) paradigm, classifying the resulting neural data, and integrating the results with computer vision. However, there is currently very little research on providing feedback to the human interacting with one of these systems. To explore this question, an RSVP task was developed to examine the effectiveness of feedback to induce changes in target category in near-real time. Three different factors involved in image presentation were explored: image presentation duration, target/distract or similarity, and feedback modality. Significant, nonlinear changes in performance were related to these independent variables. These results demonstrate the complexity inherent to human category learning and will guide future use of image presentation parameters to optimize human performance within a human-assisted computing system that is focused on image analysis.