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/distractor 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.
