1. Feedforward Eye-Tracking for Training Histological Visual Searches Andrew T. Duchowski COMPUTER SCIENCE, CLEMSON UNIVERSITY duchowski@acm.org, Abstract Our work serves as an example of the impact of feedforward eye-tracking on the teaching of Histology to students. Specifically, we aim to evaluate whether or not the use of a pattern of visual inspection (“scanpath”) can improve the accuracy and speed of a subject when said subject is performing a visual search task, and if the use of scanpaths can improve the instruction of Histology as a whole. The results show that those who have been shown scanpaths in their instruction are indeed both more accurate and faster to completion than those who have not been shown scanpaths. Introduction & Background Complementation of structure and function is a core concept in the biological sciences. Students who develop a comprehensive understanding of structure and function complementarily through Histology are better able to integrate concepts of biology across multiple scales of inquiry. Histological proficiency is assessed through a combination of standard written tests and “practical examinations” where students are allowed a limited time to inspect an image or microscope field and make an identification. The development of effective visual search strategies is thus critical to student achievement. Currently, most histology instruction still employs classical tissue staining methods that have not changed over many decades. Pani et al. [2007] have described the development of histological acumen amongst undergraduates and noted wide variation in the specific features used by the tested students for the identification of tissues or organs. Despite shared knowledge of a common syntax and vocabulary for histological identification, the students used different visual cues for their identification, with varying success. Krupinski et al. [2006] showed that expert observers spent less time scanning the image and identified regions of interest quickly. Students viewing the same images had a less-directed search pattern with more saccades, but failed to identify the same regions of interest. Methodology 33 participants of mixed demographics viewed eight histological slides. The participants viewed the images in a unique order. The participants were from three distinct groups; anatomy students, computer science students, and miscellaneous others Clemson students and faculty. The participants were given training in Histology and were tasked with finding cells marked with the immuno-gold staining technique. Half of the participants were also shown a video of an expert’s scanpaths while the other half were not. Participants were then shown 8 images and asked to choose the cells that they believed were marked. During this task their eye movements and choices were recorded. School of Computing, Clemson University Figure 1: Screenshots of the experimental equipment and the type of subject the tissue was taken from. This includes a calf (top left), an experiment in progress (top right), and the machine and program used in the experiment (bottom left & bottom right). Results The results were analyzed along two metrics; time to completion and accuracy. The accuracy was measured by taking the ratio of true and false positives for both those with the training video and those without. Time to completion was measured by taking the mean gaze time per image and the mean fixations per group. The experiment was between subjects and the accuracy and time to completion were compared between those who had watched the training video and those who had not. These two subgroups were spread indiscriminately among the three participant groups. There was a significant difference (p < 0.01) in the mean gaze time per image and a marginally significant difference (p < 0.05) in the mean fixation duration per subgroup. The results taken from participant groups showed that there was no significant difference in the mean gaze time per image between the three groups or the mean number of fixations but that there was a significant difference (p < 0.01) in the mean number of fixation durations per group. The group that consisted of the anatomy students had significantly shorter durations of fixations than the other two groups. Figure 2: Example of a Gaze Plot resulting from this experiment. The Gaze Plots shown as composed of fixations (circles) and saccadas (the lines). From this figure, it is easy to see that the participant with the video (blue) has far less fixations than the participant without the video (red). Discussion Selected References KRUPINSKI, E. A., TILLACK, A. A., RICHTER, L., HENDERSON, J. T., BHATTACHARYYA, A. K., SCOTT, K. M., GRAHAM, A. R., DESCOUR, M. R., DAVIS, J. R., AND WEINSEIN, R. S. 2006. Eye-movement study and human performance using telepathology virtual slides. Implications for medical education and differences with experience. Human Pathology 37, 1543–1556. PANI, J. R., CHARIKER, J. H., CLAUDIO, N. M., AND FELL, R. D. 2007. Diagnostic visual information in the use of microscopes in histology. In Proceedings of the 29th Annual Meeting of the Cognitive Science Society As the results of this experiment have shown, of the two groups, those with the training video and those without, the group who were shown the training video were significantly faster to completion and significantly more accurate than those without the video. Of the three groups; those recruited from an anatomy class, those recruited from Computer Science students, and miscellaneous others, there was no significant difference in the three groups in the mean gaze time per image nor was there any significant difference in the mean fixations. There was significant difference in the mean fixation durations for the three participant groups though. Ultimately, those who received training with the video (taken from all three groups) were faster and more accurate despite the level of previous experience. In fact, the only difference between groups was the anatomy students who had slightly shorter fixation durations, probably due to familiarity with the images. John E. Ingram COMPUTER SCIENCE THE UNIVERSITY OF THE SOUTH ingraje0@sewanee.edu Sarah A. Vitak BIOLOGY SCRIPPS COLLEGE svitak5916@ScrippsColl ege.edu Study Limitations Due to the nature of the pre-task training, there may have been a slight ceiling effect in the participants as all of the three participant groups were able to complete the task around the same time and there was no significant difference in the number of correct answers between the with-video and no- video groups. There was limited access to participants. Hopefully a more complete version of this study, with actual histology students will be possible in the future.