Student Scholar Symposium Abstracts and Posters

Document Type


Publication Date

Fall 12-2-2020

Faculty Advisor(s)

Vincent Berardi


Visual processing in humans is done by integrating and updating multiple streams of global and local sensory input. Interaction between these two systems can be disrupted in individuals with ASD and other learning disabilities. When this integration is not done smoothly, it becomes difficult to see the “big picture”, which has been found to have implications on emotion recognition, social skills, and conversation skills. An example of this phenomenon is local interference, which is when local details are prioritized over the global features. Previous research in this field has aimed to decrease local interference by developing and evaluating a filter to help direct ASD patients towards normative processing of the global features in images. Within this process, this research focuses on whether an image’s spatial frequency was affected by the filter and how spatial frequency impacted the filter’s functionality. Spatial frequency can be defined as a measure of the periodic distribution of light versus dark in image. In this work, we isolated “hot spots”, which are areas in the image where the eye gaze of normative individuals fixated. Using the OpenCV package in Python, I implemented an algorithm to detect hotspots and draw a contour around each one. I then drew rectangles around the contours in each image and calculated the spatial frequency within each rectangle. Statistical analysis will reveal whether the spatial frequency of hot spots had an impact on the differences in normative and ASD fixations. We plan to use these findings to improve the image filter and conduct further research in this field.


Presented at the virtual Fall 2020 Student Scholar Symposium at Chapman University.