  A wide range of neurological and cognitive disorders exhibit distinct
behavioral markers aside from their clinical manifestations. Cortical Visual
Impairment (CVI) is a prime example of such conditions, resulting from damage
to visual pathways in the brain, and adversely impacting low- and high-level
visual function. The characteristics impacted by CVI are primarily described
qualitatively, challenging the establishment of an objective, evidence-based
measure of CVI severity. To study those characteristics, we propose to create
visual saliency maps by adequately prompting deep vision models with attributes
of clinical interest. After extracting saliency maps for a curated set of
stimuli, we evaluate fixation traces on those from children with CVI through
eye tracking technology. Our experiments reveal significant gaze markers that
verify clinical knowledge and yield nuanced discriminability when compared to
those of age-matched control subjects. Using deep learning to unveil atypical
visual saliency is an important step toward establishing an eye-tracking
signature for severe neurodevelopmental disorders, like CVI.
