Computerized neglect tests could significantly deepen our disorder-specific knowledge by effortlessly providing additional behavioral markers that are hardly or not extractable from existing paper-and-pencil versions. This study investigated how testing format (paper versus digital), and screen size (small, medium, large) affect the Center of cancelation (CoC) in right-hemispheric stroke patients in the Letters and the Bells cancelation task. Our second objective was to determine whether a machine learning approach could reliably classify patients with and without neglect based on their search speed, search distance, and search strategy.

We compared the CoC measure of right hemisphere stroke patients with neglect in two cancelation tasks across different formats and display sizes. In addition, we evaluated whether three additional parameters of search behavior that became available through digitization are neglect-specific behavioral markers.

Patients’ CoC was not affected by test format or screen size. Additional search parameters demonstrated lower search speed, increased search distance, and a more strategic search for neglect patients than for control patients without neglect.

The CoC seems robust to both test digitization and display size adaptations. Machine learning classification based on the additional variables derived from computerized tests succeeded in distinguishing stroke patients with spatial neglect from those without. The investigated additional variables have the potential to aid in neglect diagnosis, in particular when the CoC cannot be validly assessed (e.g., when the test is not performed to completion).