The presence and degree of category-selective responses in the human brain remains a central research question in visual neuroscience. Evidence for category-selectivity in higher-level vision stems from neuroimaging studies of healthy participants, and converging evidence in patients after brain injury. However, the neuropsychological literature often focuses on either in-depth analysis of single case-studies or behavioural testing of one category, for example faces or words.
Here we adopt a novel approach to studying higher-level vision after brain injury by exploring the largest sample of posterior cerebral artery stroke patients currently available (n = 64). Patients were tested using an in-depth behavioural battery encompassing both low-level visual tests and higher-level visual tests of word, object, and face processing.
A data-driven approach (principal component analysis) was used to establish a pattern of co-occurrence within higher-level vision. The data revealed two principal components underlying patients’ performance. The first component included tests with a non-verbal (picture) input, including face and object processing. The second component included tests with a verbal (written word) input/output. Using a data-driven approach to study higher-level vision after brain injury suggests that patient’s behavioural performance does not reflect strict category-selective responses.