Extraction of Emotional Information via Visual Scanning Patterns
Joshua W. Wade, Heathman S. Nichols, Megan C. Ichinose, Dayi Bian, Esubalew Bekele, Matthew A. Snodgress, Ashwaq Zaini Amat, Eric L. Granholm, Sohee Park, Nilanjan Sarkar · ACM Transactions on Accessible Computing · 2018
Emotion recognition impairment is a core feature of schizophrenia (SZ), present throughout all stages of this condition, and leads to poor social outcome. However, the underlying mechanisms that give rise to such deficits have not been elucidated and hence, it has been difficult to develop precisely targeted interventions. Evidence supports the use of methods designed to modify patterns of visual attention in individuals with SZ in order to effect meaningful improvements in social cognition. To date, however, attention-shaping systems have not fully utilized available technology (e.g., eye tracking) to achieve this goal. The current work consisted of the design and feasibility testing of a novel gaze-sensitive social skills intervention system called MASI-VR. Adults from an outpatient clinic with confirmed SZ diagnosis (n=10) and a comparison sample of neurotypical participants (n=10) were evaluated on measures of emotion recognition and visual attention at baseline assessment, and a pilot test of the intervention system was evaluated on the SZ sample following five training sessions over three weeks. Consistent with the literature, participants in the SZ group demonstrated lower recognition of faces showing medium intensity fear, spent more time deliberating about presented emotions, and had fewer fixations in comparison to neurotypical peers. Furthermore, participants in the SZ group showed significant improvement in the recognition of fearful faces post-training. Preliminary evidence supports the feasibility of a gaze-sensitive paradigm for use in assessment and training of emotion recognition and social attention in individuals with SZ, thus warranting further evaluation of the novel intervention.