Computational methods for integrative evaluation of confidence, accuracy, and reaction time in facial affect recognition in schizophrenia

Abstract

People with schizophrenia (SZ) process emotions less accurately than do healthy comparators (HC), and emotion recognition have expanded beyond accuracy to performance variables like reaction time (RT) and confidence. These domains are typically evaluated independently, but complex inter-relationships can be evaluated through machine learning at an item-by-item level. Using a mix of ranking and machine learning tools, we investigated item-by-item discrimination of facial affect with two emotion recognition tests (BLERT and ER-40) between SZ and HC. The best performing multi-domain model for ER40 had a large effect size in differentiating SZ and HC (d = 1.24) compared to a standard comparison of accuracy alone (d = 0.48); smaller increments in effect sizes were evident for the BLERT (d = 0.87 vs. d = 0.58). Almost half of the selected items were confidence ratings. Within SZ, machine learning models with ER40 (generally accuracy and reaction time) items predicted severity of depression and overconfidence in social cognitive ability, but not psychotic symptoms. Pending independent replication, the results support machine learning, and the inclusion of confidence ratings, in characterizing the social cognitive deficits in SZ. This moderate-sized study (n = 372) included subjects with schizophrenia (SZ, n = 218) and healthy controls (HC, n = 154).

Keywords: Machine learning; Neural networks; Psychosis; Social cognition.