Prediction of social behavior in autism spectrum disorders: Explicit versus implicit social cognition

Abstract

Difficulties with social communication and interaction are a hallmark feature of autism spectrum disorder. These difficulties may be the result of problems with explicit social cognition (effortful and largely conscious processes) such as learning and recalling social norms or rules. Alternatively, social deficits may stem from problems with implicit social cognition (rapid and largely unconscious processes) such as the efficient integration of social information. The goal of this study was to determine how problems in explicit and implicit social cognition relate to social behavior in 34 youth with autism spectrum disorder. We measured aspects of implicit and explicit social cognition abilities in the laboratory using behavioral, cognitive, and brain (electrophysiological) measures. We then used those measures to predict "real-world" social behavior as reported by parents, clinicians, and independent observers. Results showed that overall better aspects of implicit and explicit social cognition predicted more competent social behavior. In addition, the ability to fluidly integrate social information (implicit social cognition) was more frequently related to competent social behavior that merely knowing what to do in social situations (explicit social cognition). These findings may help with the development of interventions focusing on improving social deficits.

Keywords: autism spectrum disorder; electroencephalography; event-related potential; partial least squares regression; social cognition; social perception.

Figure 1.

The point at which the Root Mean Square Error of Prediction (RMSEP) value is the lowest determines the number of components extracted for that analysis. A) RMSEP plot depicting the RMSEP value for factor solutions of 0 to 5 components in the PLSR analysis predicting z-scored ADOS CSS. B) RMSEP plot depicting the RMSEP value for factor solutions of 0 to 5 components in the PLSR analysis predicting z-scored SCQ. C) RMSEP plot depicting the RMSEP value for factor solutions of 0 to 5 components in the PLSR analysis predicting z-scored SIOS Low-Level Interaction. D) RMSEP plot depicting the RMSEP value for factor solutions of 0 to 5 components in the PLSR analysis predicting z-scored SIOS Positive Interaction; for this model, the RMSEP value does not decrease after the intercept and thus this model is not interpretable.