Dynamics of task-based confidence in schizophrenia using seasonal decomposition approach

Abstract

Objective: Introspective Accuracy (IA) is a metacognitive construct that refers to alignment of self-generated accuracy judgments, confidence, and objective information regarding performance. IA not only refers to accuracy and confidence during tasks, but also predicts functional outcomes. The consistency and magnitude of IA deficits suggest a sustained disconnect between self-assessments and actual performance. The cognitive origins of IA are unclear and are not simply due to poor performance. We tried to capture task and diagnosis-related differences through examining confidence as a timeseries. Method: This relatively large sample (N = 171; Bipolar = 71, Schizophrenia = 100) study used item by item confidence judgments for tasks including the Wisconsin Card Sorting Task (WCST) and the Emotion Recognition task (ER-40). Using a seasonal decomposition approach and AutoRegressive, Integrative and Moving Averages (ARIMA) time-series analyses we tested for the presence of randomness and perseveration. Results: For the WCST, comparisons across participants with schizophrenia and bipolar disorder found similar trends and residuals, thus excluding perseverative or random responding. However, seasonal components were weaker in participants with schizophrenia, reflecting a reduced impact of feedback on confidence. In contrast, for the ER40, which does not require identification of a sustained construct, seasonal, trend, and residual analyses were highly comparable. Conclusion: Seasonal analysis revealed that confidence judgments in participants with schizophrenia on tasks requiring responses to feedback reflected diminished incorporation of external information, not random or preservative responding. These analyses highlight how time series analyses can specify potential faulty processes for future intervention.