Mathematical Models of Behavioral and Neural Error Response for Applications in Psychosis Assessment

The aim of the present study was to develop competing mathematical models of within-person response time patterns before and after errors within a cognitive paradigm (flankers task) and evaluate their utility for schizophrenia classification (N=524). A secondary aim was to apply these models to EEG time series, focusing on areas in which event-related potentials (ERPs) connected to unconscious and conscious error monitoring and response (i.e., frontocentral and midline regions) are recorded. Models were chosen based on alternative mathematical constructions intended to capture the shape, magnitude, and timing of error response and monitoring. Candidates included linear, exponential decay, and damped oscillator models with post-error-varying parameters or shock components.

Person-average neural (ERPs) and behavioral (post-error slowing, accuracy, mean RT) measures failed to reach acceptable schizophrenia classification specificity and sensitivity, respectively, when used separately, but achieved around 70% sensitivity and specificity when used together. Parameters from piecewise linear and damped oscillator models of error/correct negativity signal and linear shock and oscillator models of error/correct positivity signal, when used alongside person-average behavioral measures, achieved 76-88% sensitivity and 71-76% specificity. Piecewise linear and damped oscillator RT models led to 72-74% sensitivity and specificity without using neural information, which would be of high practical utility given the minimal training and equipment required by the task. Notably, in contrast to the vast majority of past studies, this performance was achieved using human-interpretable machine learning in a heterogeneous sample including individuals with various forms of psychosis, their siblings, and individuals with other psychiatric syndromes. Model parameters thus facilitated ruling out not only genetically and diagnostically typical individuals, but also those who, despite not having schizophrenia specifically, may share genetic and/or environmental liabilities and even symptom overlap with those with schizophrenia. Examinations of parameter associations (e.g., with post-error accuracy) provided further evidence that models capture individual differences in functioning and signal promise for the development of neural-behavioral link functions.