Predicting school achievement from self-paced continuous performance: Examining the contributions of response speed, accuracy, and response speed variability

Trial-to-trial fluctuations in self-paced performance have long been considered an important aspect of an individual’s performance. Whereas average response speed has been considered a cognitive factor indexing the speed of mental processing, response speed variability has been considered an energetic factor indexing an individual’s capability to sustain mental processes over prolonged time periods. Here we investigated whether there is an incremental contribution of response speed variability, compared to mental speed, in predicting school achievement. A sample of 89 individuals was tested with the Serial Mental Addition and Comparison Task (SMACT) twice within a retest-interval of three days. In addition to the conventional performance measures speed (MRT) and accuracy (error percentage, EP), we evaluated two intraindividual response speed variability measures, standard deviation (SDRT) and coefficient of variation (CVRT), with regard to their power to statistically predict secondary- and high-school achievement. In general, school performance was best predicted by MRT and not at all by EP. Response speed variability, especially CVRT, appeared to be a good predictor of school performance, especially mathematics performance. The combined intake of MRT and CVRT as predictors in a multiple linear regression model, however, did not yield additional predictive value compared to the single-predictor model that contained only MRT. A further interesting finding was that the performance measures were differentially predictive across genders. In sum, we suggest that response speed variability as indexed by CVRT is a candidate dimension for the assessment of sustained concentration performance. Before applying CVRT in practical assessment settings, however, additional research is required to elucidate effects of different task factors (e.g., task length, task complexity, content domain, etc.) on the predictive power of this performance measure.