Quantifying sources of within-subject variance across different behavioural tasks

Reaction time (RT) series from any behavioural task show large fluctuations from trial to trial. These fluctuations are characterised by temporal trends such as positive autocorrelations between subsequent trials. In typical experimental paradigms, the trial-to-trial fluctuations are ignored, and RTs are summarised into conditional means, which are then statistically compared on the group level. However, at the level of individual participants, it often remains unknown which part of the total trial-to-trial variance is driven by the conditional manipulations. In the current study, we quantified sources of within-participant variance in RT across archival datasets. Specifically, we determined the relative contributions of experimental manipulations and sequential effects, split into trial-by-trial autocorrelations and blockwise trends. We quantified the trial-to-trial variance of RT with general linear models on the individual participant data. Results from 16 datasets (N = 1474) from perceptual and cognitive control tasks show that the conditional, autocorrelative, and blockwise trend factors explained similar amounts of variance in trial-to-trial RTs. Furthermore, we examined individual differences in explained variance with between-subject correlations between the amount of explained variance and performance. RT variability correlated positively with the amount of variance explained by the conditional and blockwise trend factors, but negatively with variance explained by the autocorrelative factors. Overall, experimental conditions only explained a small proportion of the total variance, and large parts of individual trial-by-trial variance remained unexplained by the investigated factors.