This site uses cookies

By using this site, you consent to our use of cookies. You can view our terms and conditions for more information.

Quantifying sources of within-subject variance across different behavioural tasks

Dr. Marlou Perquin
Bielefeld University
Prof. Tobias Heed
Prof. Christoph Kayser

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.



Reaction time
Trial-to-trial fluctuations
More data to explain more variance? Last updated 1 year ago

Very interesting analysis. At the end you mention wanting to add more data and I'm wondering if you have an idea of the total amount of data that is needed for the analysis. To me it seems like that adding more data is not all that is needed to explain more of the variance and that there are other factors to consider. Also, I want to say that I hav...

Lori Mahoney 0 comments
Cite this as:

Perquin, M., Heed, T., & Kayser, C. (2021, July). Quantifying sources of within-subject variance across different behavioural tasks. Paper presented at Virtual MathPsych/ICCM 2021. Via