Data and analysis scripts for "Unbiased post-error slowing in interference tasks: A confound and a simple solution
| dc.contributor.author | Derrfuss, Jan | |
| dc.contributor.author | Danielmeier, Claudia | |
| dc.contributor.author | Klein, Tilmann A. | |
| dc.contributor.author | Fischer, Adrian G. | |
| dc.contributor.author | Ullsperger, Markus | |
| dc.date.accessioned | 2021-02-26T08:29:00Z | |
| dc.date.available | 2021-02-26T08:29:00Z | |
| dc.date.issued | 2021 | |
| dc.description | To reproduce the analyses reported in the article, run the Jupyter notebooks included in the folders "berrypy_mean_close_only" (for analyses of imbalances in trial types and mean-based analyses), "berrypy_median_close_only" (for median-based analyses) and "bias_analysis_synthetic_data" (synthetic data analysis for Figure 8). The folder "berrypy_mean_far_only" includes the supplementary analysis of the far trials. BerryPy is available for download at https://github.com/jderrfuss/berrypy. | de_DE |
| dc.description.abstract | We typically slow down after committing an error, an effect termed post-error slowing (PES). Traditionally, PES has been calculated by subtracting post-correct from post-error RTs. Dutilh and colleagues (2012), however, showed PES values calculated in this way are potentially biased. Therefore, they proposed to compute robust PES scores by subtracting pre-error RTs from post-error RTs. Based on data from a large-scale study using the flanker task, we show that both traditional and robust PES estimates can be biased. The source of the bias are differential imbalances in the percentage of congruent vs. incongruent post-correct, pre-error and post-error trials. Specifically, we found that post-correct, pre-error and post-error trials were more likely to be congruent than incongruent, with the size of the imbalance depending on the trial type as well as the length of the response-stimulus interval (RSI). In our study, for trials preceded by a 700-ms RSI, the percentages of congruent trials were 62% for post-correct trials, 66% for pre-error trials and 56% for post-error trials. Relative to unbiased estimates, these imbalances inflated traditional PES estimates by 37% (9 ms) and robust PES estimates by 42% (16 ms) when individual-participant means were calculated. When individual-participant medians were calculated, the biases were even more pronounced (40% and 50% inflation, respectively). To obtain unbiased PES scores for interference tasks, we propose to compute unweighted individual-participant means by initially calculating mean RTs for congruent and incongruent trials separately, before averaging congruent and incongruent mean RTs to calculate means for post-correct, pre-error and post-error trials. | de_DE |
| dc.identifier.uri | http://open-science.ub.ovgu.de/xmlui/handle/684882692/83 | |
| dc.identifier.uri | https://doi.org/10.24352/ub.ovgu-2021-024 | |
| dc.language.iso | en | de_DE |
| dc.publisher | Otto-von-Guericke Universität Magdeburg | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/4.0/ | |
| dc.subject | Performance Monitoring | de_DE |
| dc.subject | post-error slowing | de_DE |
| dc.subject | unbiased post-error slowing | de_DE |
| dc.title | Data and analysis scripts for "Unbiased post-error slowing in interference tasks: A confound and a simple solution | de_DE |
| dc.type | Dataset | de_DE |