Main content



Loading wiki pages...

Wiki Version:
## Data The data used for all analysis and modelling is contained in cleaned_data.csv in long form (and "img/cleandats.RData"). The data transformations and exclusions necessary to re-obtain this from the raw data set are run in 1-extract.R (the first uncommented lines of code). The key for each column is below: - s is "subject", indexes participant number - sess is "session", indexes which session it was for the participant (1,2 or 3) - block indexes which block it was for that session (one, two, or three) - cond indexes which experimental condition it was (Low Reliability= AUTO_L, High Reliability = AUTO_H, Manual=MANUAL) - failtrial indexes whether the automation was correct or incorrect. nonf (non-failure) for automation correct, fail for automation incorrect - failtrial_H is a redundant column indexing whether automation would have been in a failure in high-reliability conditions (and so is identical to failtrial for hr trials). Can be safely ignored. - trialnum is the number of the trial within a particular block of a particular session - S is stimulus type (c for aircraft in conflict, n for nonconflict) - R is response type (C for conflict, N for nonconflict) - RT is response time in seconds ## Script Explanations The manifest results were generated using 2-results_writeup.Rmd (with wording edits in ms word). The tables in the supplementary materials were generated with 2.5-supplementary_writeup.Rmd. The cognitive model reported in text was specified in R/3-model_specification_A.R. Originally, a model with a wider prior on A was fitted (R/3-model_specification.R), but this would not converge for two participants. Model results are generated in R/4-model_results_writeup.Rmd, with pre-computing of some results taken care of in 5-individual_diffs.R and 5-posterior_exploration.R. Scripts ending with _27 are similar analyses to those reported in text, but with the extra 3 participants that we accidentally tested beyond the full counterbalance. ## Loading pre-computed model outputs In the scripts, there is a special load function (loadpath()) that I use to get pre-computed modelling results (.RData files) from my dropbox. This function will not run and so is commented out. Instead, download pre-computed results from this OSF page ( and load them into R using the standard load() function. ## Response to reviewers Some additional analyses were included in response to reviews. Specifically, we reanalysed individual-difference correlations with accuracies transformed to the probit scale, and mean RT transformed to mean[log(RT)]. In addition, we made nicer looking plots, and examined overall accuracy across conditions (marginalized over automation correct/automation incorrect trials). We also analysed the test-reliability and internal consistency of the trust in automation scale. These additional analyses are available in For convenience this also includes a precomputed object saving the new (transformed scale) model-based correlation analysis results (which run slowly): "model_correlations_A_lb_TRANSFORMED.RData"
OSF does not support the use of Internet Explorer. For optimal performance, please switch to another browser.
This website relies on cookies to help provide a better user experience. By clicking Accept or continuing to use the site, you agree. For more information, see our Privacy Policy and information on cookie use.

Start managing your projects on the OSF today.

Free and easy to use, the Open Science Framework supports the entire research lifecycle: planning, execution, reporting, archiving, and discovery.