Triggering mechanisms for motor actions: The effects of expectation on reaction times to intense acoustic stimuli.
The file contains the data obtained in 1 experiment using a simple reaction time task. The statistical data analysis was conducted in R (R Core Team, 2016) using the lmer function from the lmerTest package (Kuznetsova, Brockhoff, & Christensen, 2017). The analysis was separated into two phases. First we analyzed the median RT using a 2 (Predictability: Predicable vs. Unpredictable) x 2 (IS intensity: soft IS vs. SAS) x 3 (IS time: 1st, 2nd, and 3rd marker) linear mixed model. For this analysis, we employed the Sattethrwaite approximation (Satterthwaite, 1941) to calculate F-tests and estimate p-values for the main effects and their interactions. Predictability, IS intensity and IS time were treated as fixed factors, whereas participants were treated as a random factor into the model. The percentage of false starts was analyzed using a permutational analysis of variance using the ezPerm function (ez Package), and had Predictability and IS time as factors. In the second phase of our analysis, we fitted cumulative distribution functions (CDF) to the data of all participants in both blocks of trials when the IS was intense (SAS). Next, we recorded the 35th and 65th percentiles of each CDF for each participant. These values were then entered into a 2 (Predictability: Predicable vs. Unpredictable) x 2 (Percentile: 30th vs. 60th) x 3 (IS time: 1st, 2nd, and 3rd marker) linear mixed model. Predictability, RT Percentile, and IS time were treated as fixed factors, whereas participants were treated as a random factor into the model.
Steps to reproduce
Data contained in each file is in the long format. You can load each file into R and perform the corresponding analysis. For the CDF analysis you must subset the data so that only the 35th and 65th are entered into the model. Please email me (firstname.lastname@example.org) if you need help running the analyses and I will provide my code with further instructions.