index | term | B | SE | t | df | Pr(>|t|) |
---|---|---|---|---|---|---|
1 | (Intercept) | 6.0775 | 0.1605 | 37.8551 | 139.448161 | 0.0 |
2 | osmsos | 0.6379 | 0.2513 | 2.538 | 136.000295 | 0.0123 |
3 | trialTypepofa | 0.0138 | 0.0214 | 0.6454 | 27049.127397 | 0.5187 |
4 | TrialNum | 0.1126 | 0.0467 | 2.4106 | 136.999935 | 0.0173 |
index | sigma | logLik | AIC | BIC | REMLcrit | df.residual |
---|---|---|---|---|---|---|
1 | 1.760331 | -54598.524964 | 109213.049928 | 109278.774094 | 109197.049928 | 27316 |
Linear Mixed Model Fit by REML (Laplace Approximation) ['lmer']. This table summarizes effects on onset error rate with trial number, operating system, and stimulus.
Participants with 'Dotloc' or 'Stimulus' Onset Error median above 3SD (n = [17, 25, 49, 54, 59, 77, 80, 89, 112, 123, 138, 140, 150, 153, 180, 182, 185, 212, 221, 248, 250, 256, 262, 269, 292, 294, 298, 319, 999999, 111111, 156], 18.7%) were excluded from analysis (see methods). We employed linear mixed effects models with random intercepts and slopes using the lmer() function in the lme4 R package (R Core Team, 2013; Bates, Mächler, Bolker, & Walker, 2015). For our model, Operating System, Stimulus (IAPS, POFA), and Trial. were included as fixed effects. Random effects for Trial, and Participant. were included in the model to account for their respective variation in their slopes and intercepts. Stimulus Onset Error was used as the outcome measure.
For our analysis of Stimulus Onset Error, our results revealed a statistically significant effect of Operating System (t = 0.6379, SE = 0.2513, p = 0.0123), no statistically significant effect of Stimulus (t = 0.0138, SE = 0.0214, p = 0.5187), a statistically significant effect of Trial (t = 0.1126, SE = 0.0467, p = 0.0173).