## Data collection

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## Data processing

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## Model

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 |

**Table**: Linear Mixed Model Regression for Stimulus Onset Error (N = 138)

index | sigma | logLik | AIC | BIC | REMLcrit | df.residual |
---|---|---|---|---|---|---|

1 | 1.760331 | -54598.524964 | 109213.049928 | 109278.774094 | 109197.049928 | 27316 |

**Table**: 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.

Each individual subjects individual trend is indicated here. Participants with 'Dotloc' or 'Stimulus' onset error rate 3 SD above the median are indicated here with a semi-opaque line. The graph has been clipped at y = 200 for displaying purposes.

Data is either unbinned (c,d) or binned into 33 discrete evenly-sized groups (a,b). The model is still fit using the original data. No participants have been excluded for this analysis. The binned graph has been clipped at y = 1000 for displaying purposes.

The Normal Q-Q plot compares the standardized residuals against the theoretical quantiles from a standard normal distribution. If the model residuals are normally distributed, then the points on this graph will be plotted in a generally straight line.