Rothkegel et al. 24 course of the early CFB, we simulated scanpaths generated by different computational models for all experiments and compared simulated scanpaths to our empirical data. Additionally we computed the likelihood of the empirical data under each model (Sch¨ utt et al., 2017). Overall we compared five models. First, we simulated three control models: (i) a model that selects fixations proportional to the densities of the empirically observed fixations, (ii) a model which multiplies the fixation density with a Gaussian around the current fixation before selecting the next fixation, (iii) a model that selects fixations from a central activation map to produce a pure CFB. In a second step, we simulated two versions of the dynamical SceneWalk model (Engbert et al., 2015): (iv) the original model and (v) a version of the original model with a modulation of the activation map by a sudden image onset. For the sampling from a central activation map model (iii) and the extended SceneWalk model (v) parameters had to be estimated. We used a standard optimization algorithm (fminsearch) implemented in MATLAB (MATLAB, 2015) to obtain the parameters with maximum likelihood (Bickel & Doksum, 2015; Sch¨ utt et al., 2017) of fixations 2–4 of half of the participants (Exp. 1–4: N = 20 / 10 / 12 / 5) and a quarter of the images ( N = 30). We estimated parameters from the second to fourth fixation only for efficiency reasons and since DTC curves reached a stable value for later fixations. All models were implemented on a grid of 128 × 128 cells. For all models we normalized activation maps for target selection to a sum of one to obtain a probability value for each grid cell. For each empirical scanpath, we computed a simulated scanpath with the same number of fixations and fixation durations. Density Sampling . As the most straightforward statistical control, we simulated scanpaths by randomly sampling from a 2D density map of all fixations on a given image, i.e.