Rothkegel et al. 32 the empirical data with a similar progresson of lines and a differentiation between pre- trial fixation times. However, the early CFB on the second fixation was too small in all experiments, i.e. the distance to center in all simulations was too large. Likelihood . We computed the likelihood for each model given the empirical data (Sch¨ utt et al., 2017). As explained above all model parameters were estimated on Fixation 2 to 4 of half of the participants ( N = 5–20) and a quarter of the images ( N = 30) for each experiment. The likelihood was evaluated on the other half of participants and on the rest of the images ( N = 90) for Fixation 2 to 10. All models were computed on the same grid of 128 × 128 grid cells. The grid cells were normalized to obtain probabilities. This translates to a likelihood for a random fixation on a uniform map of 1 / (128 × 128) = 2 − $^{14}$. Thus the information gain for a fixation i at point ( x , y ) of a model map u can be computed as The computed value represents the information gained relative to a random process that generates a uniform distribution of fixations. Figure 10 shows the information gain of all models for Fixation 2 to 10. The Density model (yellow curve) predicts fixation locations well, but the information gained from this model decreases over time. Thus saliency models could explain less information for later fixations than for the initial ones. The CenterBias explained initial fixations similarly well as the density model, but the gained information declined towards zero for later fixations. Both stationary models were outperformed by all dynamic models on all fixations except for the second. For this second fixation only the SceneWalk StartMap model (green curve) generated higher information gain than the static models in experiment 1 and 3. The original SceneWalk model (pink curve) performed less good on the second fixation, but gain$_{i}$ = log$_{2}$( u [ x$_{i}$, y$_{i}$ ]) + 14 . (2)