Figure 7

Two-sample KS statistic. Here we compare the goodness of fit offered by both the EPR and our Recency model with both the empirical datasets. Our analyses suggest that the recency effect is more noticeable in specific regions of the rank space. For this reason, we tested the same \(K_{f}\) distribution hypothesis for increasingly larger \(K_{s}\) ranges. In other words, this test evaluates the distance between the empirical and synthetic distribution of the \(K_{f}\) ranks of the visited locations up to a given \(K_{f}\). \(\theta_{D1}\) and \(\theta_{D2}\) correspond to the EPR parameters vector as empirically estimated from D1 and D2 respectively, whereas \(\mathrm{EPR}(\theta)\) represents the synthetic data produced by the EPR model using the parameters vector θ. Additionally, we applied the same approach to both empirical datasets to serve as a baseline for comparison.