## 4 Discussion
For the same reasons, the system is entirely fit to run experiments with a device measuring live neural activity. As the maze is a discrete, controlled arena, and the environment is enclosed and controlled, it is very easy to use helmets or devices for transcranial magnetic/electric stimulation for the participants. Additionally, sensitivity and quality of human brain activity recording have considerably improved recently in freely moving subjects, by using controlled environments (see for example [34]). These novel preparations can easily be used in our new system to provide correlated measures of neural activity while the subject is actively navigating. Second, the system is multipurpose to implement many other spatial navigation tests previously reported, such as path integration or hidden goal tests. Third, it is portable, and finally, it allows longitudinal studies by only changing the spatial cues and the path to the goal, e.g., changing the map. Additionally, being a physical test in real space confers an intrinsic advantage over VR systems to evaluate spatial navigation, which might induce dizziness (mainly to elder people) especially when using VR glasses [35; 36]. Besides that, distinct usage rates between the young and elders might reflect disparity in the familiarity in the use of devices [37] rendering it unreliable when comparing different age groups in a study. Last but not least, elder people might have higher disaffection with a new virtual challenge than young people, compared to a new physical challenge, where both age groups may be comparable [38; 39]. In our validation study, we have seen that our system yields consistent results with what is expected in known spatial navigation tests such as hidden goal test or path integration. Our measurements of the maze test give us information about three different dimensions: how accurately are participants predicting their own position, how effective is their path-finding, and the total amount of time they take to arrive at the goal. Several difficulties arise when validating this test. Firstly, to our knowledge, there is no other human spatial navigation test consisting of a complex real-space maze measuring subjects' performance. Secondly, these mentioned dimensions are composed of several processes (not purely a path integration task, nor a hidden goal task). And lastly, two of these dimensions (prediction and decision error) are, to a certain degree, independent from each other, while total time may influence both errors (more time in the maze can result in more movements, and more movements may give rise to more errors). With all this in mind, validation of this test must come from how our results fit within both the literature and the predictions we made based on it. First, decision error and prediction error do not correlate (Rho = 0.13), as expected. Second, both errors show a low to moderate correlation with total time (Rho = 0.37 for prediction error and Rho = 0.45 for decision error) showing a clear influence, but not a strong correlation. Third, low to no correlation with tests measuring different variables. Fourth, organizing scores by quartiles we can divide our sample by a good/bad performance in each error. Since they are different dimensions, there are participants in every resultant subdivision (low prediction error with low decision error, high prediction error with low decision error, etc.). As expected, making good predictions of their position does not mean good path-finding (61.5% good path-finding), people with bad path-finding indistinctly make good or bad predictions (50%) but people with bad predictions rarely have a low path-finding error (20%). So, while these dimensions are different and do not correlate, knowing one-self's position increase the chance of making good path-finding decisions. Furthermore, we found a trend of a positive correlation between the time spent to solve the maze and the error in the real-space path integration test.