## 4 Discussion  
Our most important finding is that the test-retest reliability of a specific version of AEFEA technology, the FR8, is very high; however, it is not perfect. Having analyzed the same set of facial video recordings by FR8 twice (test\({}_{1}\) and test\({}_{2}\)), we found that in 12 of the 60 cases, test\({}_{1}\)-test\({}_{2}\) differences arose. These differences appeared in all eight of the emotion dimensions, in up to 40% of the data points altogether, but were very small in their absolute values. This suggests a remarkably high test-retest reliability as compared with human coders [14], but lags behind a _perfect_ test-retest reliability, which is often explicitly or implicitly expected from algorithmic AEFEA technologies [41]. Another set of analyses (test\({}_{3}\)) of those facial video recordings which did not initially show total test\({}_{1}\)-test\({}_{2}\) identity led to further test-retest convergence or stabilization in five cases. In the seven remaining cases, however, there were both test\({}_{1}\)-test\({}_{2}\) and test\({}_{2}\)-test\({}_{3}\) differences between the individual data series. From the perspective of utilizing AEFEA technology in screening, diagnosis, and other health technologies, it is of special importance that our analyses did not reveal any cross-group difference in test-retest reliability. Data series from neurotypical children and from children with ASD were very similarly prone to imperfect test-retest reliability and showed very similar stabilization tendency in proportional cases. Additionally, a similar proportion of cases in both groups was left non-stabilized after three analyses. The pattern we found in the cross-group and cross-emotion differences in emotion intensities fits well with the series of previous findings on emotional display in ASD [53; 56; 70]. To the best of our knowledge, the test-retest reliability of AEFEA techniques has not been explored so far; therefore, these findings are unprecedented in the literature. We address four questions: (1) Is it possible that the findings on imperfect test-retest reliability are artefacts? (2) If they are not artefacts, what may cause the imperfect test-retest reliability? (3) What factor(s) may determine which cases or data series are affected by it? (4) Does imperfect test-retest reliability influence the fundamental results? In our view, it is crucial to consider the issue of whether the above findings may be artefacts. We see two factors that could potentially lead to the emergence of spurious results. Firstly, non-sufficient hardware performance in the course of FR8 analyses might--as pilot analyses we ran suggested--lead to noise in the emotion intensity data that could deteriorate test-retest reliability. However, the hardware configuration we used considerably surpassed that recommended by [PERSON] for using FR8. In addition, the partial test-retest convergence of subsequent data series could not be explained by such a factor. Software updates of FR8, arriving between subsequent analyses and changing the input/output mapping of the system, represent another candidate. These could explain both test-retest differences and their later partial decrease (assuming subsequent updates converge towards a stable mapping). Updates, however, were turned off for FR8 during and between analyses. A \"software update effect\", moreover, could not explain why some data series converged in subsequent analyses while some failed to stabilize within two subsequent comparisons. Finally, we note that data completeness in our data series was fairly good, as compared with other recent studies [12]. Even if it were lower than usual, it would be surprising and methodologically significant if the sparsity of data could lead to imperfect test-retest reliability. The following question arises: which feature of FR8 explains its imperfect test-retest reliability? In this study, we treated the FR8 system as a black box. We refrained from reverse-engineering it and from speculating about possible specific causes.