## 4 Discussion
Nevertheless, we briefly raise three possibilities. One is that artificial neural network (ANN) technology, which is a feature of FR8, may underlie our findings. ANNs \"naturally\" respond differently to repeated stimuli in the learning phase, and they also tend to converge onto more stable input/output mappings. In the case of such an ANN learning effect, the methodological consequences of imperfect initial test-retest reliability are still to be considered. Secondly, the \"continuous calibration\" feature of FaceReader arises as a candidate for changing analysis characteristics. In our understanding, continuous calibration does modulate emotion estimation output: it adjusts facial intensity values in an ongoing analysis continuously, as a function of previous intensity values in that analysis. However, FaceReader uses a fixed formula for this process--that is, if the detected raw intensities are stable across analyses, then continuous calibration in itself could not add \"noise\" to these. Thirdly, another, more \"profane\" possibility--suggested by our informal experimentation with this factor--is that hardware requirements for running FR8 might be underestimated by its producer, and insufficient hardware resources lead to unstable performance. However, this explanation would go against the fact that most data series showed either test-retest stability or stabilizing tendency across the subsequent re-analyses. In this study, 20% of our cases/initial data series were affected by nonperfect test-retest reliability. Empirical research using AEFEA could benefit from understanding which cases/data series are more prone to this than others. Although we examined several candidate subject- or data-related factors, we failed to identify any that could determine which subsets of cases showed perfect/imperfect test-retest reliability. The question of whether imperfect test-retest reliability of AEFEA technology substantially influences results is of crucial importance in a field of research where this issue has been ignored so far. We analyzed the patterns of cross-emotion and cross-group differences, both in the first and the last (mostly stabilized) tests' data series, to see whether there were fundamental differences in the two sets of results. The overall patterns of emotion intensity differences remained closely similar. That is, the less-than-perfect test-retest reliability did not substantially influence these results in either group. Two cautionary notes must be made, however. Here, we used relatively long video recordings as input for AEFEA, resulting in relatively big emotion intensity data series. Other studies have used much shorter recordings, and therefore obtained smaller data series [41; 71]. We did not explore the distribution of test-retest differences along the data series, leaving it possible that potential \"locally high\" test-retest differences, especially in shorter data series, may have a greater effect on the patterns of the results. The overall pattern of emotion intensities itself may also affect the test-retest differences. Data from different emotions may be differently affected by the test-retest differences. Exploring this possibility was beyond the scope of this study. Finally, we wish to point to the fact that the validity of the data gained repeatedly by FR8 was not addressed or examined in this study. Therefore, we cannot know whether the initial or the final (\"stabilized\") data series were more valid. (Although small test-retest differences generally suggest little, if any, potential difference in their validity.) Accordingly, we cannot provide any insight into which should be preferred in further analyses and in reaching study conclusions, where the validity of emotion intensity data is essential.