## 3 Results
### Between-Emotion and Between-Group Differences in Emotion Intensities  
Finally, we explored the pattern of differences in EFE intensities across the eight emotions and between the two groups and examined whether the not-perfect test-retest reliability of AEFEA using FR8 influences this pattern. First, we calculated the emotion intensity means for each subject and each emotion dimension, using their individual data series from their last analysis (test). Accordingly, \(n=53\) of included individual data series showed total test-retest identity (test\({}_{\text{stable}}\)), while \(n=7\) individual data series came from test\({}_{3}\), still not showing total identity to test\({}_{2}\) data. Figure 3 shows these means for the groups. Figure 2: Histogram of the 7 subjects with nonidentical data series when comparing their test\({}_{2}\) and test\({}_{3}\) data, along the ratio of differing data points. (Data of 5 subjects with identical test\({}_{2}\)–test\({}_{3}\) datasets are not included). A two-way mixed ANOVA (two groups \(\times\) eight emotions; repeated measures) returned a main effect of emotion (Geisser-Greenhouse: F(1.566, 90.801) = 826.832; \(p<0.001\); \(\eta^{2}_{\text{partial}}=0.934\), \(\omega^{2}=0.930\)). There was no main effect of diagnosis; the interaction was marginally significant (Geisser-Greenhouse: F(1.00, 58.00) = 3.684, \(p=0.0599\), \(\eta^{2}_{\text{partial}}=0.060\), \(\omega^{2}=0.042\)). Repeating the same analysis without data on _Neutral_ left the main effect of emotion significant (Geisser-Greenhouse: F(1.665, 96.589) = 51.492; \(p<0.001\), \(\eta^{2}_{\text{partial}}=0.470\), \(\omega^{2}=0.428\)); there emerged a main effect of diagnosis--as the equalizing effect of _Neutral_ was suppressed, the remaining emotions showed a different overall level in the two groups, with a modest effect size ([PERSON]'s test: F(1, 55.4) = 5.055, \(p=0.0286\), \(\eta^{2}_{\text{partial}}=0.081\), \(\omega^{2}=0.064\)). The diagnosis-emotion interaction was also significant, but the effect size was small (Geisser-Greenhouse: F(1.665, 96.589) = 4.456; \(p=0.0391\); \(\eta^{2}_{\text{partial}}=0.071\), \(\omega^{2}=0.049\)). When _Surprised_ was also removed, the remaining six emotions still produced a significant main effect of emotion (Geisser-Greenhouse: F(2.525, 146.452) = 14.98; \(p<0.001\); \(\eta^{2}_{\text{partial}}=0.205\), \(\omega^{2}=0.165\)) but neither a main effect of diagnosis nor an interaction remained. The main effect of emotion in this case was due to a number of between-level differences, as indicated by [PERSON]'s test (_Happy_ > _[Angry, Scared, Disgusted, Contempt]_; _Sad_ > _[Disgusted, Contempt]_; _Scared_ > _Contempt_; only those differences are listed for which \(p<0.01\)). However, all six of these emotion means were very low in both groups (total group means: ASD: 0.0133; NT: 0.0135). The level of _Neutral_ differed only marginally significantly between groups (W(56.6) = \(-1.751\); \(p=0.0854\); [PERSON]'s d: \(-0.454\); mean difference: \(-0.062\), CI95: \([-0.131\); 0.009)]; while the level of _Surprised_ differed significantly in the two groups (Welch test: W(52.8) = 2.36; \(p=0.022\); [PERSON]'s d = 0.615, mean difference: 0.0469; CI95: [0.0071, 0.0876]); however, these differences would obviously not survive a type I error correction for eight emotions. To explore if the differences between the initial (test\({}_{1}\)) dataset and the final dataset influence the results above, the same ANOVA was repeated using the test\({}_{1}\) data of each subject. All results were very similar, so much so that the overall emotion intensity means plot was not noticeably different from Figure 3--therefore, we will not present it here. The two-way mixed ANOVA (2 groups X 8 emotions) returned a main effect of emotion, here, too (Geisser-Greenhouse: F(1.0, 58.0) = 827.835; \(p<0.001\); \(\eta^{2}_{\text{partial}}=0.931\), \(\omega^{2}=0.935\)).