Table 3.
MVPA: four-class classifications
| Predicted state |
||||
|---|---|---|---|---|
| Gaze | Speech | Angry | Happy | |
| Right whole STS [accuracy 39.06% (cutoff = 32.81)] | ||||
| Gaze | 8 | 1 | 2 | 5 |
| Speech | 2 | 6 | 2 | 6 |
| Angry | 1 | 4 | 2 | 9 |
| Happy | 4 | 1 | 2 | 9 |
| Left whole STS [46.88% (34.38)] | ||||
| Gaze | 8 | 4 | 1 | 3 |
| Speech | 1 | 11 | 2 | 2 |
| Angry | 3 | 3 | 6 | 4 |
| Happy | 2 | 6 | 3 | 5 |
| Right pSTS [39.06% (32.81)] | ||||
| Gaze | 9 | 4 | 1 | 2 |
| Speech | 4 | 4 | 1 | 7 |
| Angry | 1 | 4 | 5 | 6 |
| Happy | 4 | 4 | 2 | 7 |
| Left pSTS [25% (32.81)] | ||||
| Gaze | 2 | 6 | 5 | 3 |
| Speech | 2 | 4 | 7 | 3 |
| Angry | 1 | 6 | 9 | 0 |
| Happy | 4 | 6 | 5 | 1 |
Notes: Values represent the mean accuracy of four-class classification, together with significance cutoff’s reported in brackets. Results are shown for the whole anatomical STS mask, and the pSTS functional ROI defined by a separate face localizer. Classification performances that were significantly better than chance are marked in bold. For each classification, the confusion matrix reports how each condition was categorized by the SVM. Bold values refer to correct categorizations (e.g. how frequently gaze parameters were classified as gaze).