Expert2Coder: Capturing Divergent Brain Regions Using Mixture of Regression Experts
TABLE I: Comparison of average accuracy of 16 participants i) ridge regression model, ii) multi-layer perceptron and, iii) MoRE. The macro-average results display the average of performance of each individual class (class-1: voxel coordinates with value 1, class-0: voxel coordinates with value 0). In Micro-average method, we sum up the individual true positives, false positives, and false negatives of the two classes and calculate the three metrics. The last column of the table reports the performance of class-1 (voxel coordinates with value 1).
['→ Feature set↓', '→ Method↓', '[BOLD] Macro Average Precision', '[BOLD] Macro Average Recall', '[BOLD] Macro Average F1-score', '[BOLD] Micro Average Precision', '[BOLD] Micro Average Recall', '[BOLD] Micro Average F1-score', '[BOLD] Class-1 Precision', '[BOLD] Class-1 Recall', '[BOLD] Class-1 F1-score']
We predicted the brain activation using classical models as well as the proposed MoRE method. To statistically verify how our results are reliable and finding the variance in predicted brain activations from the observed average voxel intensity values, we use three-Sigma rule implies that heuristically nearly all values lie within three standard deviations of the mean. Here, the macro averaging gives equal weight to each class to evaluate the performance of various methods across the two-classes. On the other hand, micro-averaging method calculates the individual true positives, true negatives, false positives, and false negatives of the the binary-class model. For each ground-truth test word, we calculate the “μ” and “σ” from the 199,658 voxel intensity values. We make the voxel coordinates (199,658 voxels) into 2 classes for each of the above-mentioned distributions for each of the three methods. We consider the following steps for getting the classification metrics. For each test word, we predicted the brain activations from the three methods such as Ridge, MLP, and MoRE. We take the predicted brain activations from the previous step and assign the intensity values higher than “μ-3σ” to one class and the remaining voxel coordinates as belonging to the zero class. We perfrom a similar analysis on the empirically observed brain activations. We use the observed brain activations with voxel intensity values higher than “μ-3σ” as one class and the other voxel coordinates as belonging to zero class. We repeat the above steps for other intensity value distributions (corresponding to the values that are 1- or 2- standard deviations above and below “μ”) for the three methods. The macro average recall score for the MoRE method is higher than the two methods in all the seven distributions. In contrast, precision for two baseline methods is higher than MoRE, which mainly comprises false positives (some other voxel coordinates that become activate spuriously). Of the two baseline methods, MLP method results in more false positives as compared to Ridge regression.