This page is devoted to the proof that the information in the brain is distributive. This experiment is inspired by distinguishability measure.
V<0.1
V<0.2
V<0.3
V<0.4
V<0.5
Now we plot the average accuracy for each choice of V_thres:
The whole scale
Plot at the small number of voxels (1-20) only
The matlab codes are made available:
code
DemoVoxelGroupAccuracy_V0dx.m
plotAvgAccRandOrder_allV.m
Description
"_V0dx" means the non-informative voxels are decided when V<0.x. The code produces the plot as in the first figure. This is the latest version.
Plot the average accuracy from variety of V_thres in one plot so that we can compare the effect of V-thres changes on the accuracy curve.
Characteristic curve VTC voxels: average accuracy vs number of voxels within a searchlight volume
Experimental results
When increasing the number of k (in k-nn), the accuracy tends to increase and decrease after over-inclusive.
Perhaps k can be an indicator to say what is the boundary of each supervoxel
Comments:
The code is made available here.
Test if the cooperative behavior is clearer with nearest voxels than further voxel
plot across the voxels
plot across k
The maximum accuracy occurs at the ring 6-10 because the voxels closer to that is similar and does not provide an additional information to the classifier. Therefore, the ones further provide better additional information.
Art's idea to prove that the VTC is not at the optimal combination
We want to show that by including more voxels, we gain better (training) accuracy.
We can also get better accuracy by removing some (non-informative) voxels from the VTC. And we can get even better by including some more versatile voxels outside the VTC mask.