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When comparing (correlating - Pearson's r) RSMs across ROIs, the comparison is necessarily biased by the level of signal vs noise in the data. For example, if we have two candidate "models" A and B, and one "target" dataset C, if A is super noisy while B is not, A may be less highly correlated with C even if A is a better model of C's responses than B.
This seems to particularly a problem for our ROIs as the split-half reliability for the ventral ROI is consistently higher than for the lateral ROI within subject, indicating a difference in noise levels.

Correlation within and across ventral and lateral ROIs for each subject, 20% variance explained threshold

Okay now that we have the mega matrices, we need to quantify them and figure out what they mean for the feasibility of the project.
First, some visualizations:

• Left hemisphere
  
• Right hemisphere