Supplementary Fig. 7: The choice of data partition does not affect the cross-validation results. | Nature Neuroscience

Supplementary Fig. 7: The choice of data partition does not affect the cross-validation results.

From: Effective learning is accompanied by high-dimensional and efficient representations of neural activity

Supplementary Fig. 7

The analysis in this manuscript relies on cross-validation using a standard k-fold partition, where the data from each trial is randomly assigned to k-folds, such that each fold has a similar number of data points. Here, we repeat our cross-validation using a block-wise partition, where now each fold consists of data from the same temporal block. In all cases, we retain a standard k = 5 folds and divide the data such that each fold has a similar number of data points. This procedure is chosen so as to verify that our results do not depend on the standard choice of randomly assigned partitions, where neighboring trials that are temporally overlapping could be entered into training and test sets, and thereby potentially violate independence. We find that there still remains a positive correlation of Pearson’s r = 0.40 (one-sided, n = 19 subjects) between the response accuracy of the participants and their separability dimension (left), and that this result is significant with p < 0.004 when compared to the null data (right; n = 1000).

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