Table 1 Bayesian Pearson correlational analysis results
From: Level of M1 GABAB predicts micro offline consolidation of motor learning during wakefulness
Bayesian Pearson correlations | ||
|---|---|---|
Pearson’s r | BF10 | |
MEP-micro-online-T | −0.431 | 1.107 |
MEP-micro-offline-T | 0.265 | 0.486 |
MEP-K1 | −0.062 | 0.316 |
MEP-K2 | −0.038 | 0.311 |
MEP-K3 | −0.247 | 0.457 |
MEP-Block-T | 0.267 | 0.49 |
MEP-Tapping-Speed_T | −0.147 | 0.354 |
SICI-micro-online-T | 0.182 | 0.381 |
SICI-micro-offline-T | −0.114 | 0.335 |
SICI-K1 | 0.249 | 0.460 |
SICI-K2 | −0.286 | 0.525 |
SICI-K3 | 0.282 | 0.518 |
SICI-Block-T | −0.06 | 0.315 |
SICI-Tapping-Speed_T | −0.108 | 0.332 |
ICF-micro-online-T | 0.458 | 1.335 |
ICF-micro-offline-T | −0.239 | 0.445 |
ICF-K1 | 0.21 | 0.409 |
ICF-K2 | −0.126 | 0.341 |
ICF-K3 | −0.155 | 0.359 |
ICF-Block-T | 0.069 | 0.318 |
ICF-Tapping-Speed_T | 0.025 | 0.31 |
sICF-micro-online-T | 0.174 | 0.374 |
sICF-micro-offline-T | 0.244 | 0.452 |
sICF-K1 | 0.511 | 2.034 |
sICF-K2 | −0.415 | 1.005 |
sICF-K3 | 0.065 | 0.317 |
sICF-Block-T | 0.069 | 0.318 |
sICF-Tapping-Speed_T | 0.17 | 0.371 |
LICI-micro-online-T | 0.485 | 1.639 |
LICI-micro-offline-T | −0.834 | *** 468.802 |
LICI-K1 | −0.249 | 0.461 |
LICI-K2 | 0.159 | 0.362 |
LICI-K3 | 0.137 | 0.347 |
LICI-Block-T | −0.245 | 0.454 |
LICI-Tapping-Speed_T | −0.316 | 0.595 |
