In a Bayesian linear regression, micro-offline-T learning explains a moderate proportion of variance in LICI (R² = 0.69), with moderate evidence for its inclusion¶

Claim · All claims
Evidence · 1 study · associational r1 · q2
Currency · newest study 2025 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? A Bayesian linear regression with LICI as the outcome found the model containing micro-offline-T learning explained R² = 0.69 (BF10 > 100), and micro-offline-T had B = −0.58 with BF inclusion = 52.6. → Pasquale Cardellicchio & Sara Borgomaneri 2025

Evidence¶

Pasquale Cardellicchio & Sara Borgomaneri 2025¶

Pasquale Cardellicchio & Sara Borgomaneri. (2025). Level of M1 GABAB predicts micro offline consolidation of motor learning during wakefulness. npj Science of Learning 10:10. https://doi.org/10.1038/s41539-025-00299-1

q2 · i? · associational · r1

Bayesian linear regression (JASP, JZS prior r scale = 0.354) testing whether LICI predicts individual learning-fitting estimates and micro-online/micro-offline performance in the 16-participant experiment. R² and B are regression quantities, not standardized effect sizes, so no impact value is assigned.

"The model with Micro-offline-T learning explained a moderate proportion of the variance R2 =0 . 6 9 ( B F10 > 100) (Table 2). Micro-offline-T learning (B = −0.58, BF inclusion= 52.6) provides moderate evidence for being included in the model"

Discussion¶

Related Claims¶

• Lower resting GABAB-mediated inhibition in M1 (measured as LICI) is strongly negatively associated with micro-offline motor consolidation gains during early learning — related
• No other neurophysiological measure (MEP, SICI, ICF, sICF) showed a significant correlation with micro-online or micro-offline learning — related
• The GABAB–micro-offline correlation is significantly stronger than the corresponding GABAA (SICI) correlation — related