21Many recent studies found signatures of motor learning in neural beta oscillations (13)(14)(15)(16)(17)(18)(19)(20)(21)(22) 30Hz), and specifically in the post-movement beta rebound (PMBR). All these studies were in 23 simplified laboratory-tasks in which learning was either error-based or reward-based. Interestingly, 24 these studies reported opposing dynamics of the PMBR magnitude over learning for the error-based 25 and reward-based tasks (increase verses decrease, respectively). Here we explored the PMBR 26 dynamics during real-world motor-skill-learning in a billiards task using mobile-brain-imaging. 27Our EEG recordings highlight opposing dynamics of PMBR magnitudes between different subjects 28 performing the same task. The groups of subjects, defined by their neural-dynamics, also showed 29 behavioral differences expected for error-based verses reward-based learning. Our results suggest 30 that when faced with the complexity of the real-world different subjects might use different learning 31 mechanisms for the same complex task. We speculate that all subjects combine multi-modal 32 mechanisms of learning, but different subjects have different predominant learning mechanisms. 33 recorded with the DSI-24 there were 6 PMBR Increasers and 4 PMBR Decreasers. 126Correspondingly, there was no correlation between the system and the PMBR-Error correlation 127 (Spearman rank correlation r=0.01, p=0.97). 128Based on the EEG data, which suggests two groups of subjects with different PMBR 129 dynamics, we looked for behavioral signatures in the task performance of different learning 130 between these groups. The lag-1 autocorrelation (ACF(1)) of the performance measure (in our case, 131 the directional error of the target-ball relative to the pocket) was suggested as an index of 132 performance in motor-skill learning, where close to zero values corresponds to high skill (van Beers 133 et al., 2013). We calculated for each subject the ACF(1) in the first and the second half of the 134 training session (blocks 1-6 and 7-12, respectively). The ACF(1) values of both groups were 135 significantly greater than zero during both halves of the session (t test p<0.01), as expected for 136 naïve participance ( Figure 3A). But, the decay in the ACF(1) from the first half of the training 137 session to the second was significantly higher for the PMBR Decreasers (t test p<0.01, Figure 3B). 138Additionally, the decay in the intertrial variability (measured from the first block (trials 1-25) to 139 the learning plateau (trials 201-300)) was also significantly larger in the PMBR Decreasers (t test