We evaluated practice-related change in MT over the course of training on the three frequently presented sequences (Figure 1A; see Experimental Procedures) using a
two-way (sequence X session) repeated-measures ANOVA. This revealed a main effect for session [F(2,21) ≈ 92.13, p < 0.00001]. This finding confirms that subjects learned the sequences during training. There was no significant effect of sequence type or interaction, confirming that the three sequences were learned similarly and with similar speed (Figure 2). The mean percent error (±SD) across the training sessions was 12.8 ± 7.5. We found no significant effect of error over sessions, indicating that there was no change in the speed/accuracy tradeoff even though MT values decreased with training. We quantified chunking within each sequence MDV3100 chemical structure by the optimized modularity QmultitrialQmultitrial of the sequence networks (see Experimental Procedures). Modularity in this case measures the separability between clusters Cabozantinib ic50 of IKIs. Higher values of Q indicate a greater ease in separating chunks. The average modularity was 0.54 ± 0.007, which was significantly greater than that expected in a random null-model network (p < 0.000000001, t ≈ 8.44, DF = 42). This demonstrates that significant chunking exists in the data. We predicted
that φ would increase with learning, reflecting stronger associations across adjacent chunks. Subjects demonstrated considerable variability of φ ( Figure 3A). To test
for increasing φ over time at the group level, we correlated group φ¯ to a linear slope. We first calculated group φ¯ by taking a random sample of 100 values of φ ordered in time for each participant. To control for the random selection of trials, we performed Carnitine palmitoyltransferase II and then pooled 100 instances of the correlation between the group φ¯ and the linear slope ( Figure 3B). Confirming our prediction, group φ¯ increased significantly over the course of training (R > 0.40, p ≈ 0.0002). Because φ and MT both change over time, it is critical to evaluate their relationship. We correlated trial-wise φ and MT for each participant and then pooled (averaged) the R values and resultant p values over subjects, revealing that the two measures are independent (R ≈ 0.13, p > 0.20). This suggests that brain regions correlated with φ reflect a performance diagnostic related to sequence learning. Although we found φ had no significant relationship to MT, the two performance diagnostics could still be related to individual differences. An important question to ask is whether “good learners” are also “good chunkers”? In this sense, good learners can be defined as those with the greatest improvement in MT over training (e.g., Crossman, 1959), and good chunkers can be defined similarly, as those with the greatest increase in φ over training.