Overlearning refers to the continued training of a skill after performance improvement has plateaued. Whether overlearning is beneficial is a question in our daily lives that has never been clearly answered. Here we report a new important role: overlearning in humans abruptly changes neurochemical processing, to hyperstabilize and protect trained perceptual learning from subsequent new learning. Usually, learning immediately after training is so unstable that it can be disrupted by subsequent new learning until after passive stabilization occurs hours later. However, overlearning so rapidly and strongly stabilizes the learning state that it not only becomes resilient against, but also disrupts, subsequent new learning. Such hyperstabilization is associated with an abrupt shift from glutamate-dominant excitatory to GABA-dominant inhibitory processing in early visual areas. Hyperstabilization contrasts with passive and slower stabilization, which is associated with a mere reduction of excitatory dominance to baseline levels. Using hyperstabilization may lead to efficient learning paradigms.
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We thank A. Berard, J. Dobres, M. Nassar, D. Rahnev and E. Robertson for their important comments on early drafts. This work was supported by NIH R01 EY015980 and R01EY019466 (to T.W.), NSF BCS 1539717 (to Y.S.) and JSPS KAKENHI Grant Number 16H06857 (to K.S.). L.-H.C. was supported by MOST (104-2410-H-010-001-MY2, 105-2420-H-010-002-MY2), NYMU Aging and Health Research Center and Yen Tjing Ling Medical Foundation.
The authors declare no competing financial interests.
Integrated supplementary information
Overlearning hyper-stabilizes learning states by rapidly changing the E/I ratio in the early visual areas from more excitatory to more inhibitory than the pre-training baseline, so that learning of a task will not be disrupted by subsequent training on a new task. Several hours after training, irrespective of whether hyper-stabilization occurred or not, the E/I ratio returns to the baseline, leading to typical stabilization which allows for both the existing and new learning without disrupting each other.
(a) Schematic of a trial of the orientation detection task. Subjects were asked to indicate which of the 2 stimulus intervals contained an oriented structure (2-interval-forced-choice task). (b) Results. The purpose of the preliminary experiment was to estimate the amount of training that induces the saturation of performance improvements of the orientation detection task (Supplementary Fig. 2a) so that the effects of overlearning on learning stability would be tested in the main experiments. The experiment consisted of 3 stages conducted over 2 consecutive days: pre-test, training, and post-test stages (see Preliminary experiment in Online Methods for details). Subjects were asked to perform the orientation detection task (Supplementary Fig. 2a). Task difficulty was controlled by adjusting the signal-to-noise (S/N) ratio of the stimuli using a standard 2-down 1-up staircase method. S/N ratio thresholds were measured for each block of trials. Five different groups of subjects (N=12 for each group) were trained on the task for one orientation (trained orientation) for 4, 7, 8, 9, and 16 blocks, respectively. During each of the pre- and post-test stages, we measured S/N ratio thresholds for 3 orientations (10, 70, and 130 degrees): one was the trained orientation, while the remaining 2 served as untrained orientations that were rotated ±60 degrees away from the trained orientation. Performance improvement for each orientation after training was calculated as percent reduction in the S/N ratio threshold in the post-test stage relative to the pre-test stage (see Pre- and post-test stages in Online Methods for the definition of performance improvement). The graph shows the mean (±s.e.m.) performance improvement for the trained orientation as a function of the number of training blocks. We applied segmented regression analysis on the mean performance improvements. The root mean square error for the 2 linearly interpolated lines connected at one breaking point was calculated when the breaking point was the 7th, 8th, or 9th block of training. The root mean square error was the smallest when the breaking point was at the 8th block. The 2 interpolated lines with the 8th block as the breaking point are shown as black lines in the graph. These results suggest that the performance improvement plateaued around the 8-block training. That is, the 8-block training group experienced no overlearning, while the 16-block training group experienced overlearning.
(a) Procedure of Control Experiment 1. (b) Procedure of Control Experiment 2 in which the time interval between the first- and second-training phases was set to 50 min. (c) Mean (±s.e.m.) performance improvements (N=12) for the first-trained, second-trained, and untrained orientations in Control Experiment 1. A significant performance improvement was found only for the first-trained orientation (one-sample t-test, t11=3.615, P=0.012 after Bonferroni correction for 3 comparisons), but not for the second-trained (t11=0.705, P=0.496 without Bonferroni correction) or untrained (t11=0.578, P=0.575 without Bonferroni correction) orientation. (d) Mean (±s.e.m.) performance improvements (N=12) for the first-trained, second-trained, and untrained orientations in Control Experiment 2. A significant performance improvement was found only for the second-trained orientation (one-sample t-test, t11=3.953, P=0.007 after Bonferroni correction for 3 comparisons), but not for the first-trained (t11=1.008, P=0.335 without Bonferroni correction) or untrained (t11=0.706, P=0.495 without Bonferroni correction) orientation. * P<0.05, ** P<0.01 after Bonferroni correction.
The measured spectra are shown in the top row. The second row shows the spectra fitted with the LC-Model (see MRS analysis in Online Methods for details). The bottom row shows the residual remaining after the fitting. The remaining rows show individual fits for all metabolites that can be detected by a given acquisition. Macromolecular and lipid signals were used to produce the baseline correction. (a) An example spectrum from the GABA scan. (b) An example spectrum from the glutamate scan. NAA, N-acetylaspartate; NAAG, N-acetylaspartylglutamate; GSH, glutathione; Glu, Glutamate; GABA, gamma-aminobutyric acid; Asp, aspartate; GPC, glycerophosphocholine; Ins, myo-inositol; Lac, lactate; Glc, glucose; Gln, Glutamine, PCh, phosphocholine; Cr, creatine; CrCH2, creatine methylene group; PCr, phosphocreatine; Scyllo, scyllo-inositol; Tau, taurine.
The value on each plot shows the full-width-at-half-maximum line-width for NAA in Hz. (a) The raw spectra obtained in the glutamate scan for each of 12 subjects and each of pre-MRS, post-MRS 1, and post-MRS 2 stages. (b) The raw difference spectra obtained in the GABA scan.
(a) Mean (±s.e.m.) performance improvements in the no-overlearning group for the trained and 2 untrained orientations (orientation rotated -60 degrees from the trained, orientation rotated +60 degrees from the trained). A significant performance improvement was found only for the trained orientation (one-sample t-test, t11=4.905, P=0.003 after Bonferroni correction for 6 comparisons), and not for the untrained orientations (t11<1.292, P>0.223 without Bonferroni correction across both tests). (b) Mean (±s.e.m.) performance improvements in the overlearning group. A significant performance improvement was found only for the trained orientation (t11=4.483, P=0.006 after Bonferroni correction for 6 comparisons), and not for the untrained orientations (t11<0.840, P>0.419 without Bonferroni correction across both tests). In addition, the performance improvements for the trained orientation did not significantly differ between the 2 groups (two-sample t-test, t22=0.510, P=0.615). ** P<0.01 after Bonferroni correction.
(a) Mean (±s.e.m.) E/I ratio changes calculated using an alternative method. While some MRS studies have used glutamate as a representative excitatory neurotransmitter (Jocham et al., Nat Neurosci, 2012; Terhune et al., J Neurosci, 2014), others have used a combined signal from glutamate and glutamine (Glx) (Ref. 55; Duncan et al., PLoS One, 2013). We tested whether the use of Glx, instead of glutamate alone, affects the observed E/I ratio changes. We observed the same statistical tendency, as originally found in Experiment 3 (Fig. 3d). Results of a two-way mixed-model ANOVA on E/I ratio change with factors of time (30 min after vs. 3.5 hours after training) and group (no-overlearning vs. overlearning groups) showed a significant main effect of group (F1,22=17.886, P<10-3) and a significant interaction between time and group (F1,22=5.281, P=0.031). No significant main effect of time was observed (F1,22=0.009, P=0.924). In the no-overlearning group (red), we found a significant quadratic trend in the time-course of the E/I ratio change (F1,11=5.270, P=0.042). The E/I ratio change was significantly greater than zero 30 min after training (one-sample t-test, t11=3.543, P=0.018 after Bonferroni correction for 4 comparisons), but not significantly different from zero 3.5 hours after training (t11=0.947, P=0.364 without Bonferroni correction). In the overlearning group (cyan), we also found a significant quadratic trend in the time-course of the E/I ratio change (F1,11=7.278, P=0.021). The E/I ratio change was significantly less than zero 30 min after training (one-sample t-test, t11=3.041, P=0.045 after Bonferroni correction for 4 comparisons), but not significantly different from zero 3.5 hours after training (t11=1.251, P=0.237 without Bonferroni correction). * P<0.05 after Bonferroni correction. (b) Mean (±s.e.m.) percent changes in the creatine concentrations for the no-overlearning (red) and overlearning (cyan) groups. We did not find any evidence that group or time significantly affected creatine concentrations. Results of a two-way mixed-model ANOVA on change in the creatine concentrations with factors being time (30 min after vs. 3.5 hours after training) and group (no-overlearning vs. overlearning groups) showed no significant main effect of time (F1,22=0.413, P=0.527), group (F1,22=0.248, P=0.623), or interaction between the 2 factors (F1,22=1.067, P=0.313). In addition, no significant change in the creatine concentration from the baseline was found 30 min (one-sample t-test, t11<0.783, P>0.450) or 3.5 hours (t11<0.751, P>0.468) after training for either group.
(a) Procedures. During the training stage, 2 orientations were presented on alternating blocks during 16 blocks of training (red and cyan box). (b) Mean (±s.e.m.) performance improvements (N=7) for trained orientation 1, trained orientation 2, and untrained orientation. No significant performance improvement was found for any orientation (one-sample t-test, t6<0.296, P>0.777). (c) Mean (±s.e.m.) E/I ratio changes. The E/I ratio change was not significantly different from zero 30 min after training (one-sample t-test, t6=1.074, P=0.324) or 3.5 hours after training (t6=0.731, P=0.492).
(a) Procedures. During the first-training stage (red box), subjects were trained on the orientation detection task with one orientation (first-trained orientation) for 16 blocks. During the second-training stage (cyan box), subjects were trained on the task with another orientation (second-trained orientation) for 8 blocks. (b) Mean (±s.e.m.) performance improvements (N=12) for the first-trained, second-trained, and untrained orientations. A significant performance improvement was found only for the first-trained orientation (one-sample t-test, t11=4.968, P=0.001 after Bonferroni correction for 3 comparisons), but not for the second-trained (t11=1.132, P=0.282 without Bonferroni correction) or untrained (t11=0.369, P=0.722 without Bonferroni correction) orientation. These results indicate that anterograde interference occurred, as originally found in Experiment 1. ** P<0.01 after Bonferroni correction. (c) Mean (±s.e.m.) E/I ratio changes. As in Experiment 3, the E/I ratio was significantly decreased 30 min after the 16-block training in the first-training stage (one-sample t-test, t11=3.179, P=0.009). ** P<0.01. (d) The scatter plot of the E/I ratio change 30 min after the first-training stage against the performance improvement for the second-trained orientation. The black line indicates the ordinary least-square regression. A significant correlation was found between the E/I ratio change 30 min after the first-training stage and the performance improvement for the second-trained orientation across subjects (r=0.643, P=0.024). There was no significant outlier in the plot (Grubbs’ test, P>0.05).
(a) Mean (±s.e.m.) changes in the concentrations of glutamate (magenta) and GABA (blue) for the overlearning group (N=12). A two-way ANOVA with repeated measures on concentration change with factors of time (30 min vs. 3.5 hours after training) and metabolite (glutamate vs. GABA) showed a significant main effect for metabolite (F1,11=9.934, P=0.009), but not for time (F1,11=0.326, P=0.580) nor interaction between the 2 factors (F1,11=1.827, P=0.204). For glutamate, no significant quadratic trend in the time-course of the glutamate changes was found (F1,11=0.002, P=0.967). Subsequent tests showed no significant change in the glutamate concentration 30 min after (one-sample t-test, t11=0.143, P=0.889) or 3.5 hours after (t11=0.172, P=0.867) training. For GABA, we also found no significant quadratic trend in the time-course of the GABA changes (F1,11=4.189, P=0.065). Subsequent tests showed no significant change in the GABA concentration 30 min after (one-sample t-test, t11=2.637, P=0.092 after Bonferroni correction for 4 comparisons) or 3.5 hours after training (t11=1.314, P=0.216). (b) Mean (±s.e.m.) changes in the concentrations of glutamate and GABA for the no-overlearning group (N=12). A two-way ANOVA with repeated measures on concentration change with factors of time (30 min vs. 3.5 hours after training) and metabolite (glutamate vs. GABA) showed a significant main effect for metabolite (F1,11=7.175, P=0.022), but not for time (F1,11=0.426, P=0.528) nor interaction between the 2 factors (F1,11=1.471, P=0.251). For glutamate, no significant quadratic trend in the time-course of the glutamate changes was found (F1,11=0.820, P=0.385). Subsequent tests showed no significant change in the glutamate concentration 30 min after (one-sample t-test, t11=1.973, P=0.074) or 3.5 hours after (t11=1.403, P=0.188) training. For GABA, we also found no significant quadratic trend in the time-course of the GABA changes (F1,11=0.206, P=0.659). Subsequent tests showed no significant change in the GABA concentration 30 min after (one-sample t-test, t11=0.506, P=0.623) or 3.5 hours after (t11=1.204, P=0.254) training.
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Shibata, K., Sasaki, Y., Bang, J. et al. Overlearning hyperstabilizes a skill by rapidly making neurochemical processing inhibitory-dominant. Nat Neurosci 20, 470–475 (2017). https://doi.org/10.1038/nn.4490
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