During the dichoptic training, the AE performed contrast discrimination under dichoptic noise masking from the FE (
Fig. 1C). Significant learning was evident as the maximal TNC increased during the course of dichoptic training (
Figs. 2A and
2B). We used the percent improvement (PI = (threshold_post/threshold_pre - 1)*100) to quantify the amount of learning. Training improved the maximal TNC of the NPT group by 747% ± 342% (
t13 = 2.22,
P = 0.045, Cohen's d = 0.59; two-tailed paired
t-test here and later unless specified), from a root mean square contrast of 0.015 ± 0.003 to 0.070 ± 0.012 (
Figs. 2A–
2C). Likewise, training improved the maximal TNC of the PT group by 580% ± 164% (
t12 = 3.55,
P = 0.004, Cohen's d = 0.99), from a root mean square contrast of 0.023 ± 0.005 to 0.090 ± 0.011 (
Figs. 2A–
2C). A mixed-design ANOVA suggested a significant main factor of training (F
1,25 = 63.38,
P < 0.001, η
2 = 0.72), a nonsignificant main factor of group (F
1,25 = 2.01,
P = 0.17, η
2 = 0.07), and a nonsignificant interaction between training and group (F
1,25 = 0.60,
P = 0.45, η
2 = 0.023). Moreover, the amount of dichoptic demasking learning appeared to depend on the pretraining maximal TNC, as shown by the Deming regression fit on the log-log plot (slope = −1.53,
R2 = 0.57,
P < 0.001) (
Fig. 2D), suggesting that those with poorer pretraining maximal TNC tended to have more room for dichoptic learning. This correlation was consistent with previous studies
45,46 showing that the learning speed and amount were strongly coupled to pretraining performance levels.