(A) The contrast response function (CRF), based on the amplitude of the LPD component, which was averaged over posterior electrodes from 230–380 ms poststimulus. Focused attention increased the LPD amplitude by a comparable amount across early and late training phases. Error bars represent within-subject SEM. (B) Corresponding maximum response (response at 100% contrast minus baseline) and half-maximum contrast parameters of the CRFs shown in (A). Error bars represent the 68% CIs; *, **, and *** represent significant pairwise comparisons between attention conditions in each training phase, with p <0.05, p < 0.01, and p < 0.001, respectively. (C) The LPD-based CRFs evoked by the divided target nontarget stimuli during the first and second stimulus intervals across the early and late training phases. We observed a separation between the divided target and nontarget responses only during the second stimulus interval of the late training phase, consistent with the idea that the LPD reflects postperceptual decision-making processes that can be enhanced with training [67–68]. Error bars represent within-subject SEM. (D) Corresponding maximum response and half-maximum contrast parameters of the CRFs shown in (C). Error bars represent the 68% CIs; **, and *** represent significant pairwise comparisons between attention conditions in each training phase with p < 0.01 and p < 0.001, respectively. (E) The day-by-day analysis of the LPD data with baseline subtraction (2 electroencephalography [EEG] sessions or ~1 day per each time bin). Top and bottom panels represent the estimated maximum response and half-maximum contrast parameters. (F) The day-by-day analysis of the LPD data shown in (C) and (D). Error bars in (E) and (F) represent the 68% CIs. Note that the contrast values on the x-axis in (A) and (C) are not exactly the same across target and nontarget conditions because, in the target conditions, we used the averaged contrast values between the pedestal and incremental stimuli. Data are available from the Open Science Framework (https://osf.io/pc7dr/).