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. 2016 Jan 15;125:756–766. doi: 10.1016/j.neuroimage.2015.11.009

Supplementary Fig. 2.

Supplementary Fig. 2

Effects of trial variability and scan noise on efficiency of estimating the mean of single trial-type using LSU when SOA = 3 s. See legend of Supplementary Fig. 1 for more details.

Effects of trial variability and scan noise on efficiency of estimating the mean of single trial-type using LSU when SOA = 3 s. See legend of Supplementary Fig. 1 for more details.

When SOA is short, the spread of points along the x-axis reduces compared to Supplementary Fig. 1 (where SOA = 20 s), and this reduced range prevents precise estimate of the slope of the best-fitting line, which is why PPM generally decreases as SOA decreases in Fig. 2B of main paper (without the influence of transients; cf Supplementary Fig. 3).

When SOA is short, the spread of points along the x-axis reduces compared to Supplementary Fig. 1 (where SOA = 20 s), and this reduced range prevents precise estimate of the slope of the best-fitting line, which is why PPM generally decreases as SOA decreases in Fig. 2B of main paper (without the influence of transients; cf Supplementary Fig. 3).

A more subtle point is that, when trial-variability is high and scan noise low (Panels B and E), the LSU estimate (solid line) can be closer to the true mean (dotted line), on average across simulations, than when the SOA is longer (cf. Panels B and E of Supplementary Fig. 1). This is because a shorter SOA entails more trials in total, i.e., a greater number of columns, and better “anchoring” the best-fitting line. This explains why the optimal SOA for PPM decreases as trial-variability increases in Fig. 2B of main paper. Note that the sample mean is independent of the number of trials, so the optimal SOA does not change this way for PSM in Fig. 2C of main paper.

A more subtle point is that, when trial-variability is high and scan noise low (Panels B and E), the LSU estimate (solid line) can be closer to the true mean (dotted line), on average across simulations, than when the SOA is longer (cf. Panels B and E of Supplementary Fig. 1). This is because a shorter SOA entails more trials in total, i.e., a greater number of columns, and better “anchoring” the best-fitting line. This explains why the optimal SOA for PPM decreases as trial-variability increases in Fig. 2B of main paper. Note that the sample mean is independent of the number of trials, so the optimal SOA does not change this way for PSM in Fig. 2C of main paper.