Table 1.

Regression statistics

	Saccade + perception task	Perception-only task
Orthogonal regressions
Single subject (Figure 2C)
Slope [95% CI]	1.03 [0.73, 1.44]	1.12 [0.83, 1.53]
Pearson's correlation	r = 0.876, p = 0.00009	r = 0.854, p = 0.0002
Pool of subjects (Figure 2G)
Slope [95% CI]	0.98 [0.77, 1.13]	0.83 [0.48, 1.09]
Pearson's correlation	r = 0.942, p = 0.0000	r = 0.833, p = 0.0004
Psychometric functions
Single subject (Figure 2D)
Threshold: mean [95% CI]	105.3 [104.0, 106.4]	100.5 [99.3, 101.4]
Width: mean [95% CI]	291.1 [289.0, 293.7]	208.2 [206.8, 210.0]
Guess rate	0.022 ± 0.001	0.039 ± 0.001
Lapse rate	0.084 ± 0.000	0.083 ± 0.000
Pool of subjects (Figure 2H)
Threshold: mean [95% CI]	74.4 [73.5, 75.7]	66.0 [63.6, 67.5]
Width: mean [95% CI]	581.6 [580.6, 582.5]	535.6 [534.5, 536.4]
Guess rate: mean ± SEM	0.006 ± 0.000	0.005 ± 0.000
Lapse rate: mean ± SEM	0.023 ± 0.000	0.007 ± 0.000

Confidence intervals for orthogonal regression come from 2000 bootstrap resamplings. To estimate the parameters of the psychometric function [its mean, width, and low (guess rate) and high (lapse rate) asymptotes] with a Bayesian technique, a priori assumptions must be made about the distribution of the parameters (Kuss et al., 2005). We used a prior with a normal distribution, Gauss (μ = 50, σ = 150), for the threshold parameter. (Changing the threshold parameter's a priori μ from −50 to 50 ms changed the threshold value by 3 ms or less.) The width parameter was given a γ distribution prior, γ (shape = 1.01, scale = 2000). The lapse and guess rate were assumed to be small, and were given priors based on the β distribution, β (α = 1, β = 20).