Comparing two logit/probit curves with a single parameter [duplicate]

I've conducted a psychological experiment on the same subject, under two different condition. For each condition I've collected the number of correct and wrong answer for each stimulus (number of trials per stimulus = 10; number of stimulus = 15). From the data collected I fitted a psychometric curve (probit or logit curve), and now I want to compare the results.

Consider subject 1: His answers are collected in the following matrices

**COND_1**
correct
cnt   Y  N
1   0 10
2   0 10
3   0 10
4   0 10
5   0 10
6   0 10
7   1  9
8   4  6
9   5  5
10  7  3
11  8  2
12  7  3
13 10  0
14 10  0
15 10  0

**COND_2**
correct
cnt   Y  N
1   0 10
2   2  8
3   2  8
4   3  7
5   2  8
6   4  6
7   8  2
8  10  0
9   8  2
10 10  0
11  9  1
12 10  0
13 10  0
14 10  0
15 10  0

COND_1.1 <- matrix(c(0, 2, 2, 3, 2, 4, 8, 10, 8, 10, 9, 10, 10, 10, 10, 10, 8, 8, 7, 8, 6, 2, 0, 2, 0, 1, 0, 0, 0, 0), byrow=F, ncol=2)
COND_1.2 <- matrix(c(0, 0, 0, 0, 0, 0, 1, 4, 5, 7, 8, 7, 10, 10, 10, 10, 10, 10, 10, 10, 10, 9, 6, 5, 3, 2, 3, 0, 0, 0), byrow=F, ncol=2)

cnt <- seq(from=0, to=1.4, by=0.1)

ddprob.1.1 <- glm(COND_1.1 ~ cnt, family = binomial(link = "probit"))
ddprob.2.1 <- glm(COND_2.1 ~ cnt, family = binomial(link = "probit"))


Observing the plot, you can surely say that performance under condition 2 is better then the performance under condition 1. In fact if you compute some parameters like AUC, p25, p50, p75 (the value of the stimulus at which the number of correct answers are 25%, 50% and 75% each) [threshold parameters], you'll see that:

COND_1      COND_2
AUC    <    AUC
p25    >    p25
p50    >    p50
p75    >    p75


Now consider subject 2:

COND_1
correct
cnt   Y  N
1   0 10
2   0 10
3   0 10
4   0 10
5   3  7
6   3  7
7   4  6
8   5  5
9   9  1
10  9  1
11 10  0
12 10  0
13 10  0
14 10  0
15 10  0

COND_2
correct
cnt   Y  N
1   0 10
2   1  9
3   0 10
4   4  6
5   2  8
6   6  4
7   4  6
8   7  3
9   4  6
10  7  3
11  7  3
12 10  0
13  9  1
14 10  0
15 10  0

COND_1.2 <- matrix(c(0, 0, 0, 0, 3, 3, 4, 5, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 7, 7, 6, 5, 1, 1, 0, 0, 0, 0, 0), byrow=F, ncol=2)
COND_2.2 <- matrix(c(0, 1, 0, 4, 2, 6, 4, 7, 4, 7, 7, 10, 9, 10, 10, 10, 9, 10, 6, 8, 4, 6, 3, 6, 3, 3, 0, 1, 0, 0), byrow=F, ncol=2)
cnt <- seq(from=0, to=1.4, by=0.1)

ddprob.1.1 <- glm(COND_1.2 ~ cnt, family = binomial(link = "probit"))
ddprob.2.1 <- glm(COND_2.2 ~ cnt, family = binomial(link = "probit"))


The plotted curves are shown in the image below.

Can you say what performance is better? Under condition 1 or under condition 2? Extrapolating AUC, p25, p50, p75, you have:

COND_1      COND_2
AUC     >   AUC
p25     >   p25
p50     =   p50
p75     <   p75


So my question is: Is there a method to say that a generic curve (logit, probit or whatelse) is HIGHER then another? Is there a method to compare (a single number of "performance") that describe the differences of the two curves? My example shows that AUC, p25, p50, p75 are not good parameters. I'd like to compute a single numeric parameter for each curves, to make a simple t-paired test, extrapolated from the distribution seen.