我认为在您的情况下，最好使用置换测试，您可以在其中计算每个条件的置换相关性，然后计算它们的差异。例如，您可以将条件 A 下的两个变量和条件 B 下的两个变量逐行连接，因此您最终得到一个矩阵 (20*2 2)。然后您在这 40 行中进行置换并获得 p 值，如以下 R 代码所述：$\times$

# fix the # permutations
nperm <- 5000 # needs to be large enough but depends on the samp. size

# set a void vector for the dif of correl.
cor.dif <- rep(NA, nperm)

# simulate some fake data
n1 <- n2 <- 10
x1a <- runif(n1)
x2a <- rnorm(n2)
x1b <- rnorm(n1)
x2b <- runif(n2)

X1 <- cbind(x1a, x2a) # the two measurements in cond. A
X2 <- cbind(x1b, x2b) # the two measurements in cond. B

# concatenate row-wise X1 and X2
X <- rbind(X1, X2) # this is the matrix of 20*2 x 2

# now start permuting
for(i in 1:nperm){

  # sample an index
  idx <- sample(na+nb,na, replace = FALSE)

  # calculate the permuted correlation in the first condition
  cor.1 <- cor(X[idx,1],X[idx,2])

  # calculate the permuted correlation in the second condition
  cor.2 <- cor(X[-ida,1],X[-idx,2])

  # store the dif. of correlations
  cor.dif[i] <- cor.1-cor.2
}

# compute the empirical/actual difference of correlations
emp.cor.dif <- cor(x1a, x2a)-cor(x1a, x2a)

# see at the plot
hist(cor.dif)
abline(v = emp.cor.dif)

# compute the Monte Carlo approximation of the permutation p-value
2*min(mean(cor.dif>emp.cor.dif), mean(cor.dif<emp.cor.dif))

如果您正在测试两种不同条件下行为参数对神经生理参数的影响。您可以测试两种不同条件和行为的相互作用是否对神经生理学产生影响。如果添加的交互项很重要，则可以说神经生理值与两种不同的情况相关。