> str(data_gr)
'data.frame':   192 obs. of  4 variables:
 $ sbj       : Factor w/ 24 levels "aggfyt95","agkxri94",..: 1 1 1 1 1 1 1 1 2 2 ...
 $ block     : Factor w/ 4 levels "1","2","3","4": 1 1 2 2 3 3 4 4 1 1 ...
 $ IA_LABEL  : Factor w/ 2 levels "ideog","label": 1 2 1 2 1 2 1 2 1 2 ...
 $ DWELL_TIME: num  781 769 608 757 796 ...

> ezANOVA(data=data_gr, dv=.(DWELL_TIME), wid=.(sbj), within=.(IA_LABEL,block), type=3)
$ANOVA
      Effect DFn DFd        F            p p<.05          ges
2       IA_LABEL   1  23 0.310731 0.5826170174       0.0006009409
3          block   3  69 1.054737 0.3740832150       0.0087791282
4 IA_LABEL:block   3  69 7.269528 0.0002626766     * 0.0238067489

mod<-lme(DWELL_TIME ~ IA_LABEL* block, random=list(sbj=pdBlocked(list(~1, pdIdent(~IA_LABEL-1), pdIdent(~block-1)))), data=data_gr)

tmp <- expand.grid(IA_LABEL = unique(data_gr$IA_LABEL), block = unique(data_gr$block))
X <- model.matrix(~ block * IA_LABEL, data =tmp)

Tukey <- contrMat(table(data_gr$IA_LABEL), "Tukey")
mat<-matrix(0, nrow = nrow(Tukey), ncol = ncol(Tukey))
K1 <- cbind(Tukey, mat,  mat,  mat)
rownames(K1) <- paste(levels(data_gr$block)[1], rownames(K1), sep = ":")
K2 <- cbind(mat, Tukey, mat, mat)
rownames(K2) <- paste(levels(data_gr$block)[2], rownames(K2), sep = ":")
K3 <- cbind(mat,mat, Tukey,mat)
rownames(K3) <- paste(levels(data_gr$block)[3], rownames(K3), sep = ":")
K4 <- cbind(mat, mat, mat, Tukey)
rownames(K4) <- paste(levels(data_gr$block)[4], rownames(K4), sep = ":")
K <- rbind(K1, K2, K3, K4)
colnames(K) <- c(colnames(Tukey), colnames(Tukey), colnames(Tukey), colnames(Tukey))

summary(glht(mod, linfct = K %*% X))

Simultaneous Tests for General Linear Hypotheses

Fit: lme.formula(fixed = DWELL_TIME ~ IA_LABEL * block, data = data_gr,
random = list(sbj = pdBlocked(list(~1, pdIdent(~IA_LABEL -
    1), pdIdent(~block - 1)))))

Linear Hypotheses:
                     Estimate Std. Error z value Pr(>|z|)
1:label - ideog == 0   118.27      44.07   2.684   0.0247 *
2:label - ideog == 0   -45.58      55.24  -0.825   0.8073
3:label - ideog == 0   -83.12      55.24  -1.505   0.3484
4:label - ideog == 0   -44.26      44.07  -1.004   0.6856
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Adjusted p values reported -- single-step method)