R lmer model: degree of freedom and chi square values are zero

I have built the following models:

full <- lmer(DV~ A*B + (1|speaker), data, REML=FALSE)

A <- lmer(DV~ A+ A:B + (1|speaker), data, REML=FALSE)
B <- lmer(DV~ B+ A:B + (1|speaker), data, REML=FALSE)
interaction <- lmer(DV~ A + B + (1|speaker), data, REML=FALSE)

I use anova to compare the first full model to the other ones:

anova(full, A)
anova(full, B)
anova(full, interaction)

The first two comparisons generated results with both df and chi square values being zeros, as shown below:

However, I have also tried to compare the null model with another model only include A or B:

null <- lmer(DV~ 1 + (1|speaker), data, REML=FALSE)
AA <- lmer(DV~ A + (1|speaker), data, REML=FALSE)
BB <- lmer(DV~ B + (1|speaker), data, REML=FALSE)
AB <- lmer(DV~ A:B + (1|speaker), data, REML=FALSE)

all the comparisons generated reasonable results (i.e. not 0 df and all comparisons are significant)

I have looked online and found this post: https://www.researchgate.net/post/What_is_a_Likelihood_ratio_test_with_0_degree_of_freedom

And my guess is that maybe for my full model, the interaction might be able to predict everything without the main effects (A and B).

I have a few questions:

1. Is my guess possibly true?
2. If it is true, why did the comparison with the null model show a significant effect?
3. On a more general scale, when I build linear mixed effect models, can I start from the Null model and add a factor at a time, then compare with the previous models? Or do I have to reduce from the full model?
4. If I use A+B as the base model:

base <- lmer(DV~ A+B + (1|speaker), data, REML=FALSE)

A <- lmer(DV~ A + (1|speaker), data, REML=FALSE)
B <- lmer(DV~ B + (1|speaker), data, REML=FALSE)
interaction <- lmer(DV~ A*B + (1|speaker), data, REML=FALSE)

Is it ok to report the comparison between the base model and A, B, interaction respectively?

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Please find the data file and the R markdown document here: dropbox.com/sh/88m8h6blow2xbn5/AABiNccsUlu3AlfPyamQP4n_a?dl=0
I also asked a question about the procedures I used in the R script in this post R lmer model: add factors or reduce factors

I’d be most grateful if you could help me please. Thank you!

(Intercept)  6.03977    0.34949  17.282
AT2         -0.55051    0.07597  -7.246
AT3         -1.16472    0.07597 -15.331
AT4          0.48228    0.07597   6.348
BS          -0.64024    0.07597  -8.427
AT2:BS       0.35379    0.10744   3.293
AT3:BS       0.47244    0.10824   4.365
AT4:BS       0.05247    0.10744   0.488

            Estimate Std. Error t value
(Intercept)  6.03977    0.34949  17.282
AT2         -0.55051    0.07597  -7.246
AT3         -1.16472    0.07597 -15.331
AT4          0.48228    0.07597   6.348
AT1:BS      -0.64024    0.07597  -8.427
AT2:BS      -0.28645    0.07597  -3.770
AT3:BS      -0.16781    0.07710  -2.177
AT4:BS      -0.58777    0.07597  -7.737

> -0.64024 + 0.35379
[1] -0.28645
> -0.64024 + 0.47244
[1] -0.1678
> -0.64024 + 0.05247
[1] -0.58777