In practice, assigning direction is utterly complicated by the fact that only very few features allow an unambiguous detection of direction. In biology, Gogarten et al. (1989), Iwabe et al. (1989) have used gene duplications to root groups of bacteria at the root to the tree of life itself for which outgroups are not applicable. The logic behind this is that gene copies are relatively specific and that once present they are unlikely to be lost. The best parallel in textual criticism are probably certain types of skips such as line skips. The problem for a copyist having a model with a lineskip is that they cannot restitute the text unless knowing it by heart or having another exemplar. Now, lineskips appear to be no mass phenomenon and other indications for direction are rather rare and relatively unstable. For instance, one could try to classify variants for (linguistic or relative) age as done in the so-called CBGM method (Mink 2004) and then assign direction change to the node on a leaf-leaf path which has relatively most oldest variants.<ref xmlns="http://www.tei-c.org/ns/1.0" target="ftn1" n="1" /> But, the classification could be tricky and require masses of input data. Or one could take a heuristic explained by West (1973:32) who elaborates on groups of manuscripts sharing variants but this criterion may be too weak in our case and in addition lead to some overweighting of vulgate variants. We choose psycholinguistically obtained asymmetric letter confusion probabilities instead. Those are largely constrained to one-to-one aligned letter pairs and in a nutshell give a probability of whether the letter sequence &lt;tap&gt; is more likely to be copied from or to &lt;top&gt;. Hoenen (2018) used these to determine distances (MMD) to compute stemmata and found that they are only interpretable with roughly a third of the cases of variation in the three artificial datasets Parzival (Spencer et al. 2004), Notre Besoin (Baret et al. 2004) and Heinrichi (Roos & Heikkilä 2009). Furthermore due to orthographic depth (Katz & Frost 1992) Finish (Heinrichi) was more processable than English (Parzival) or French (Notre Besoin). We detect that node as most probable DC point on a leaf-leaf path which has a minimal sum of MMD on the way to each leaf. Marmerola et al. (2016) use a heuristic where they take each node in UT as tentative root, sum the weights on all paths to all leafs and finally, comparing all possible roots take the overall minimum cost root. Weights come from one of their similarity functions, but in our implementation, we simply take the Hamming distance (Hamming 1950) and the MMD. Additionally, we implement a similar approach using the MMD distance only. We take each UT node as tentative root, then sum weights not on all paths root-leaf <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">in</hi> but simply on all edges <hi xmlns="http://www.tei-c.org/ns/1.0" rend="italic">of</hi> the actual tree, since our distance matrix is asymmetric and will thus lead to a different sum for each tentative root. We choose the minimum cost root (LCM mincost). Finally, we implement the above outlined approach of the HDCCP root where we use again the letter confusion matrices, this time to determine the most likely DC nodes (<hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#424242)">◇</hi><hi xmlns="http://www.tei-c.org/ns/1.0" rend="color(#424242)">)</hi> on the leaf-leaf paths (HDCCP LCM). We used the matrix of Geyer (1977)<ref xmlns="http://www.tei-c.org/ns/1.0" target="ftn2" n="2" /> for lowercase andPaap et al. (1982) for uppercase.