[Blog Post] Rightward and Downward in the Voynich Manuscript - Patrick Feaster

[pfeaster]

In order to investigate the issues pointed out by Emma, I quickly edited one of the scripts to remove all white space and work on character position in the line, rather than word position. Positions are still mapped into 10 discrete slots.

The contrastive distribution patterns among word sets (such as those containing [k] versus those containing [t]) often seem to recapitulate patterns among the characters themselves, so from that standpoint this seems like a defensible strategy.  There may also be differences among sets of words where the characters are initial, medial, or final, but multiple factors could be at play there.  One possibility that could be worth investigating is whether abstract word patterns (e.g., body rank structures) display positional preferences of their own.

I had to cut a few points when turning my written paper submission into a presentation, a couple of which had to do with possible implications for "hot topics" in Voynichology, including your (Emma's and Marco's) discovery of statistical anomalies among word-break combinations.  What I'm wondering is whether differences between predicted and actual token counts for word-break combinations might be due to positional distribution patterns -- e.g., words that end [y] and words that begin [q] tend to occupy similar positions within lines and paragraphs, so the word-break combination [y.q] is overrepresented, whereas the positions of words that end [n] and words that begin [q] don't share much overlap, so the word-break combination [n.q] is underrepresented.

Since finishing the paper, I've tweaked my position-visualization script a bit to work with word-break combinations.  Basically, I subtract the first word from lines when graphing word-initial elements and the last word from lines when graphing word-final elements, so that each pixel (before resizing) now represents a word break rather than a word.  Then I can create two graphs for review:

(1) A color graph with words ending with a particular glyph (the left-hand element in the word-break combination) represented in blue and words starting with another glyph (the right-hand element in the word-break combination) represented in red, each normalized separately so that the area of highest frequency is at 100% brightness.  In RGB color space, blue plus red equals pink, so pink parts of the image should reveal where the distributions of the two word sets overlap.

(2) A grayscale graph of actual tokens of the word-break combination.

Here are graphs for the word-break combination [n.q], which is underrepresented:

   

There's not much pink in the graph on the left, but the graph on the right seems to be brighter where the graph on the left is pinker (or purpler).

Here are graphs for the word-break combination [y.q], which is overrepresented:

   

The graph on the left shows a lot more pink (indicating more overlap), and the brightness in the graph on the right is more evenly spread out throughout the paragraph.

So I think we might have been able to predict that [n.q] would be underrepresented, and that [y.q] would be overrepresented, based just on the degree of overlap in distribution of words ending [n] and [y] and words beginning [q].

I haven't done enough testing to say yet whether this case is typical, but it hints at a possible connection between these two phenomena (uneven positional distributions and statistical anomalies among word-break combinations).