The optimal keyboard design also depends on the distribution of character frequencies F i, which are defined by the text corpus that is appropriate for a given user.
These character frequencies might be estimated from general databases, or might be based on the specific type of text entered by a specific user.
Given the differences in character frequencies, it is not surprising that the optimized assignment of characters to keyboard positions varies with the text corpus, as a comparison of Figs. 11 and 15 demonstrates.
In all likelihood, the observed correlation between the RTs and the first character frequencies was an artifact of the positive correlation between charword frequencies and character frequencies.
The last two were character frequencies (i.e., the frequencies of the characters independent of whether they came from single-character words or from multi-character words).
This table shows (1) that the SUBTL_logCHR index is slightly better than the other measures, and (2) that character frequencies outperform word frequencies.
The first is CCL (http://ccl.pku.edu.cn:8080/ccl_corpus), which gives access to the unsegmented and untagged corpus and provides information about character frequencies but not word frequencies.
In line with what has been found in the other languages, the new word and character frequencies explain significantly more of the variance in Chinese word naming and lexical decision performance than measures based on written texts.
Intriguingly, when we added the character frequencies, we also found a correlation of -.325 for SUBTL_logCHR of the first character in the word but not of the second character (all ps<0.01).
Figure 2 shows the lay-out of the information, which is analogous to that of the character frequencies (except for the fact that WCount is based on total of 33.5 million words).
Following recent work by New, Brysbaert, and colleagues in English, French and Dutch, we assembled a database of word and character frequencies based on a corpus of film and television subtitles (46.8 million characters, 33.5 million words).
