ht and (+) have been analogized to what is called algorithmic complexity, which quantifies a computer algorithm’s minimal representation of a symbol sequence as it grows longer (Chaitin, 1974; Bennett, C.H., 1982; Nicolis, 1986; Rissanen, 1982; Crutchfield and Young, 1989b). Examples of applications of a pseudocomputational compression scheme have quantified differences among protein sequences (Ebling and Jimenez-Montano, 1980), discriminated therapist- directed “transference” manifestations in verbally encoded processes in psychotherapy (Rapp et al, 1991), characterized neural spike train patterns in a penicillin kindled spike focus (Rapp et al, 1994), differentiated among spike sequence patterns of biogenic amine families of brain stem neurons (Mandell and Selz, 1994) and as a sample length-dependent rate, in content-free, mouse driven computer tasks differentiated borderline from obsessive-compulsive personality patterns (Selz and Mandell, 1997). Computation of lexical complexity is a good example of this approach. This procedure recursively surveys the sequence of symbols for the longest word, where “words” are subsequences that appear at least three times if they contain two letters or at least twice if they contain more than two letters. Upon finding a longest repeated word, the compression algorithm replaces all occurances of this word with a single distinct (new) symbol and looks again for the longest repeated word in the modified sequence. When the sequence cannot be further recursively compressed, there may remain identical adjacent symbols in the sequence. These are coded as the symbol raised to the power of the number of its adjacent occurances. This exponent cannot exceed five because six adjacent identical symbols would be two occurances of a three letter word. The numerical value of the lexical complexity is simply the sum of the number of distinct symbols and the (sum of the) logarithm of the exponents of the symbol sequences (Ebling and Jimenez-Mon