This will be short but sweet. In the spirit of @J.R answer- (in particular the block section)- the information content of any "word" is most certainly less than that in an equivalent random amount of characters in that word (if one is writing) or syllables (if one is speaking.) As anyone who reads @J.R. block passage will agree, predictability is assured- providing we are on the same rational basis. Nothing more is required- anyone with any questions: look up information theory.
a definite answer as requested.
ok so it seems i need to do all the work:
Entropy is defined in the context of a probabilistic model. Independent fair coin flips >have an entropy of 1 bit per flip. A source that always generates a long string of B's has >an entropy of 0, since the next character will always be a 'B'.
The entropy rate of a data source means the average number of bits per symbol needed to encode it. Shannon's experiments with human predictors show an information rate of between 0.6 and 1.3 bits per character, depending on the experimental setup; the PPM compression algorithm can achieve a compression ratio of 1.5 bits per character in English text.
From the preceding example, note the following points:
The amount of entropy is not always an integer number of bits.
Many data bits may not convey information. For example, data structures often store information redundantly, or have identical sections regardless of the information in the data structure.
Shannon's definition of entropy, when applied to an information source, can determine the minimum channel capacity required to reliably transmit the source as encoded binary digits (see caveat below in italics). The formula can be derived by calculating the mathematical expectation of the amount of information contained in a digit from the information source. See also Shannon-Hartley theorem.
Shannon's entropy measures the information contained in a message as opposed to the portion of the message that is determined (or predictable). Examples of the latter include redundancy in language structure or statistical properties relating to the occurrence frequencies of letter or word pairs, triplets etc. See Markov chain.<
right out of wikipedia: http://en.wikipedia.org/wiki/Entropy_(information_theory)
and seeing as the random generation of an alphabetical character- in english in particular- requires 4.7 bits of information (anyone? anyone?) we simply see that there is no possible way for amount of time it takes "to read X number of characters" to scale linearly or "super-linearly" (whatever that means) with X. In particular note in the block quote of @J.R. above we can remove characters and replace them with others but still not alter the information content of the message. This clearly indicates that- as my own block quote above states- that the brain is engaged in a probabilistic modeling of what the words (spoken or written) actually are- and it is not relying on the input of a certain number of characters to ascertain what is communicated.
to the downvoters: sorry but this is well established- and the question is clearly hinting at some sort of compression algorithm- so perhaps this is not really a neuroscience problem ehh? as my shorter answer was lost somehow on those...