1. Problem and history
Giving a word as a stimulus, different persons respond with different words, e.g. “music” as stimulus can evoke “violin”, “Chopin”, “love”, “melody”, etc. Asking many persons one can observe that the frequency of particular responses (associations) is not equal, on the contrary, the response words can be ranked according to their frequency. The problem is to find the adequate ranking () distribution. Associations are thus both ranking and diversification problems.
Not considering qualitative work and compilation of frequency lists having mostly the character of voluminous books, the first attempt at modelling was probably made by Horvath (1963) who used inductively the Yule distribution. Haight (1966) compared the Borel, the Yule, and the logarithmic distributions with the distribution derived by him for this purpose, called now Haight-zeta distribution (cf. Wimmer, Altmann 1999), but none of them could yield adequate results. Haight and Jones (1974) as well as Lánský and Radil-Weiss (1980) tried another approach but attained good results only in about 50% of cases. Dolinskij (1994, 1988) used for this purpose the Zipf-Alekseev distribution which is a generalization of Zipf distribution. Altmann (1992) has shown that the deviations from this distribution are extremely small (P ≈ 1 in almost all cases) but the foundation of this distribution was established by Hřebíček (1995, 1996, 1997). Altmann (1992) used the usual proportionality approach with speaker-hearer balance and obtained the negative binomial distribution.
The ranking of word associations abides by a regular ranking distribution.
3.1. The Zipf-Alekseev model (Hřebíček 1997: 43)
Hřebíček starts from two assumptions:
(i) The frequency of an association at any rank is proportional to the frequency at the first rank .
(ii) The frequency at rank is inversely proportional to the rank .
Putting this together, we obtain
or, in logarithmic form
The proportionality is given by Menzerath´s law, i.e.
Solving for yields
Usually is so important that one gives it a special value, i.e. one modifies the distribution obtaining
3.2. The negative binomial model (Altmann 1992)
Assumption: The probability at rank is proprotional to the probability at rank , the coefficient of proportionality being . If ranking begins with , one solves the equation for the displaced form, i.e.
and after reparametrization one obtains the 1-displaced negative binomial distribution
where , .
Example: Associations of the word “high”
Altmann (1992) used the associations of the word “high” (4th grade, male) from Palermo, Jenkins (1964) and obtained the result presented in Table 1 and Fig. 1.
Both results are excellent.
4. Authors: U. Strauss, G. Altmann, J. Eom
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