  Building on ideas from linguistics, psychology, and social sciences about the
possible mechanisms of human decision-making, we propose a novel theoretical
framework for the citation analysis. Given the existing trend to investigate
citation statistics in the context of various forms of power and Zipfian laws,
we show that the popular models of citation have poor predictive ability and
can hardly provide for an adequate explanation of the observed behavior of the
empirical data. An alternative model is then derived, using the apparatus of
statistical mechanics. The model is applied to approximate the citation
frequencies of scientific articles from two large collections, and it
demonstrates a predictive potential much superior to the one of any of the
citation models known to the authors from the literature. Some analytical
properties of the developed model are discussed, and conclusions are drawn.
Directions for future work are also given at the paper's end.
