| Abstract |
In three experiments on the social induction of food preferences in rats, I found: (a) that eight 30-min exposures of a naive “observer” rat to a “demonstrator” rat fed one of two approximately equipalatable diets produced observer preference for the diet fed to its demonstrator that lasted for more than a month, (b) that simple exposure of naive subjects to a diet itself, rather than to a rat that had eaten a diet, was not sufficient to enhance preference for that diet, and (c) that lasting preference for an unpalatable, piquant diet could also be established by exposing naive rats to demonstrators that had eaten the piquant diet, but not by simply exposure to the piquant diet itself. These findings are consistent with the hypothesis proposed by both Birch and Rozin that social-affective contexts are important in establishing stable, learned preferences for foods. |
| Abstract |
Book Description
The success of the first edition of Generalized Linear Models led to the updated Second Edition, which continues to provide a definitive unified, treatment of methods for the analysis of diverse types of data. Today, it remains popular for its clarity, richness of content and direct relevance to agricultural, biological, health, engineering, and other applications. The authors focus on examining the way a response variable depends on a combination of explanatory variables, treatment, and classification variables. They give particular emphasis to the important case where the dependence occurs through some unknown, linear combination of the explanatory variables. The Second Edition includes topics added to the core of the first edition, including conditional and marginal likelihood methods, estimating equations, and models for dispersion effects and components of dispersion. The discussion of other topics-log-linear and related models, log odds-ratio regression models, multinomial response models, inverse linear and related models, quasi-likelihood functions, and model checking-was expanded and incorporates significant revisions. Comprehension of the material requires simply a knowledge of matrix theory and the basic ideas of probability theory, but for the most part, the book is self-contained. Therefore, with its worked examples, plentiful exercises, and topics of direct use to researchers in many disciplines, Generalized Linear Models serves as ideal text, self-study guide, and reference. |
