Affectual dynamics in simple sentences.

Abstract

Original attitudes toward words predict attitudes resulting when words are combined into simple assertions. For example, attitudes toward father, kill, and uncle predict how one feels toward all three after assertion the father killed uncle. prediction equation involves terms for original subject attitude (S), original verb attitude (V), and interaction of V and object attitudes (Q). level of prediction is high, with original attitudes accounting for two-thirds of total variance in resultant attitudes. Activity connotations can be predicted in same way, except that a V X Q interaction term is unnecessary. Potency connotations also appear to be predictable, but obtained data do not permit firm generalizations. Recent research by Heider (1967) and Gollob (196S, 1968) has set attitude-balance theory in a linguistic framework, with attitude change being analyzed as a function of attitude combination in sentences. A basic innovation in approach is recognizing that verbs as well as nouns have attitudinal associations, and so a sentence like The Senator kissed baby is to be analyzed in terms of three attitudes rather than in terms of two attitude objects which are associated or dissociated. verb attitude enters into attitude dynamics both additively and through a multiplicative interaction with object. Gollob's research suggests that considering verb attitudes explicitly and quantitatively yields attitude-change predictions of high accuracy. present study builds on insights developed by Heider and Gollob, replicating some of their research and extending it in several directions. Since Gollob's study is more quantitative, it serves as more important guideline and comparison standard for analyses here. An effort is made to replicate Gollob's finding that attitude toward subject of a sentence can be predicted from attitudes associated with each word of sentence. However, in present study original word attitudes are measured directly rather than inferred as in 1 Computations on CDC 1604 computer were supported by National Science Foundation and Wisconsin Alumni Research Foundation.