Emotional Factors in Attitudes and Persuasion
Bibliographic record
Abstract
Abstract In this chapter we examine the role of emotional factors in attitudes and persuasion. Attitudes refer to people’s global evaluations of any object, such as oneself, other people, possessions, issues, abstract concepts, and so forth. Thus a person’s dislike of ice cream and favorable predisposition toward a political candidate are examples of attitudes. Persuasion is said to occur when a person’s attitude changes. Change can refer to moving from no attitude to some attitude or from one attitude to another. Persuasion can be very explicit and blatant, such as when a person sets out to modify another’s evaluation and provides a strong communication against the other’s point of view, or it can be rather implicit and subtle, such as when a person’s attitude changes simply because the attitude object (e.g., one’s car) shifts from being associated with pleasant to unpleasant outcomes. Classic treatises on persuasion have held that understanding emotion is critical to understanding attitude change. For example, Aristotle’s Rhetoric described how to make an audience feel specific emotions, such as anger or fear, and then how to use these emotions to influence the audience (see also Cicero, 55 B.C./1970). The importance of emotional factors was also recognized in some of the earliest contemporary work on attitudinal processes. We begin by discussing work on the structure of attitudes because it is in this work that emotional factors first received substantial attention as a theoretical construct. Then we turn to the role of affect in producing attitude change. Attitude structure refers to the underlying foundation, components, and organization of a person’s evaluation. Probably the first major attitude structure theory to feature affect prominently was the tripartite theory of attitudes. According to advocates of this perspective (e.g., Insko & Schopler, 1967; Katz & Stotland, 1959; Rosenberg & Hovland, 1960; Smith, 1947), attitudes can be conceptualized as made up of three components: affective, cognitive, and behavioral. The affective component consists of positive and negative feelings associated with the attitude object. The cognitive component comprises beliefs about and perceptions of the attitude object. Finally, the behavioral component is made up of response tendencies and overt actions related to the attitude object.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".