The Influence of the Level of Representation of Information on Belief Formation and Change
Bibliographic record
Abstract
The upward extrapolation of information from an individual to a category and the downward extrapolation of information from a category to an individual are pervasive social-cognitive processes. People can form beliefs using upward and downward extrapolation processes, as well as by learning information directly about an object (i.e., a process with no extrapolation). These three processes can be conceptualized as levels within the variable: the level of representation of information. Although extrapolation processes have been long recognized in many literatures, level of representation has been largely disregarded as a property of attitude structure and a property of persuasive messages. This program of research consisted of four studies that examined the effects of level of representation on persuasion. Studies One and Two examined upward extrapolation processes. Study Three examined downward extrapolation processes. Study Four examined upward and downward extrapolation processes simultaneously. The findings supported several key principles. First, messages represented at the level of target are generally the most persuasive. Second, messages become decreasingly persuasive as they require more extreme extrapolation processes. Third, the persuasiveness of messages is contingent on the level of representation at belief formation about the target. Persuasive messages represented at the target level are similarly effective irrespective of the level of representation at belief formation, while the persuasiveness of messages represented at non-target levels varies across different levels of representation at belief formation. When the initial beliefs about a target are based on target-level information, persuasive messages represented at a non-target level are generally less effective than a message represented at the target level. However, when the initial beliefs about a target are based on information that requires an extrapolation process that is equally as extreme as or more extreme than the non-target-level message at persuasion, the non-target-level message becomes more effective and the difference in effectiveness between the non-target-level message and the target-level message diminishes. Overall, this program of research has demonstrated that level of representation should be seriously considered as a property of attitude structure and a property of persuasive messages, and thus, should be further investigated as such.
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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 | 0.011 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".