The Role of Multimedia in Changing First Impression Bias
Bibliographic record
Abstract
First impression bias refers to a limitation of human information processing in which people are strongly influenced by the first piece of information that they are exposed to, and that they are biased in evaluating subsequent information in the direction of the initial influence. The psychology literature has portrayed first impression bias as a virtually “inherent” human bias. Drawing from multimedia literature, this study identifies several characteristics of multimedia presentations that have the potential to alleviate first impression bias. Based on this literature, a set of predictions was generated and tested through a laboratory experiment using a simulated multimedia intranet. Half of the 80 subjects were provided with a biased cue. Subjects were randomly assigned to four groups: (1) text with first impression bias cue, (2) multimedia with first impression bias cue, (3) text without biased cue, and (4) multimedia without biased cue. The experimental task involved conducting a five-year performance appraisal of a department head. The first impression bias cue was designed to provide incomplete and unfavorable information about the department head, but the information provided subsequently was intended to be favorable of his performance. Results show that the appraisal score of the text with biased cue group was significantly lower than the text only (without biased cue) group. On the other hand, the appraisal score of the multimedia with biased cue group was not significantly different from the multimedia only (without biased cue) group. As a whole, the results suggest that multimedia presentations, but not text-based presentations, reduce the influence of first impression bias.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".