The Role of Multimedia in Changing First Impression Bias
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it