Bibliographic record
Abstract
People are confident in their opinions, believing that they see events objectively. Because people believe they interpret the world logically and without bias, they conclude that more people agree with their views than actually do – an effect called false consensus. People also believe that those who disagree with them are biased by ideologically extreme beliefs – an effect called false polarization. Past research finds that demonstrating the effects of biased perception through visual illusions instills doubt that one’s perceptions match reality, thereby decreasing confidence in and closed-minded adherence to their views. We examined whether this procedure would reduce the tendency to engage in false consensus and false polarization, and if high scores in certain personality traits could reduce these tendencies. Four hundred and six participants were shown a number of visual illusions, which demonstrated that the brain engages in hidden work that can result in erroneous perception. We were unable to replicate the previous findings that this procedure reduces confidence in one’s beliefs, and as such, this procedure did not reduce the tendency to engage in false consensus or false polarization. The personality variables that were examined are Need for Cognitive Closure (NFCC), Personal Need for Structure (PNFS), and Need for Cognition (NFC). Overall, the effects of personality on the cognitive biases of interest were negligible. Discipline: Psychology Honours Faculty Mentor: Dr. Craig Blatz
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.
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 | 0.001 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".