Preference-driven biases in decision makers’ information search and evaluation
Bibliographic record
Abstract
Abstract While it is well established that the search for information after a decision is biased toward supporting that decision, the case of preference-supporting search before the decision remains open. Three studies of consumer choices consistently found a complete absence of a pre-choice bias toward searching for preference-supporting information. The absence of this confirming search bias occurred for products that were both hedonic and utilitarian, both expensive and inexpensive, and both high and low in expected brand loyalty. Experiment 3 also verified the presence of the expected post-choice search bias to support the chosen alternative. Therefore the absence of a pre-choice search bias in all three studies was not likely to be due to our using a method that was so insensitive that a search bias would not be observed under any circumstances. In addition to the absence of an effect of prior preferences on information selection, subjects’ self-reported search strategies exhibited a clear tendency toward a balance of positive and negative information. Across the three studies, we also tested for the presence of a preference-supporting bias in the evaluation of the information acquired in the search process. This evaluation bias was found both pre- and post-choice.
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 | 0.022 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".