Effects of initial experiences on risky choice
Bibliographic record
Abstract
When people make risky choices based on prior experience, biases in learning and memory can affect their preferences. One such bias is the primacy effect, whereby outcomes experienced during initial learning disproportionately affect later memory and choice. Here we investigated potential primacy effects with two features of risky options: outcome probability and relative value. In the first two experiments (total N = 382), the order of experiencing different outcome probabilities was varied across three groups, and neither experiment revealed any primacy effects. In the last experiment (total N = 390), the relative value of outcomes was manipulated by including an extra wildcard option. This wildcard option sometimes had more extreme outcomes than the rest of the choice set, and sometimes had more moderate outcomes. The order of experiencing the more-extreme wildcard option was manipulated to evaluate potential primacy effects. During initial learning, including the more-extreme wildcard option affected choice as compared to a group with a wildcard that yielded moderate outcomes; adding that same more-extreme wildcard later in the session, however, had no effect on choice. Together, these results suggest that risk preferences based on relative value show a lasting primacy effect, but that learning about outcome probabilities is more continuously updated.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".