The Vancouver Roulette test: a new measure of decision-making under risk
Bibliographic record
Abstract
Background: Neurological disorders may impair decisions involving reward, leading to behaviours such as pathological gambling or excessively risk-averse attitudes: this creates a need for clinical tests to assess different aspects of risky behaviour. Objective: Our aim was to create a realistic test of decisions "under risk" (i.e. when probabilities and sizes of the sums involved are known) with each chance of a win offset by the possibility of loss. We examined thresholds determining when healthy subjects are willing to engage in risky behaviour, and how confidence in a prospect changes with expected value. Methods: 17 healthy subjects were presented with a ‘roulette wheel’ scenario, with varying probabilities and reward magnitudes. The wheel depicted the probability of winning, ranging from 0.2 to 0.8, while a numeral depicted the payout, ranging from 0.8 to 3.2 times their bet. Subjects could decline to play or choose to bet 1 to 3 coins. If they did not win, they lost the sum bet. The dependent variables were the proportion of times when subjects chose to bet 1 or more, 2 or more, or 3 coins. At the end, subject received their winnings. Results: The 50% threshold for participation occurred at an expected value of -0.21 (i.e. on average subjects would lose 20% of their bet). The willingness to bet more coins was a linear function of expected value, with an increase of expected value of 0.45 per added coin. Secondary analyses showed that subjects were twice as responsive to changes in expected value generated by altered probability than to changes from altered magnitude. Conclusion: Our paradigm shows a slight risk-prone tendency in healthy subjects, a linear increase in the confidence of the gamble as a function of expected value, and greater influence of probability than magnitude information in decisions under risk. Meeting abstract presented at VSS 2012
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".