"Never tell me the odds" do people emphasize value or probability when choosing between alternatives?
Bibliographic record
Abstract
When people have to select and then aim to one of two target-penalty prospects that have equal maximum expected gain (MEG), they tend to select prospects with a higher probability of target hits than prospects with lower penalty values (Neyedli & Welsh, 2015). The present study explored whether participants held this tendency when selecting the prospect via a key-press (i.e., a non-motor task) without prior aiming experience thus having little feedback on the outcome of their decision. Participants chose between prospects via left/right key-presses that 1) had different MEG, with either only the values (Penalty condition) or probabilities (Distance condition) differing between prospects; and 2) had similar MEG (Similar condition), with one prospect having a higher probability of hitting the target but a higher penalty value and the other having a lower probability of hitting the target but a lower penalty value. In the Penalty and Distance conditions, participants chose the prospect with the larger MEG. In the Similar MEG condition participants, on a group level, chose the prospects with higher probability and with lower value equally. However, a participant-by-participant analysis revealed 3 subgroups: those with value preferences, probability preferences, or no preference. Interestingly, performance metrics during a motor task (i.e., variable and constant error) and small variations in MEG difference between prospects in the Similar condition did not predict choice behaviour. Thus, probability preference is not consistent across individuals when interaction with target-penalty prospects and prior motor experience is not given.Acknowledgments: Dr. Tim Welsh, Dr. Heather Neyedli, and Joseph Manzone (PhD Candidate)
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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".