Reward probability and magnitude in saccadic decisions under risk: measuring bias and sensitivity to expected value
Bibliographic record
Abstract
Objective: Our goal was to evaluate how healthy subjects integrate information of reward magnitude and the likelihood of reward, to determine their threshold sensitivity to differences in value and their choice biases. Background: There are few clinical tests of decisions under risk. The most well-known, the Iowa Gambling task, assesses the ability to learn cumulative probabilities of gain or loss and to forego large rewards for smaller ones. However, tests that characterize sensitivity to expected value and biases between reward magnitude versus probability may provide insights in conditions with anomalous reward-related behaviour. Design/Methods: Twenty subjects were required in 170 trials to choose between two explicitly described prospects, one having higher probability of reward but lower magnitude of reward than the other. The sizes of reward and the degree of probability were varied so that the difference in expected value between the two prospects varied from 3% to 23%. We first plotted choice as a function of expected value. Second, we used Prospect Theory to evaluate choice as a function of perceived value, using an exponential function for perceived reward utility and a single-parameter Prelec function for perceived probability. Results: Subjects showed a threshold sensitivity of 9.0% difference in expected value. Regarding choice bias, we found a ‘risk premium’ of 9.4%, indicating a slight tendency to choose higher probability over higher reward. Prospect Theory analysis showed that this risk premium is the predicted outcome of non-linearities in the subjective perception of reward value and probability. Conclusions: This simple test provides a robust measure of discriminative value thresholds and biases in decisions under risk. Prospect Theory makes predictions about choice patterns when perception of reward or probability is anomalous, as may occur in populations with dopaminergic or striatal dysfunction, such as Parkinson's disease and schizophrenia.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".