Pupil dilation reflects the difficulty of evaluations in decisions under risk
Bibliographic record
Abstract
Background: Changes in pupil size can reflect not only illumination but also arousal and other cognitive processses. Studies of decision making have shown pupil dilation related to errors in judging uncertainty, and shifts between task engagement and disengagement, which may be related to phasic noradrenalin activity. Objective: We asked whether pupil dilation also indexes judgments made in decisions under risk, when participants must weigh the knowable value of different prospects to make a choice. Method: 19 subjects had to choose between 2 explicitly described prospects, one having higher probability, the other having larger magnitude of reward. Probability and magnitudes varied so that the difference in the expected values between the two prospects varied between 3% to 23%, with half of trials having larger expected value on the side of the larger reward, and half having greater expected value on the side with greater probability. Subjects were given 4 seconds to make a choice and a video eye tracker recorded pupil sizes during that interval. We analysed pupil dilation, operationalized as the difference between the initial minimum pupil size and the maximum pupil size, and examined its relation to various parameters involving reward magnitude, probability, expected value, and mean variance. Results: Pupils reached maximum size at around 2250ms. Pupil dilation was correlated with the sum of reward magnitude of the two prospects (r=0.52), and there were trends to an inverse relationship with the percent difference in expected value between the two prospects (r= -0.48), and the percent difference in mean variance, which indexes the difference in riskiness (r= - 0.49). Pupil dilation did not vary with probability information. Conclusion: Pupil sizes can reflect both reward size and the degree of difficulty involved in decisions under risk when choosing between prospects. Meeting abstract presented at VSS 2012
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".