Demand Avoidance in Value-Based Choice Under Risk: A Behavioral and Pupillometric Examination
Bibliographic record
Abstract
Why does decision-making sometimes feel demanding while other times feel effortless? Thedominant view of cognitive effort suggests that, else being equal, individuals prefer to avoidmentally effortful courses of action—an empirical phenomenon which has been well-studied incognitive control paradigms. However, less work has investigated cognitive demand avoidancein value-based decisions. Here we investigate subjective (self-reported) demand, preferences fordemand, and psychophysiological measures of effort outlay in the context of risky decision-making. Across three experiments (N=199), we observe that individuals evaluate choice pairs—consisting of two options with described risk levels and reward magnitudes—with lessdiscriminable expected value differences as subjectively more demanding. More interestingly,participants exhibit a robust preference for low-effort risky choice pairs in a novel DemandAvoidance Task, which we modeled after well-characterized effort preference paradigms used inthe cognitive control domain. Finally, using pupillometry, we find that participants, contrary toour expectations, exhibit larger task-evoked pupillary responses (TEPRs)—a well-characterizedmeasure of momentary effort exertion—when choosing between low-demand risky choice pairs,and that these TEPR magnitudes predicted demand-avoidant preferences in a subsequent testphase. Together, these results demonstrate that cognitive demand avoidance generalizes beyondcognitive control tasks to risky value-based choice.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".