Let me Take the Wheel: Illusory Control and Sense of Agency
Bibliographic record
Abstract
Illusory control refers to an effect in games of chance where features associated with skilful situations increase expectancies of success. Past work has operationalized illusory control in terms of subjective ratings or behaviour, with limited consideration of the relationship between these definitions, or the broader construct of agency. This study used a novel card-guessing task in 78 participants to investigate the relationship between subjective and behavioural illusory control. We compared trials in which participants (a) had no opportunity to exercise illusory control, (b) could exercise illusory control for free, or (c) could pay to exercise illusory control. Contingency Judgment and Intentional Binding tasks assessed explicit and implicit sense of agency, respectively. On the card-guessing task, confidence was higher when participants exerted control than in the baseline condition. In a complementary model, participants were more likely to exercise control when their confidence was high, and this effect was accentuated in the pay condition relative to the free condition. Decisions to pay were positively correlated with control ratings on the Contingency Judgment task, but were not significantly related to Intentional Binding. These results establish an association between subjective and behavioural illusory control and locate the construct within the cognitive literature on agency.
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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 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| 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".