Doing things intentionally: Probability raising and control
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Intentionality judgments can depend on probability raising—people are more likely to see a desired outcome as intentional if the agent who produced it did something to increase its odds. However, intentionality also depends on related factors such as the agent's skill, ability, and control over the outcome. In three experiments (total N = 1074), we investigated how probability raising relates to these factors, and whether it makes distinct contributions to judgments of intentionality. Participants saw vignettes where an agent got a winning ball from a lottery machine. In all experiments, participants gave higher ratings of both intentionality and control in conditions where the agent increased her odds of success than in conditions where she did not. This pattern suggests that probability raising and control are closely linked. The findings of our third experiment, though, also suggest that probability raising may uniquely contribute to attributions of intentionality. In this experiment, the agent received a winning ball after taking an action that unpredictably either increased or decreased her odds of success. Participants gave higher intentionality ratings when this action happened to increase the odds. But participants also showed this pattern when rating control, even though the agent's control did not vary across conditions. These results suggest that probability raising contributes to intentionality even when control does not, and moreover suggest that people may use probability raising to inform attributions of control. However, we also discuss the possibility that control and probability raising are not distinct, and amount to the same thing.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it