Probability and intentional action
Bibliographic record
Abstract
How does probability affect attributions of intentionality? In five experiments (total N = 1410), we provide evidence for a probability raising account holding that people are more likely to see the outcome of an agent’s action as intentional if the agent does something to increase the odds of that outcome. Experiment 1 found that high probability without probability raising does not suffice for strong attributions of intentionality. Participants were more likely to conclude a girl intentionally obtained a desired gumball from a single gumball machine when it offered favorable odds for getting that kind of gumball compared with when it offered poor odds, but their attributions of intentionality were lukewarm. Experiments 2 and 3 then found stronger attributions of intentionality when the girl raised her probability of success by choosing to use machines offering favorable odds over machines offering poor odds. Finally, Experiments 4 and 5 examined whether these effects of probability raising might reduce to consideration of agents’ beliefs and expectations. We found that although these mental states do matter, probability raising matters too—people attribute intentional actions to agents who increase their odds of success, rather than to agents who merely become convinced that success is likely. We discuss the implications of these findings for claims that control and skill contribute to attributions of intentional action.
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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 | 0.008 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".