Substitution Sensitivity and the Bat-and-Ball Problem: A Direct Replication of De Neys et al. (2013)
Bibliographic record
Abstract
Background: Cognitive misers are no happy fools. Earlier findings (1) came to this conclusion by assessing people’s sensitivity to attribute substitution, which they defined as the situation that occurs when we are confronted with a problem that demands greater cognitive effort, for which we rely on automatic and intuitive processes that substitute the complex situation for an easier one. Methods: Through the exploration of the “bat-and-ball” problem, (2) De Neys, Rossi, and Houdé (1) found that participants were indeed sensitive to the substitution bias. Specifically, participants who incorrectly answered the question that gave rise to the substitution bias were significantly less confident in their answer relative to their answer on a control problem that did not give rise to the substitution. Using the same methods, we conducted a direct replication study on a sample of 264 undergraduate psychology students. Results and Conclusion: Our results suggest that we successfully replicated the original conclusions; participants who answered by substituting the difficult question for an easier one significantly (p<.0001) decreased their confidence ratings on the version of the problem that gave rise to the substitution bias, relative to the problem that did not. Limitations: Though there may have been limitations, it seems that we are sensitive to attribute substitution.
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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 | 0.017 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".