Is there a faculty of deontic reasoning? A critical re-evaluation of abstract deontic versions of the Wason selection task
Bibliographic record
Abstract
Undoubtedly one of the most important studies in evolutionary cognitive psychology is Cosmides' (1989) analysis of content effects on the Wason selection task in terms of adaptive cheater detection. However, in her landmark paper, Cosmides ventured beyond the confines of the selection task to argue for a bold new synthesis of evolutionary biology and cognitive psychology using Marr's (l982) concept of a computational theory as the bridge between these two disciplines. Following Marr's lead, Cosmides argued that cognitive psychology could not make progress unless it was informed by task analyses of the problems the mind was designed to solve. These task analyses would ultimately be supplied by evolutionary theory, which specializes in the study of natural design. Social Contract Theory, Cosmides' specific account of content effects on the Wason selection task, was merely one illustrative part of a larger programme, evolutionary psychology. Unfortunately, the double message of Cosmides' (1989) paper has generated a considerable amount of confusion as some appear to have confused Social Contract Theory as a general theory of reasoning whereas the true scope of the theory is much narrower. In this chapter I hope to dispel some of the confusion surrounding Social Contract Theory and, in the process, clarify where some of the difficulties facing the theory lie and where they do not.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.021 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.009 |
| 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".