Mindreading in Great Apes: Dissolving the Logical Problem and a New Multiple Soft-Indicators Model
Bibliographic record
Abstract
This thesis explores the debated capacity of great apes to mindread, addressing the 'logical problem of mindreading' and advancing an alternative 'multiple softindicators' model of mindreading.The logical problem is that no experimental protocol can be designed (even in principle) that produces evidence that supports a mindreading hypothesis, which posits the inference of unobservable mental states on the part of the ape, over a behaviour-reading hypothesis, which posits that the ape relies solely on observable cues.Challenging this, I utilize Daniel Dennett's framework of the intentional stance to argue that mindreading does not conform to a binary classification but rather exists on a continuum, thereby dissolving the traditional logical problem by reframing it.In chapter four, I introduce a Wittgensteinian-inspired conception of 'mindreading' suggesting that mindreading varies in degree based on the presence and intensity of multiple soft-indicators compared to paradigmatic human cases.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".