Can nonhuman primate signals be arbitrarily meaningful like human words? An affective approach
Bibliographic record
Abstract
Whether one can label nonhuman primate signals as 'meaningful' hinges on what one takes as central features to meaning.If one targets a notion of meaning closely related and comparable to meaning in human words, two features must be identified: firstly, a concrete ascribable meaning to the signal and, secondly, an element of convention or arbitrariness of the signal's meaning.In their seminal paper published in 1980, Seyfarth, Cheney and Marler demonstrated that vervet monkey alarm calls have concrete, discrete, ascribable meaning.But what about their arbitrariness?Here we will suggest a potential way into the investigation of this second feature: Human individuals are capable of comprehending arbitrary word meaning through learning and teaching processes.The current theory suggests in particular that imitation learning and natural pedagogy-like teaching behavior are necessary.For nonhuman primate signals, there is high doubt that learning processes are involved in the acquisition of novel signals, for instance, during ontogeny, and even higher doubt in the involvement of natural pedagogy.We will tackle the question of why complex imitation learning and natural pedagogy is not necessary for animal signals to be arbitrarily meaningful.We will also argue that the framework of ASL -Affective Social Learningcan help us determine whether simple forms of learning and passive forms of (indirect) teaching hinging on affective states of the teacher are involved, allowing for an arbitrary character of nonhuman signals.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".