Anthropofabrication and the redressing of memory: an embodied approach to comparative cognition
Bibliographic record
Abstract
On what basis do researchers posit that humans and other animals share cognitive capacities? We argue that such claims are not based on inherent, pre-existing similarities, but rather emerge through a two-step process, which we will call 'anthropofabrication'. In the initial stage, embodied action-based strategies and environmental context in human studies are ignored owing to the need for measurement and quantification. Consequently, cognitive terms become disconnected from the context to which we apply them, and human classificatory cognitive terms are transformed into broad explanatory terms, assumed to be 'species-neutral'. The second phase entails translating and applying these generalized explanatory terms to specific nonverbal animals in ways that serve to further cloak differences between animals and other species. Here, again, researchers selectively discard contextual information to facilitate the comparison with humans. To limit anthropofabrication, we should (re)acknowledge that cognitive abilities are not species-neutral and cannot be detached from embodied action, perception and their context of occurrence. We illustrate our points about anthropofabrication using the example of memory research. This article is part of the theme issue 'Minds in movement: embodied cognition in the age of artificial intelligence'.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.036 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".