The first prior: From co-embodiment to co-homeostasis in early life
Bibliographic record
Abstract
The idea that whatever we perceive now is influenced by whatever we perceived before lies at the core of Predictive Processing (PP) theories in philosophy and computational neuroscience. If this is so, then it becomes crucial to look at how perception, cognition and actions get off the ground from square one, in utero. Here we examine how humans self-regulate their homeostatic bodily states and build their most basic self- and world-model, literally through others’ bodies, in utero. Indeed, one basic yet overlooked aspect of current embodied and PP approaches is that brains (and minds), and human bodies, first develop within another human body. Crucially, while not all humans will have the experience of being pregnant or carrying a baby, the experience of being carried and growing within another person’s body is universal. Specifically, we define in utero development as a process co-embodiment and co-homeostasis, and highlight their close relationship. We show that the case of pregnancy offers a clear and fundamental example of co-embodiment, building upon theoretical and empirical work tackling the emergence of perceptual experiences in utero. We focus on pregnancy, a case where two individuating organisms literally grasp/ grip one into each other. Contrary to the common view of the foetus being passively ‘contained’ and solipsistically ‘trapped’ in the solitude of the womb, we will present evidence speaking in favour of an active and bidirectional co-regulation between the two living bodies, what we will call co-homeostasis. The co-embodiment and co-homeostasis theses will lay the preliminary ground for introducing a predictive processing reading of in utero development of perceptual experiences.We conclude that when it comes to understanding the nature of predictive perceptions, the infant is father to the human - to paraphrase the famous Wordsworth metaphor.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".