Intersubjectivity and the Emergence of Words
Bibliographic record
Abstract
Intersubjectivity refers to two non-verbal intersubjective relations infants experience during their first year that are precursors to the emergence of words. Trevarthen, a pioneer in the study of intersubjectivity, referred to those relations as primary and secondary intersubjectivity. The former, a dyadic coordination between the infant and her caregiver, begins at birth. The latter, a triadic coordination that develops around 9 months, allows the infant and a caregiver to share attention to particular features of the environment. Secondary intersubjectivity is crucial for an infant's ability to begin to produce words, at around 12 months. Much research on the social and cognitive origins of language has focused on secondary intersubjectivity. That is unfortunate because it neglects the fact that secondary intersubjectivity and the emergence of words are built on a foundation of primary intersubjectivity. It also ignores the evolutionary origins of intersubjectivity and its uniquely human status. That unique status explains why only humans learn words. This article seeks to address these issues by relating the literature on primary intersubjectivity, particularly research on bi-directional and contingent communication between infants and mothers, to joint attention and ultimately to words. In that context, we also discuss Hrdy's hypothesis about the influence of alloparents on the evolution of intersubjectivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".