The Structural Effects of Modality on the Rise of Symbolic Language: A Rebuttal of Evolutionary Accounts and a Laboratory Demonstration
Bibliographic record
Abstract
Why does symbolic communication in humans develop primarily in an oral medium, and how do theories of language origin explain this? Non-human primates, despite their ability to learn and use symbolic signs, do not develop symbols as in oral language. This partly owes to the lack of a direct cortico-motoneuron control of vocalizations in these species compared to humans. Yet such modality-related factors that can impinge on the rise of symbolic language are interpreted differently in two types of evolutionary storylines. (1) Some theories posit that symbolic language originated in a gestural modality, as in "sign languages." However, this overlooks work on emerging sign and spoken languages showing that gestures and speech shape signs differently. (2) In modality-dependent theories, some emphasize the role of iconic sounds, though these lack the efficiency of arbitrary symbols. Other theorists suggest that ontogenesis serves to identify human-specific mechanisms underlying an evolutionary shift from pitch varying to orally modulated vocalizations (babble). This shift creates numerous oral features that can support efficient symbolic associations. We illustrate this principle using a sound-picture association task with 40 learners who hear words in an unfamiliar language (Mandarin) with and without a filtering of oral features. Symbolic associations arise more rapidly and accurately for sounds containing oral features compared to sounds bearing only pitch features, an effect also reported in experiments with infants. The results imply that, beyond a competence to learn and use symbols, the rise of symbolic language rests on the types of signs that a modality of expression affords.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".