Bibliographic record
Abstract
Commentaries: In the keynote article, Janet Werker advances the proposal that the phonological system and semantic system develop in concert, with each system exerting influences on the other during the early language learning years. Her seminal work has beautifully demonstrated that infants track information from the signal and use visual, motor, and auditory information to make sense of their linguistic world. The account articulated in the target article, along with the supporting body of research from her lab, provides the field with a foundation for exploring the bidirectional relations between the perceptual world and early language learning.In what follows, we expand on two core themes articulated in the target article:first, we discuss how the discrimination of speech sound contrasts helps infants to identify sound categories; and second, we review research demonstrating that the developing sound system shapes early word learning and predicts later language skills. We embed our discussion in a theoretical framework, Processing Rich Information from Multidimensional Interactive Representation (PRIMIR; Werker & Curtin, 2005). This framework helped shape thinking about how infants’ processing systems and representations work in concert during early language development. Further, this framework has highlighted that the developmental level of the child, as well as the input, biases, and task demands are critical in understanding how infants in monolingual and multilingual learning environments (Curtin, Byers-Heinlein, & Werker, 2011) begin to build their linguistic system. That is, to fully understand whether an infant will or will not demonstrate an ability, the task, the specific speech sound(s), and the current state of learning system have to be considered. PRIMIR takes into account indexical information (visual, motor, etc.) contained within the context in which the task is taking place, supporting the assertion that performance and learning is situationally dependent. Couched within this framework, we can begin to explore how divergent experimental results are obtained depending on the developmental level of the child, the task, and the various stimuli used.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.041 | 0.014 |
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".