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Record W2766365052 · doi:10.3389/fpsyg.2017.01918

How Much of Language Acquisition Does Operant Conditioning Explain?

2017· article· en· W2766365052 on OpenAlex
Christopher B. Sturdy, Elena Nicoladis

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Psychology · 2017
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of AlbertaWomen and Children’s Health Research Institute
Fundersnot available
KeywordsOperant conditioningPsychologyLanguage acquisitionMistakeCognitive psychologyFocus (optics)Cognitive scienceLinguisticsReinforcementSocial psychologyMathematics education

Abstract

fetched live from OpenAlex

Since the 1950s, when Chomsky argued that Skinner's arguments could not explain syntactic acquisition, psychologists have generally avoided explicitly invoking operant or instrumental conditioning as a learning mechanism for language among human children. In this article, we argue that this is a mistake. We focus on research that has been done on language learning in human infants and toddlers in order to illustrate our points. Researchers have ended up inventing learning mechanisms that, in actual practice, not only resemble but also in fact are examples of operant conditioning (OC) by any other name they select. We argue that language acquisition researchers should proceed by first ruling out OC before invoking alternative learning mechanisms. While it is possible that OC cannot explain all of the language acquisition, simple learning mechanisms that work across species may have some explanatory power in children's language learning.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.322
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it