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Record W4233506222 · doi:10.31234/osf.io/yjqm7

Evolutionary Linguistics: A New Look at an Old Landscape

2016· preprint· en· W4233506222 on OpenAlexaff
Marc D. Hauser, David Barner, Tim O’Donnell

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicLanguage and cultural evolution
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFocus (optics)PluralSyntaxLinguisticsGenerative grammarHierarchySemantics (computer science)Computer scienceCognitive sciencePsychologyPhilosophyProgramming language

Abstract

fetched live from OpenAlex

This article explores the evolution of language, focusing on insightsderived from observations and experiments in animals, guided by currenttheoretical problems that were inspired by the generative theory ofgrammar, and carried forward in substantial ways to the present bypsycholinguists working on child language acquisition. We suggest that overthe past few years, there has been a shift with respect to empiricalstudies of animals targeting questions of language evolution. Inparticular, rather than focus exclusively on the ways in which animalscommunicate, either naturally or by means of artificially acquired symbolsystems, more recent work has focused on the underlying computationalmechanisms subserving the language faculty and the ability of nonhumananimals to acquire these in some form. This shift in emphasis has broughtbiologists studying animals in closer contact with linguists studying theformal aspects of language, and has opened the door to a new line ofempirical inquiry that we label evolingo. Here we review some of theexciting new findings in the evolingo area, focusing in particular onaspects of semantics and syntax.With respect to semantics, we suggest thatsome of the apparently distinctive and uniquely linguistic conceptualdistinctions may have their origins in nonlinguistic conceptualrepresentations; as one example, we present data on nonhuman primates andtheir capacity to represent a singular–plural distinction in the absence oflanguage. With respect to syntax, we focus on both statistical andrule-based problems, especially the most recent attempts to exploredifferent layers within the Chomsky hierarchy; here, we discuss work ontamarins and starlings, highlighting differences in the patterns of resultsas well as differences in methodology that speak to potential issues oflearnability. We conclude by highlighting some of the exciting questionsthat lie ahead, as well as some of the methodological challenges that faceboth comparative and developmental studies of language evolution.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0060.004
Science and technology studies0.0050.038
Scholarly communication0.0110.041
Open science0.0020.007
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.024
GPT teacher head0.308
Teacher spread0.284 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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