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Record W4307848414 · doi:10.1098/rstb.2021.0334

Language as a mechanism for reasoning about possibilities

2022· review· en· W4307848414 on OpenAlexafffund
Myrto Grigoroglou, Patricia A. Ganea

Bibliographic record

VenuePhilosophical Transactions of the Royal Society B Biological Sciences · 2022
Typereview
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsHolland Bloorview Kids Rehabilitation HospitalSmiths Detection (Canada)University of Toronto
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsSketchCognitive scienceSyllogismPerspective (graphical)Mechanism (biology)LinguisticsComputer scienceTheme (computing)EpistemologyPsychologyArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

The ability to entertain and reflect on possibilities is a crucial component of human reasoning. However, the origin of this reasoning-whether it is language-based or not-is highly debated. We contribute to this debate by investigating the relation between language and thought in the domain of possibility from a developmental perspective. Our investigation focuses on disjunctive syllogism, a specific type of possibility reasoning that has been explored extensively in the developmental literature and has clear linguistic correlates. Seeking links between conceptual and linguistic representations, we review evidence on how children reason by the disjunctive syllogism and how they acquire logical and modal language. We sketch a proposal for how language and thought interact during development. This article is part of the theme issue 'Thinking about possibilities: mechanisms, ontogeny, functions and phylogeny'.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.005
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.111
GPT teacher head0.376
Teacher spread0.265 · 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
GenreReview

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

Citations13
Published2022
Admission routes2
Has abstractyes

Explore more

Same venuePhilosophical Transactions of the Royal Society B Biological SciencesSame topicChild and Animal Learning DevelopmentFrench-language works237,207