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Record W7154575905 · doi:10.48448/9yvv-zr50

Units of representation: Children’s perception of number in the “connectedness illusion”

2025· other· W7154575905 on OpenAlexaff
Cognitive Science Society 2025, Darko Odic, Eloise West

Bibliographic record

VenueUnderline Science Inc. · 2025
Typeother
Language
Field
Topic
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSocial connectednessPerceptionCardinality (data modeling)Representation (politics)Task (project management)Spatial relationIllusion

Abstract

fetched live from OpenAlex

The developmental and evolutionary origins of abstract number reasoning have long been debated. Central to this debate is the underlying unit: whether the quantitative reasoning observed in infants and animals necessitates truly numeric object-level representation or can instead be inferred from covarying low-level spatial frequency. Recent studies with adults rely on the “connectedness illusion” to dissociate cardinality from spatial frequency, suggesting object-level representation is fundamental. However, whether these representations exist early in development remains underexplored. We use the connectedness illusion to test whether 3–6-year-old children enumerate objects or spatial frequency. Children complete a non-symbolic comparison task modeled after He et al. (2009). On 50% of trials, two dots are connected by a line, forming a “barbell.” Results show that, like adults, children underestimate connected displays despite instructions to ignore the connections. These findings suggest that object-level representations, rather than low-level spatial frequency, underlie children’s quantitative reasoning.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.028
GPT teacher head0.328
Teacher spread0.300 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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