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Record W2405701669

Symbiotic Symbols: Symbolic (but not Nonsymbolic) Number Representation Predicts Calculation Fluency in Adults

2014· article· en· W2405701669 on OpenAlexfundno aff
Adam T. Newton, Rylan J. Waring, Marcie Penner‐Wilger

Bibliographic record

VenueeScholarship (California Digital Library) · 2014
Typearticle
Languageen
FieldMathematics
TopicCognitive and developmental aspects of mathematical skills
Canadian institutionsnot available
FundersKing's University College
KeywordsFluencyNumeracyPsychologyRepresentation (politics)CognitionCognitive psychologySubtractionProcessing fluencyTask (project management)ArithmeticComputer scienceMathematics educationMathematics
DOInot available

Abstract

fetched live from OpenAlex

There is debate in the numerical cognition literature concerning symbolic and nonsymbolic number representation systems as foundations for more complex mathematical skills.The purpose of this study was to investigate the relation between these number representation systems and calculation fluency.The present study used 51 university students.Participants completed symbolic and nonsymbolic magnitude comparison and ordinality tasks on an iPad as well as a penand-paper version of the addition and subtractionmultiplication subtest of the Kit of Factor-Referenced Cognitive Tests (French, Ekstron, & Price, 1963).Data reductions were performed and a symbolic and a nonsymbolic factor were constructed.A multiple regression analysis revealed that the symbolic factor was a significant predictor of calculation fluency, but the nonsymbolic factor was not.Two separate repeated measures ANOVAs revealed 3-way interactions between task, distance, and format for both accuracy and response time.These results support the view that the two systems develop separately.

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 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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.005

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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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 teacher head, not a consensus.

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

Citations4
Published2014
Admission routes1
Has abstractyes

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