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Record W2399784120 · doi:10.1037/dev0000113

Aiming to complete the matrix: Eye-movement analysis of processing strategies in children’s relational thinking.

2016· article· en· W2399784120 on OpenAlexaff
Zhe Chen, Ryan Honomichl, Diane Kennedy, Enda Tan

Bibliographic record

VenueDevelopmental Psychology · 2016
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPsycINFOPsychologyCognitive psychologyCognitionTask (project management)Eye trackingRelation (database)Developmental psychologyCognitive developmentEncoding (memory)Cognitive scienceComputer scienceArtificial intelligenceMEDLINE

Abstract

fetched live from OpenAlex

The present study examines 5- to 8-year-old children's relation reasoning in solving matrix completion tasks. This study incorporates a componential analysis, an eye-tracking method, and a microgenetic approach, which together allow an investigation of the cognitive processing strategies involved in the development and learning of children's relational thinking. Developmental differences in problem-solving performance were largely due to deficiencies in engaging the processing strategies that are hypothesized to facilitate problem-solving performance. Feedback designed to highlight the relations between objects within the matrix improved 5- and 6-year-olds' problem-solving performance, as well as their use of appropriate processing strategies. Furthermore, children who engaged the processing strategies early on in the task were more likely to solve subsequent problems in later phases. These findings suggest that encoding relations, integrating rules, completing the model, and generalizing strategies across tasks are critical processing components that underlie relational thinking. (PsycINFO Database Record

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)0.001
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 score0.055
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.029
GPT teacher head0.343
Teacher spread0.314 · 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

Citations24
Published2016
Admission routes1
Has abstractyes

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