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Record W4415813531 · doi:10.1037/dev0002093

Visuospatial-based block-building training for improving emotion understanding and theory of mind in 4.5-year-olds.

2025· article· en· W4415813531 on OpenAlexaff
Melissa Pearl Caldwell, Him Cheung, Tik Sze Carrey Siu

Bibliographic record

VenueDevelopmental Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsTheory of mindEmbodied cognitionPerspective (graphical)Training (meteorology)Perspective-takingDialogicMetacognition

Abstract

fetched live from OpenAlex

= 2.53 months; 53% girls) from Hong Kong participated in a pretest-posttest training study. Participants were randomly assigned to one of three training conditions: (a) block building with verbal cues, (b) block building with visual-verbal cues, or (c) dialogic reading. Each group received 1.5-hr weekly sessions for 6 weeks. Posttraining assessments showed significant gains in visuospatial perspective-taking and social-cognitive abilities, particularly among children in the block-building group with visual-verbal cues. Notably, the visuospatial-based training was as effective as the language-based approach in improving emotion understanding and theory of mind. These findings illuminate the interconnectedness of visuospatial perspective-taking and sociocognitive abilities and suggest that embodied simulation mechanisms may underlie psychological perspective taking. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.061
GPT teacher head0.347
Teacher spread0.285 · 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 designNon-randomized trial
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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