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Record W4376621049 · doi:10.1037/xhp0001111

Peri-hand space: A helping hand for faster object recognition in children.

2023· article· en· W4376621049 on OpenAlexafffund
Nikola R. Klassen, Lindsay E Bamford, Jenni M. Karl

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2023
Typearticle
Languageen
FieldPsychology
TopicPsychological Testing and Assessment
Canadian institutionsThompson Rivers University
FundersNatural Sciences and Engineering Research Council of CanadaThompson Rivers University
KeywordsTouchscreenTask (project management)Visual searchEye–hand coordinationPsychologyObject (grammar)Eye trackingAudiologyComputer visionArtificial intelligenceComputer scienceCognitive psychologyMedicineHuman–computer interactionEngineering

Abstract

fetched live from OpenAlex

Visual processing is altered for stimuli located near the hands, in what is termed peri-hand space, but it is unclear whether peri-hand effects are stable across the lifespan. To investigate this, adults and 5- to 8-year-old children completed a naturalistic visual search task on a touchscreen monitor while wearing eye-tracking glasses. Upon recognizing a previously specified target image in a 12-image array, they released a pushbutton with their left index finger in order to reach out and touch the target. Participants completed the task twice, once with their right hand positioned on the monitor beside the visual array and once with their right hand positioned in their lap. Both children and adults were faster at recognizing the target when their right hand was near the array, but the magnitude of this peri-hand effect was greater in children than adults. The results are discussed in relation to the idea that object recognition may be facilitated within peri-hand space to a greater extent during childhood. (PsycInfo Database Record (c) 2023 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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.103
GPT teacher head0.427
Teacher spread0.324 · 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

Citations1
Published2023
Admission routes2
Has abstractyes

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