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Record W2907799130 · doi:10.1123/jmld.2015-0001

Do Children Have the Same Capacity to Perceive Affordances as Adults? An Investigation of Tool Selection and Use

2016· article· en· W2907799130 on OpenAlexaff
Sara M. Scharoun Benson, Pamela J. Bryden, Michael E. Cinelli, David A. Gonzalez, Éric Roy

Bibliographic record

VenueJournal of Motor Learning and Development · 2016
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsWilfrid Laurier UniversityUniversity of Waterloo
Fundersnot available
KeywordsAffordanceShovelWrenchPsychologyHammerPerceptionTask (project management)Block (permutation group theory)Selection (genetic algorithm)Object (grammar)Action (physics)Cognitive psychologySocial psychologyCommunicationHuman–computer interactionComputer scienceArtificial intelligenceEngineeringMathematicsStructural engineering

Abstract

fetched live from OpenAlex

This study investigated whether 5- to 11-year-old children perceive affordances in the same way as adults ( M age = 22.93, SD = 2.16) when presented with a task and four tools (nail in a block of wood and a hammer, rock, wrench, and comb; bucket of sand and a shovel, wooden block, rake, and tweezers; and a screw in a block of wood and a screwdriver, knife, dime, and crayon). Participants were asked to select the best tool and act on an object until all four assigned tools had been selected. No explicit instructions were provided because we were interested in how task perception would influence tool selection and action. Results support the notion that the capacity to perceive affordances increases with age. Furthermore, differences in the way in which 5-year-olds acted on the screw in a block of wood demonstrated that the ability to detect some affordances takes longer to refine. Findings help to further the understanding of the development of perception-action coupling.

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)0.004
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.279
Teacher spread0.250 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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