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Record W3158377399 · doi:10.24908/iqurcp.8361

Coming to Understand the Social and Physical Worlds

2016· article· en· W3158377399 on OpenAlexvenueno aff
Sydney Hopkins

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2016
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive psychologyPhenomenonAdaptation (eye)PsychologyPerceptionObject (grammar)Lift (data mining)Cognitive scienceTheory of mindComputer scienceCognitionArtificial intelligenceEpistemology

Abstract

fetched live from OpenAlex

Children’s conceptual development has been described as a process of“theory change.” Specifically, children begin with an idea and then iteratively update that idea by combining existing and new information, making and testing predictions and then revising their idea based on new data again. Similar processes have been postulated to account for adaptive phenomenon in perceptual psychology and motor control. The similarities between the two processes suggest that performance on tasks that measure conceptual and sensory‐motor “theory change”respectively may be related. The goal of the present study is to determine whether children’s development in a complex conceptual domain, theory of mind, is associated with children’s performance in a load force adaptation paradigm. Theory of mind is broadly defined as the ability to understand how mental states, such as beliefs and desires, motivate ourown and other people’s actions. In contrast, load force adaptation is the ability to gradually adjust the amount of force exerted on an object in order to smoothly lift it up, as experience with the weight of the object is gained. To explore the mechanisms underlying these two processes, children between the ages of 3.5 and 4.5 years participate in a load force adaptation task and a battery of theory of mind tasks. We predict that since the underlying processes appear to be theoretically similar, the individual differences in the ability to adapt load force and in theory of mind ability will be positively correlated.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.009
Scholarly communication0.0050.007
Open science0.0010.004
Research integrity0.0010.003
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 head0.158
GPT teacher head0.428
Teacher spread0.270 · 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 designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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