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Record W4245518176 · doi:10.31234/osf.io/exqun

Prototypical actions with objects are more easily imagined than atypical actions

2018· preprint· en· W4245518176 on OpenAlexaff
Christopher R. Madan, Adrian K. T. Ng, Anthony Singhal

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
Fundersnot available
KeywordsAction (physics)Object (grammar)Task (project management)Cognitive psychologyPsychologyMental imageFacet (psychology)Computer scienceArtificial intelligenceCognitionSocial psychology

Abstract

fetched live from OpenAlex

Tool use is an important facet of everyday life, though sometimes it is necessary to use tools in ways that do not fit within their typical functions. Here we asked participants to imagine characters using objects based on instructions that fit the prototypical actions for the object or were atypical in a novel object-action imagery task. Atypical action instructions either described sensible, substitute uses of the object, or actions that were bizarre but possible. Participants were better able to imagine the prototypical than atypical actions, but no effect of bizarreness was found. We additionally assessed inter-individual differences in movement imagery ability using two objective tests. Performance in the object-action imagery task correlated with the movement imagery tests, providing a link between motor simulations and mental imagery ability.

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)0.005
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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)0.0030.000

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 head0.093
GPT teacher head0.375
Teacher spread0.282 · 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
Published2018
Admission routes1
Has abstractyes

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