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Record W2516732686 · doi:10.1177/0956797616661544

Ownership Status Influences the Degree of Joint Facilitatory Behavior

2016· article· en· W2516732686 on OpenAlexafffund
Merryn Constable, Andrew P. Bayliss, Steven P. Tipper, Ana Paula Spaniol, Jay Pratt, Timothy N. Welsh

Bibliographic record

VenuePsychological Science · 2016
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
FundersOntario Ministry of Research and InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsPsychologyDegree (music)Joint (building)Social psychologyCognitive psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

When engaging in joint activities, humans tend to sacrifice some of their own sensorimotor comfort and efficiency to facilitate a partner's performance. In the two experiments reported here, we investigated whether ownership-a socioculturally based nonphysical feature ascribed to objects-influenced facilitatory motor behavior in joint action. Participants passed mugs that differed in ownership status across a table to a partner. We found that participants oriented handles less toward their partners when passing their own mugs than when passing mugs owned by their partners (Experiment 1) and mugs owned by the experimenter (Experiment 2). These findings indicate that individuals plan and execute actions that assist their partners but do so to a smaller degree if it is the individuals' own property that the partners intend to manipulate. We discuss these findings in terms of underlying variables associated with ownership and conclude that a self-other distinction can be found in the human sensorimotor system.

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)0.011
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 score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
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)0.0040.000

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 head0.420
GPT teacher head0.375
Teacher spread0.045 · 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

Citations18
Published2016
Admission routes2
Has abstractyes

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