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Record W2409169330 · doi:10.1037/xhp0000142

Mine in motion: How physical actions impact the psychological sense of object ownership.

2015· article· en· W2409169330 on OpenAlexaff
Grace Truong, Craig S. Chapman, Joseph D. Chisholm, James T. Enns, Todd C. Handy

Bibliographic record

VenueJournal of Experimental Psychology Human Perception & Performance · 2015
Typearticle
Languageen
FieldNeuroscience
TopicFace Recognition and Perception
Canadian institutionsUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsRecallObject (grammar)Action (physics)Relevance (law)PsychologySelfCognitive psychologyMotion (physics)Social psychologyTest (biology)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Our attention and memory can be biased toward objects having high self-relevance, such as things we own. Yet in explaining such effects, theorizing has been limited to psychological determinants of self-relevance. Here we examined the contribution physical actions make to this ownership bias. In Experiment 1, participants moved object images on a touch interactive table that either arbitrarily belonged to "self" or "other," and that were moved into locations closer or farther from their bodies. Subsequent recognition was highest for self-owned objects moved closer to the body, as measured via a subsequent memory recall test. In Experiment 2, when participants moved images via keyboard rather than overt action, the proximity effect of the body on attention was abolished. In Experiment 3, participants pulled or pushed self-owned or other-owned object images to side-by-side locations on a touch interactive table. Self-owned objects that were pulled were recognized the most. Our findings demonstrate that physical actions can have a direct impact on the psychological saliency of owned objects, with the act of bringing objects toward the self leading to greater recall.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.232
GPT teacher head0.454
Teacher spread0.222 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2015
Admission routes1
Has abstractyes

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