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Record W4285792880 · doi:10.1162/leon_a_02249

Dancing with Objects: A Psychological and Neurophysiological Analysis

2022· article· en· W4285792880 on OpenAlexaff
Marc Boucher

Bibliographic record

VenueLeonardo · 2022
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversité du Québec en Outaouais
Fundersnot available
KeywordsBody schemaNeurophysiologyPerceptionSchema (genetic algorithms)PhenomenonPsychologyObject (grammar)Haptic technologyHaptic perceptionRelation (database)Space (punctuation)Multisensory integrationCognitive scienceComputer scienceCognitive psychologyArtificial intelligenceEpistemologyNeuroscience

Abstract

fetched live from OpenAlex

Abstract This article uses the psychological concept of body schema and the neurophysiological notion of peripersonal space to discuss the phenomenon of dancing bodies that wear, handle, and share objects. The author shows the complex and dynamic relationship between body and object to be central to the experience of dancing with objects, which is investigated in terms of multisensory integration, most notably in relation to proprioceptive, haptic, and tactile perception. It is posited that, although stemming from different theoretical approaches, both the psychological and neurophysiological perspectives demonstrate how the body incorporates and is incorporated by the things it moves with.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.297
Teacher spread0.274 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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