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Record W4224238438 · doi:10.1177/10892680221085506

Phenomenological Origins of Psychological Ownership

2022· article· en· W4224238438 on OpenAlexaff
Haider Riaz Khan, John Turri

Bibliographic record

VenueReview of General Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicAction Observation and Synchronization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEmbodied cognitionSense of agencyGestalt psychologyBody schemaPsychologySocial psychologySchema (genetic algorithms)FeelingAgency (philosophy)Construct (python library)EpistemologyCognitive psychologySociologyComputer science

Abstract

fetched live from OpenAlex

Motivated by a set of converging empirical findings and theoretical suggestions pertaining to the construct of ownership, we survey literature from multiple disciplines and present an extensive theoretical account linking the inception of a foundational naïve theory of ownership to principles governing the sense of (body) ownership. The first part of the account examines the emergence of the non-conceptual sense of ownership in terms of the minimal self and the body schema—a dynamic mental model of the body that functions as an instrument of directed action. A remarkable feature of the body schema is that it expands to incorporate objects that are objectively controlled by the person. Moreover, this embodiment of extracorporeal objects is accompanied by the phenomenological feeling of ownership towards the embodied objects. In fact, we argue that the sense of agency and ownership are inextricably linked, and that predictable control over an object can engender the sense of ownership. This relation between objective agency and the sense of ownership is moderated by gestalt-like principles. In the second part, we posit that these early emerging principles and experiences lead to the formation of a naïve theory of ownership rooted in notions of agential involvement.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0480.000

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 head0.139
GPT teacher head0.439
Teacher spread0.300 · 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.

Study designNot applicable
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

Citations7
Published2022
Admission routes1
Has abstractyes

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