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Record W2087683740 · doi:10.1037/a0025883

More than a body: Mind perception and the nature of objectification.

2011· article· en· W2087683740 on OpenAlexfundno aff
Kurt Gray, Joshua Knobe, Mark Sheskin, Paul Bloom, Lisa Feldman Barrett

Bibliographic record

VenueJournal of Personality and Social Psychology · 2011
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyObjectificationSocial psychologyPerceptionAttributionRegulatory focus theoryHarmMentalizationSense of agencySocial perceptionAgency (philosophy)MoralityAction (physics)Cognitive psychologyEpistemology

Abstract

fetched live from OpenAlex

According to models of objectification, viewing someone as a body induces de-mentalization, stripping away their psychological traits. Here evidence is presented for an alternative account, where a body focus does not diminish the attribution of all mental capacities but, instead, leads perceivers to infer a different kind of mind. Drawing on the distinction in mind perception between agency and experience, it is found that focusing on someone's body reduces perceptions of agency (self-control and action) but increases perceptions of experience (emotion and sensation). These effects were found when comparing targets represented by both revealing versus nonrevealing pictures (Experiments 1, 3, and 4) or by simply directing attention toward physical characteristics (Experiment 2). The effect of a body focus on mind perception also influenced moral intuitions, with those represented as a body seen to be less morally responsible (i.e., lesser moral agents) but more sensitive to harm (i.e., greater moral patients; Experiments 5 and 6). These effects suggest that a body focus does not cause objectification per se but, instead, leads to a redistribution of perceived mind.

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)0.008
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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0010.001
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 head0.127
GPT teacher head0.350
Teacher spread0.224 · 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

Citations272
Published2011
Admission routes1
Has abstractyes

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