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Record W4232058571 · doi:10.31234/osf.io/jmurp

Situated embodiment: When physical weight does and does not inform judgments of importance

2020· preprint· en· W4232058571 on OpenAlexaff
David Hauser, Norbert Schwarz

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsQueen's University
Fundersnot available
KeywordsSituatedPsychologyMetaphorSensory systemCognitive psychologySocial psychologyComputer scienceLinguisticsArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Bodily sensations impact metaphorically related judgments. Are such effects obligatory or do they follow the logic of knowledge accessibility? If the latter, the impact of sensory information should be moderated by the accessibility of the related metaphor at the time of sensory experience. We manipulated whether “importance” was on participants’ minds when they held a physically heavy vs. light book. Participants held the book while making an importance judgment vs. returned it before making the judgment (Study 1) or learned prior to holding the book that the study was about “importance evaluations” vs. “graphics evaluations” (Study 2). In both studies, the same book was judged more important when its heft was increased, but only when importance was on participants’ minds at the time of sensory experience. We conclude that sensory experiences only impact metaphorically-related judgments when the applicable metaphor is highly accessible at the time of experience.

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)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
Scholarly communication0.0020.003
Open science0.0000.002
Research integrity0.0010.001
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 head0.022
GPT teacher head0.291
Teacher spread0.269 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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