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Record W2399589095

Later events lie behind her, but not behind you: Compatibility effects for temporal sequences along the sagittal axis depend on perspective

2013· article· en· W2399589095 on OpenAlexfundno aff
Esther Walker, Benjamin K. Bergen, Rafael Núñez

Bibliographic record

VenueeScholarship (California Digital Library) · 2013
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPerspective (graphical)PsychologyPerspective-takingFrame of referenceCognitive psychologyTime perspectiveCognitive scienceLinguisticsSocial psychologyComputer scienceArtificial intelligencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Perspective plays a large role in how we think about space.Does perspective also influence how we think about abstract concepts, such as time, which have been shown to be closely associated with how we think about space?Linguistic patterns suggest that speakers talk about temporal sequences from two perspectives: field-based and ego perspective (Moore, 2011).However, the psychological reality of these mappings beyond their use in language is unclear.The present study examines whether sequential reasoning recruits the sagittal (front-back) axis differently, depending on the perspective adopted for the task.We manipulated perspective by using pronouns meant to evoke a field-based or ego perspective ("her" vs "your" high school graduation, respectively).Participants made earlierthan or later-than judgments about event sequences using a mouse in front of or behind their body.We observed an interaction between pronoun, temporal reference, and response location.Participants map space onto time differently depending on the frame of reference from which temporal sequences are interpreted.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.005

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 head0.021
GPT teacher head0.264
Teacher spread0.244 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2013
Admission routes1
Has abstractyes

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