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Record W2327829656 · doi:10.1037/a0032233

Moving away from a bad past and toward a good future: Feelings influence the metaphorical understanding of time.

2013· article· en· W2327829656 on OpenAlexaff
Albert Lee, Li‐Jun Ji

Bibliographic record

VenueJournal of Experimental Psychology General · 2013
Typearticle
Languageen
FieldPsychology
TopicPsychological and Temporal Perspectives Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsFeelingPerspective (graphical)PsychologyRuminationId, ego and super-egoCognitive psychologyMoodEvent (particle physics)Social psychologyValence (chemistry)CognitionComputer science

Abstract

fetched live from OpenAlex

People move close to things they like and away from things they dislike. Can the same be applied to temporal events? Through alternating between the ego-moving and time-moving metaphorical perspectives of time, people can manage the psychological distance between themselves and various temporal events by staying away from unpleasant experiences and bringing pleasant ones within reach. Consistent with theoretical predictions, 4 studies showed that recalling an unpleasant event from the past prompted the ego-moving perspective, whereas recalling a pleasant past event prompted the time-moving perspective. In contrast, anticipating a pleasant future invoked the ego-moving perspective, whereas anticipating an unpleasant future invoked the time-moving perspective. The valence of feelings explained the systematic shifts in how time is metaphorically understood. These findings highlight the role of basic psychological processes in temporal reasoning. Clinical implications for rumination and mood disorders are discussed.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0040.002
Open science0.0000.002
Research integrity0.0010.001
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 head0.063
GPT teacher head0.378
Teacher spread0.315 · 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

Citations36
Published2013
Admission routes1
Has abstractyes

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