MétaCan
Menu
Back to cohort
Record W2315699712 · doi:10.1037/a0033705

Using imagery perspective to access two distinct forms of self-knowledge: Associative evaluations versus propositional self-beliefs.

2013· article· en· W2315699712 on OpenAlexaff
Lisa K. Libby, Greta Valenti, Karen Anne Hines, Richard P. Eibach

Bibliographic record

VenueJournal of Experimental Psychology General · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPsychologyPerspective (graphical)Associative propertyCognitive psychologyMental imageSocial psychologyCognitionEvent (particle physics)Computer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

When mentally simulating life events, people may visualize them from either an actor's 1st-person or observer's 3rd-person visual perspective. Two experiments demonstrated that visual perspective differentially determines reliance on 2 distinct forms of self-knowledge: associative evaluations of the simulated environment and propositional self-beliefs about relevant values and preferences. Implicit measures indexed associative evaluations of environmental stimuli (political candidates, outgroups); explicit measures indexed propositional self-beliefs about relevant personal values or preferences. A separate session manipulated participants' visual perspective for mentally simulating a pertinent event (voting, interracial interaction) as they forecasted their behavior or feelings if that event occurred. Forecasts corresponded more closely with associative evaluations from the 1st-person than 3rd-person perspective but more closely with propositional self-beliefs from the 3rd-person than 1st-person. Results have practical implications for channeling the power of mental simulation to desired ends and theoretical implications for understanding the pathways by which imagery and mental simulation shape cognition.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.542
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

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

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 head0.102
GPT teacher head0.530
Teacher spread0.428 · 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.

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

Citations32
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Experimental Psychology GeneralSame topicSocial and Intergroup PsychologyFrench-language works237,207