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Record W2158662668 · doi:10.1177/0956797611398494

When Does Feeling of Fluency Matter?

2011· article· en· W2158662668 on OpenAlex
Claire I. Tsai, Manoj Thomas

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePsychological Science · 2011
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Toronto
FundersMcGill University
KeywordsFluencyPsychologyFeelingProcessing fluencyCognitive psychologyPriming (agriculture)Verbal fluency testValue (mathematics)Social psychologyCognitionNeuropsychologyComputer scienceNeuroscienceMathematics education

Abstract

fetched live from OpenAlex

It has been widely documented that fluency (ease of information processing) increases positive evaluation. We proposed and demonstrated in three studies that this was not the case when people construed objects abstractly rather than concretely. Specifically, we found that priming people to think abstractly mitigated the effect of fluency on subsequent evaluative judgments (Studies 1 and 2). However, when feelings such as fluency were understood to be signals of value, fluency increased liking in people primed to think abstractly (Study 3). These results suggest that abstract thinking helps distinguish central decision inputs from less important incidental inputs, whereas concrete thinking does not make such a distinction. Thus, abstract thinking can augment or attenuate fluency effects, depending on whether fluency is considered important or incidental information, respectively.

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.

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 categoriesInsufficient 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 score0.274
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0360.002

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 head0.169
GPT teacher head0.458
Teacher spread0.289 · 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