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Novelty Is Not Always the Best Policy

2009· article· en· W2126699625 on OpenAlexfundno aff
Michael D. Dodd, Stefan Van der Stigchel, Andrew Hollingworth

Bibliographic record

VenuePsychological Science · 2009
Typearticle
Languageen
FieldComputer Science
TopicVisual Attention and Saliency Detection
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInhibition of returnVisual searchNoveltyPsychologyMemorizationFacilitationSaccadic maskingCognitive psychologyEye movementArtificial intelligenceCommunicationVisual attentionSocial psychologyComputer scienceNeurosciencePerception

Abstract

fetched live from OpenAlex

We report a study that examined whether inhibition of return (IOR) is specific to visual search or a general characteristic of visual behavior. Participants were shown a series of scenes and were asked to (a) search each scene for a target, (b) memorize each scene, (c) rate how pleasant each scene was, or (d) view each scene freely. An examination of saccadic reaction times to probes provided evidence of IOR during search: Participants were slower to look at probes at previously fixated locations than to look at probes at novel locations. For the other three conditions, however, the opposite pattern of results was observed: Participants were faster to look at probes at previously fixated locations than to look at probes at novel locations, a facilitation-of-return effect that has not been reported previously. These results demonstrate that IOR is a search-specific strategy and not a general characteristic of visual attention.

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)0.004
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0030.006
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.005

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 head0.072
GPT teacher head0.410
Teacher spread0.338 · 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 designNot applicable
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

Citations74
Published2009
Admission routes1
Has abstractyes

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