MétaCan
Menu
Back to cohort
Record W4386816180 · doi:10.31234/osf.io/4t3m2

Inhibition of Return in a 3D Scene Depends on the Direction of Depth Switch Between Cue and Target

2023· preprint· en· W4386816180 on OpenAlexaff
Hanna Haponenko, Noah Britt, Brett A. Cochrane, Hong‐Jin Sun

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInhibition of returnCued speechSpace (punctuation)PsychologyDepth perceptionCommunicationCognitive psychologyComputer scienceVisual attentionNeurosciencePerception

Abstract

fetched live from OpenAlex

Inhibition of Return (IOR) is a phenomenon that reflects slower target detection when the target appears at a previously cued rather than uncued location. In the present study, we investigated the extent to which IOR occurs in 3D scenes comprised of pictorial depth cues. Peripheral cues and targets appeared on top of 3D rectangular boxes placed on the surface of a textured ground plane in virtual space. It was revealed that when the target appeared at a farther location than the cue, the magnitude of the lateral IOR effect remained similar regardless of whether cues and targets appeared at different depths (i.e., IOR was depth-blind). When the target appeared at a nearer location than the cue, the magnitude of the lateral IOR effect was significantly attenuated (i.e., IOR was depth-specific). The present findings address inconsistencies in the literature on the effect of depth on IOR and support the notion that visuospatial attention exhibits a near space advantage.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.127
GPT teacher head0.344
Teacher spread0.216 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicVisual perception and processing mechanismsFrench-language works237,207