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Record W2008917785 · doi:10.1167/13.9.513

The Influence of Scene Context on Parafoveal Processing of Objects

2013· article· en· W2008917785 on OpenAlexaff
Eduardo Manoel Pereira, Monica S. Castelhano

Bibliographic record

VenueJournal of Vision · 2013
Typearticle
Languageen
FieldComputer Science
TopicVisual Attention and Saliency Detection
Canadian institutionsQueen's University
Fundersnot available
KeywordsFixation (population genetics)SaccadePsychologyEye movementCognitive psychologyFixation pointObject (grammar)Context (archaeology)CommunicationArtificial intelligenceComputer scienceMedicineGeography

Abstract

fetched live from OpenAlex

Previous research in reading has shown that information about a word is obtained from the parafovea before it is directly fixated, which speeds the processing of that word once it is subsequently fixated (Rayner, 1975). Further research has shown that contextual constraints (e.g. the predictability of a word) lead to an increase in information acquired from the parafovea (Balota, Rayner & Pollatsek, 1985; McClelland & O’Regan, 1981). Studies have also shown similar effects of contextual constraints on the processing of objects (Biederman, Mezzanotte & Rabinowitz, 1982; Henderson & Hollingworth, 1998). In the present study, we examine whether scene context constrains parafoveal processing of objects prior to fixation. Participants were shown a preview of the target object at either 4° (parafovea) or 10° (periphery) from the point of fixation in either a consistent or inconsistent scene context. The preview could be: (i) identical to the target; (ii) a different category with the same shape; (iii) a different category and different shape; or (iv) a black rectangle control. During the saccade towards the preview object, it changed to the target. In Experiment 1, participants had better performance for identical and similarly-shaped previews, with a stronger benefit for consistent contexts. In Experiment 2, participants had to verify whether the target matched a given object name seen before the trial commenced. Results showed a higher sensitivity (A-prime) for consistent vs. inconsistent contexts. In Experiment 3, participants verified the object name at the end of the trial. The added uncertainty of the target identity led to no difference in sensitivity between context conditions, but did demonstrate a difference in bias (B''D) toward ‘yes’ responses between consistent and inconsistent scene contexts. Across the three experiments, we show that scene context does have a constraining effect on preview processing of objects prior to fixation. Meeting abstract presented at VSS 2013

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)0.000
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 score0.830
Threshold uncertainty score0.123

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.296
Teacher spread0.283 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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