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Record W2029176470 · doi:10.1167/11.11.128

Individual differences in object-based attention effects in discrimination and detection tasks

2011· article· en· W2029176470 on OpenAlexaff
Karin S. Pilz, A. B. Roggeveen, Sarah E. Creighton, Patrick Bennett, Allison B. Sekuler

Bibliographic record

VenueJournal of Vision · 2011
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsSheridan CollegeYork UniversityMcMaster University
Fundersnot available
KeywordsHorizontal and verticalPsychologyBootstrapping (finance)Cognitive psychologyTask (project management)Orientation (vector space)Object (grammar)Computer scienceCommunicationArtificial intelligenceMathematicsGeometry

Abstract

fetched live from OpenAlex

It has been long suggested that attention can be allocated to both space and objects. Previously, Roggeveen et al. (VSS 2008) used a target discrimination task (Moore, Yantis, & Vaughan, 1998) to investigate individual differences in object-based attention (OBA) for both vertical and horizontal objects. Interestingly, although we found evidence of space based attention in both orientations, we found OBA only for horizontal objects. One limitation of Roggeveen's study is that only discrimination tasks were tested, while most OBA research has focused on detection. Here we directly compare results for 60 observers tested in tasks involving both target discrimination and target detection (Egly, Driver, & Rafal, 1994). In general, RTs were much shorter for the detection task. For target discrimination we found the same pattern of results as described before: OBA for horizontal objects and opposite effects for vertical ones. For target detection, OBA was more pronounced for horizontal objects, but the trend was in the same direction for vertical objects. These results underline orientation effects may exist in multiple tasks, and that OBA is generally stronger for horizontal objects. Previous research has suggested that performance in a variety of visual tasks is better on the horizontal than the vertical meridian (e.g., Carrasco Talgar, Cameron, 2001). Such attentional preferences could explain why OBA in the current study was generally more pronounced for horizontal objects. Finally, given our large sample size, we were able to use bootstrapping to estimate effect sizes for individual subjects. We found high degrees of variability among subjects across both tasks and orientations. Whereas more than 80% of observers exhibited significant space-based attention effects, fewer than 10% of the observers in each task and orientation showed significant OBA. Taken as a whole, these results suggest that OBA might not be as robust and ubiquitous as previously assumed.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.069
GPT teacher head0.326
Teacher spread0.256 · 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 designBench or experimental
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
Published2011
Admission routes1
Has abstractyes

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