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Record W4246228751 · doi:10.1167/14.10.1206

When caused by an eye movement inhibition of return's effect is post-perceptual: Evidence from SAT functions

2014· article· en· W4246228751 on OpenAlexaff
R. S. Redden, M. D. Hilchey, R. M. Klein

Bibliographic record

VenueJournal of Vision · 2014
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsDalhousie University
Fundersnot available
KeywordsInhibition of returnCued speechEye movementPerceptionPsychologyMovement (music)Cognitive psychologyNeuroscienceVisual attentionPhysicsAcoustics

Abstract

fetched live from OpenAlex

Inhibition of return (IOR) is an inhibitory aftermath of orienting typically seen in the form of slower response to targets presented in the previously attended location. IOR has been shown to exist in two mutually exclusive forms (Taylor & Klein, 2000): an effect on motoric processes (an output form) is observed when the oculomotor system is not suppressed and an effect on attentional/perceptual processes (an input form) when the oculomotor system is suppressed. Whereas Chica, Taylor, Lupianez, and Klein (2010) discovered that when caused by an eye movement to an uninformative peripheral cue, the delay in responding to targets at the originally cued location (the IOR effect) was accompanied by more accurate responding (a speed-accuracy tradeoff). It is impossible to tell from their data pattern if this evidence for a criterion shift was or was not accompanied by a genuine improvement in information processing. We investigated the trading relation between speed and accuracy when IOR was caused by an eye movement to a spatially-uninformative cue by implementing five 210 ms response windows (beginning 120ms, 240ms, 360ms, 480ms, and 600ms after the target's appearance) within which observers were required to make a non-spatial discrimination about the target. By generating speed-accuracy tradeoff functions as proposed by Wickelgren (1977) and implemented by Ivanoff and Klein (2006), we determined the output form of IOR is characterized exclusively by a criterion shift, as represented by performance at both cued and uncued locations existing on a single speed-accuracy function. Meeting abstract presented at VSS 2014

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)0.001
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.335
Teacher spread0.297 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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