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Record W4254103712 · doi:10.24908/iqurcp.8721

Response Conflict and Inhibition of Return

2016· article· en· W4254103712 on OpenAlexvenueno aff
Yena Bi

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2016
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsInhibition of returnCued speechStimulus onset asynchronyPsychologyStimulus (psychology)Cognitive psychologyResponse inhibitionVisual attentionPerceptionAudiologyCognitionNeuroscience

Abstract

fetched live from OpenAlex

Much research has suggested that attention is biased away from previously attended locations-- a phenomenon termed inhibition of return (IOR). Traditionally, IOR studies use simple visual stimuli in detection tasks and employ a cue-target paradigm where a task-irrelevant cue is briefly presented followed by a target at either a cued location (same location as cue) or at an uncued location. Participants provide no response to the cue, but then produce a key press response upon target detection. When the stimulus onset asynchrony (SOA) is less than 300 ms, response to the target is facilitated by the cue; when the SOA is greater than 300 ms, response to the target is slowed at the cued location. The current study investigates different cue-target tasks and their effect on inhibition of return (IOR). We will conduct a between-subjects experiment with three conditions differing in response instruction. Target-only condition replicates the classic IOR study using a cue-target, detection task paradigm in which participants respond to the target but not the cue. Same-response condition requires participants to make identical responses to the cue and target. Different-response condition requires participants to provide a response to both the cue and the target, but the responses for the cue and target will differ. Together these studies help us understand the extent that IOR is caused by a motor response conflict as we compare the magnitude of IOR from the three testing conditions.

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)0.011
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.331
GPT teacher head0.446
Teacher spread0.114 · 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
Published2016
Admission routes1
Has abstractyes

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