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Why are antisaccades slower than prosaccades? A novel finding using a new paradigm

2003· article· en· W2048173575 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNeuroreport · 2003
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSaccadeEye movementSaccadic eye movementAntisaccade taskPsychologyLatency (audio)NeuroscienceComputer science

Abstract

fetched live from OpenAlex

Eye movements away from a new object (antisaccades) are slower than towards it (prosaccades). This finding is assumed to reflect the fact that prosaccades to new objects are made reflexively, and that for antisaccades, reflexive eye movements have to be inhibited and antisaccades are generated volitionally. Experiment 1 investigated the relative contribution of saccade inhibition by comparing the latency difference between pro- and antisaccades obtained in the traditional blocked paradigm and in a new paradigm in which oculomotor inhibition across pro- and antisaccades was matched. When inhibition was placed on the oculomotor system, the latency difference between pro- and antisaccades was significantly reduced. Experiment 2 examined the contribution of volitional saccade programming and execution by requiring both pro- and antisaccades to be programmed volitionally. This manipulation did not decrease further the difference between pro- and antisaccades. It is thus concluded that oculomotor inhibition is the main factor leading to long antisaccade latency. The remaining difference is attributed to the reallocation of covert attention from the target location towards the opposite antisaccade location.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.075
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.183
GPT teacher head0.361
Teacher spread0.178 · 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