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The Geometry of Perisaccadic Visual Perception

2009· article· en· W1978739951 on OpenAlexafffund
Alby Richard, Jan Churan, Daniel Guitton, Christopher C. Pack

Bibliographic record

VenueJournal of Neuroscience · 2009
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsSaccadeSaccadic maskingVisual spaceGazeEye movementPerceptionSaccadic suppression of image displacementVisual perceptionComputer visionPsychologyPsychophysicsStimulus (psychology)Artificial intelligenceComputer scienceCommunicationNeuroscienceCognitive psychology

Abstract

fetched live from OpenAlex

Our ability to explore our surroundings requires a combination of high-resolution vision and frequent rotations of the visual axis toward objects of interest. Such gaze shifts are themselves a source of powerful retinal stimulation, and so the visual system appears to have evolved mechanisms to maintain perceptual stability during movements of the eyes in space. The mechanisms underlying this perceptual stability can be probed in the laboratory by briefly presenting a stimulus around the time of a saccadic eye movement and asking subjects to report its position. Under such conditions, there is a systematic misperception of the probes toward the saccade end point. This perisaccadic compression of visual space has been the subject of much research, but few studies have attempted to relate it to specific brain mechanisms. Here, we show that the magnitude of perceptual compression for a wide variety of probe stimuli and saccade amplitudes is quantitatively predicted by a simple heuristic model based on the geometry of retinotopic representations in the primate brain. Specifically, we propose that perisaccadic compression is determined by the distance between the probe and saccade end point on a map that has a logarithmic representation of visual space, similar to those found in numerous cortical and subcortical visual structures. Under this assumption, the psychophysical data on perisaccadic compression can be appreciated intuitively by imagining that, around the time of a saccade, the brain confounds nearby oculomotor and sensory signals while attempting to localize the position of objects in visual space.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.223
Threshold uncertainty score0.295

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.053
GPT teacher head0.356
Teacher spread0.303 · 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 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

Citations28
Published2009
Admission routes2
Has abstractyes

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