MétaCan
Menu
Back to cohort

Motion Parallax Is Computed in the Updating of Human Spatial Memory

2003· article· en· W1936005615 on OpenAlex
W. Pieter Medendorp, Douglas Tweed, J. Douglas Crawford

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

VenueJournal of Neuroscience · 2003
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork UniversityUniversity of TorontoCanadian Institutes of Health Research
Fundersnot available
KeywordsParallaxGazeComputer visionMotion (physics)Artificial intelligenceComputer scienceSpace (punctuation)Visual spaceFlash (photography)Computer graphics (images)PhysicsOpticsPsychologyNeurosciencePerception

Abstract

fetched live from OpenAlex

As we move through space, stationary objects around us show motion parallax: their directions relative to us change at different rates, depending on their distance. Does the brain incorporate parallax when it updates its stored representations of space? We had subjects fixate a distant target and then we flashed lights, at different distances, onto the retinal periphery. Subjects translated sideways while keeping their gaze on the distant target, and then they looked to the remembered location of the flash. Their responses corrected almost perfectly for parallax: they turned their eyes farther for nearer targets, in the predicted nonlinear patterns. Computer simulations suggest a neural mechanism in which feedback about self-motion updates remembered locations of objects within an internal map of three-dimensional 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.

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 score0.053
Threshold uncertainty score0.227

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 science0.0000.000
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 head0.104
GPT teacher head0.355
Teacher spread0.251 · 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