MétaCan
Menu
Back to cohort
Record W2004212861 · doi:10.1167/12.3.4

The role of discrepant retinal motion during walking in the realignment of egocentric space

2012· article· en· W2004212861 on OpenAlexaff
Tracey Herlihey, Simon K. Rushton

Bibliographic record

VenueJournal of Vision · 2012
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Toronto
FundersEngineering and Physical Sciences Research Council
KeywordsMotion (physics)PerceptionSensory systemObserver (physics)FreedmanVisual spacePsychologyComputer visionMotion perceptionComputer scienceRetinalSpace (punctuation)Cognitive psychologyArtificial intelligenceNeurosciencePhysicsMedicineOphthalmologyGeography

Abstract

fetched live from OpenAlex

Visually guided action relies on accurate perception of egocentric direction. Unfortunately, perceived direction easily becomes misaligned. How is this problem overcome? One theory (R. Held & S. J. Freedman, 1963) is that during self-movement the observer uses the relationship between anticipated and experienced sensory feedback as a source of information to maintain alignment. However, data supporting this theory is equivocal, and recent evidence appears contradictory. We reexamined the issue. We injected an error into perceived visual direction and then assessed realignment after a period of walking toward a target. We manipulated the sensory information available (presence of retinal motion, Experiment 1; presence of peripheral motion, Experiment 2) and found that as the amount of retinal motion was reduced (Experiments 1 and 2), realignment of perceived visual direction decreased. When we then (Experiment 3) removed the discrepancy between anticipated and experienced retinal motion, no realignment was observed. Our results provide evidence that a discrepancy between anticipated and experienced sensory feedback is an important source of information for the alignment of egocentric space, with retinal motion having a particular role in driving a realignment of perceived visual direction.

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)0.000
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 score0.136
Threshold uncertainty score0.110

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.023
GPT teacher head0.318
Teacher spread0.295 · 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

Citations16
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of VisionSame topicVisual perception and processing mechanismsFrench-language works237,207