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Spatial Updating Depends on Gaze Direction Even after Loss of Vision

2012· article· en· W2028890621 on OpenAlexaff
Johanna Reuschel, Frank Rösler, Denise Y. P. Henriques, Katja Fiehler

Bibliographic record

VenueJournal of Neuroscience · 2012
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsYork University
FundersAlexander von Humboldt-Stiftung
KeywordsGazePsychologyProprioceptionComputer visionCognitive psychologyEye movementBlindnessArtificial intelligenceCommunicationOptometryComputer scienceNeuroscienceMedicine

Abstract

fetched live from OpenAlex

Direction of gaze (eye angle + head angle) has been shown to be important for representing space for action, implying a crucial role of vision for spatial updating. However, blind people have no access to vision yet are able to perform goal-directed actions successfully. Here, we investigated the role of visual experience for localizing and updating targets as a function of intervening gaze shifts in humans. People who differed in visual experience (late blind, congenitally blind, or sighted) were briefly presented with a proprioceptive reach target while facing it. Before they reached to the target's remembered location, they turned their head toward an eccentric direction that also induced corresponding eye movements in sighted and late blind individuals. We found that reaching errors varied systematically as a function of shift in gaze direction only in participants with early visual experience (sighted and late blind). In the late blind, this effect was solely present in people with moveable eyes but not in people with at least one glass eye. Our results suggest that the effect of gaze shifts on spatial updating develops on the basis of visual experience early in life and remains even after loss of vision as long as feedback from the eyes and head is available.

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 score0.144
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.048
GPT teacher head0.347
Teacher spread0.299 · 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

Citations23
Published2012
Admission routes1
Has abstractyes

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