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Record W2073008250 · doi:10.1068/p5798

Plasticity of the Association between Visual Space and Action Space in a Blind-Walking Task

2008· article· en· W2073008250 on OpenAlexaff
Colin G. Ellard, Lori S Wagar

Bibliographic record

VenuePerception · 2008
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAssociation (psychology)Visual spaceTask (project management)Action (physics)Space (punctuation)PsychologyProprioceptionCognitive psychologyPhysical medicine and rehabilitationComputer scienceCommunicationComputer visionNeurosciencePerceptionMedicine

Abstract

fetched live from OpenAlex

Many experiments have shown that a brief visual preview provides sufficient information to complete certain kinds of movements (reaching, grasping, and walking) with high precision. This suggests that participants must possess a calibration between visual target location and the kinaesthetic, proprioceptive, and/or vestibular stimulation generated during movement towards the target. We investigated the properties of this calibration using a cue-conflict paradigm in which participants were trained with mismatched locomotor and visual input. After training, participants were presented with visual targets and were asked to either walk to them or locate them in a spatial updating task. Our results showed that the training was sufficient to produce significant, systematic miscalibrations of the association between visual space and action space. These findings suggest that the association between action space and visual space is modifiable by experience. This plasticity could be either due to modification of a simple, task-specific sensory motor association or it could reflect a change in the gain of a path integration signal or a reorganisation of the relationship between perceived space and action space. We suggest further experiments that might help to distinguish between these possibilities.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.080
GPT teacher head0.340
Teacher spread0.259 · 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

Citations4
Published2008
Admission routes1
Has abstractyes

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