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Record W2015122366 · doi:10.1167/8.6.612

Seeing all the obstacles in your way: The effect of visual feedback on obstacle avoidance

2010· article· en· W2015122366 on OpenAlexaff
Craig S. Chapman, D. Kirshen, Melvyn A. Goodale

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldPsychology
TopicHuman-Automation Interaction and Safety
Canadian institutionsWestern University
Fundersnot available
KeywordsVisual feedbackObstacle avoidanceObstacleComputer scienceKinematicsDorsumPsychologyVisual processingCognitive psychologyComputer visionNeuroscienceArtificial intelligencePerceptionPhysicsMedicine

Abstract

fetched live from OpenAlex

Human reaching behaviour displays sophisticated obstacle avoidance. Previous patient work has identified dorsal stream visuomotor processing as being integral to this ability (Schindler et al., 2004, Nature Neuroscience; Rice et al., 2006, Experimental Brain Research). Recently, we demonstrated that the obstacle avoidance system in normal participants is sensitive to both the position and size of obstacles (Chapman & Goodale, VSS 2007). A limitation in these previous studies was that reaches were performed without visual feedback, and were not made to a specific target (i.e. the target was a strip instead of a point). Many studies have shown that both the introduction of visual feedback and the order in which the feedback is received significantly alter performance in simple visuomotor tasks (e.g. Jakobson & Goodale, 1991, Experimental Brain Research). Thus, the present study was designed to compare obstacle avoidance when reaches were made to a discrete target with and without visual feedback (VF vs. NVF) under different orders of feedback availability. Twenty-four right-handed participants performed reaches in the presence of one, two, or no obstacles placed mid-reach. Three visual-feedback-order conditions were used: blocked (all VF trials occurred together, separate from NVF trials), alternating, and random. In addition to replicating the previous work, we showed that robust avoidance behaviour occurred when reaches were made to a specific target, and that visual feedback modulated this behaviour. Moreover, the order in which visual feedback was made available also had a significant impact; VF and NVF trials differed significantly across several kinematic measures but only in the blocked condition. Performance did not differ between VF and NVF trials in the alternating or random conditions, suggesting that motor strategies are automatically adjusted by recent experience (Song & Nakayama, 2007, Journal of Vision) and are not affected by explicit knowledge about feedback availability on an upcoming trial.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
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)0.0020.000

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 head0.019
GPT teacher head0.391
Teacher spread0.373 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2010
Admission routes1
Has abstractyes

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