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Record W6997339153

Visual Inputs and Motor Outputs as Indivduals Walk Through Dynamically Changing Environments

2006· dissertation· en· W6997339153 on OpenAlexaff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2006
Typedissertation
Languageen
FieldNeuroscience
TopicMotor Control and Adaptation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAction (physics)Noise (video)Work (physics)Feature (linguistics)LimitingPerception
DOInot available

Abstract

fetched live from OpenAlex

Walking around in dynamically changing environments require the integration of three of our sensory systems: visual, vestibular, and kinesethic. Vision is the only modality of these three sensory systems that provides information at a distance for proactively controlling locomotion (Gibson, 1958). The visual system provides information about self-motion, about body position and body segments relative to one another and the environment, and environmental information at a distance (Patla, 1998). Gibson (1979) developed the idea that everyday behaviour is controlled by perception-action coupling between an action and some specific information picked up from the optic flow that is generated by that action. Such that visual perception guides the action \nrequired to navigate safely through an environment and the action in turn alters perception. The objective of my thesis was to determine how well perception and action are coupled when approaching and walking through moving doors with dynamically changing apertures. My first two studies were grouped together and here I found that as the level of threat increased, the parameters of control changed and not the controlling mechanism. The two dominant action control parameters observed were a change in approach velocity and a change in posture (i.e. shoulder rotation). These findings add to previous work done in this area using a similar set-up in virtual reality, \nwhere after much practice participants increased success rate by decreasing velocity prior to crossing the doors. In my third study I found that visual fixation patterns and action parameters were similar when the location of the \naperture was predictable and when it was not. Previous work from other researchers has shown that vision and a subsequent action are tightly coupled with a latency of about 1second. I have found that vision only tightly \ncouples action when a specific action is required and the threat of a collision increases. My findings also point in the same direction as previous work that has shown that individuals look where they are going. My last study \nwas designed to determine if we go where we are looking. Here I found that action does follow vision but is only loosely correlated. The most important and common finding from all the studies is that at 2 seconds prior to crossing the moving doors (any type of movement) vision seems to have the most profound effect on action. At this time variability in action is significantly lower than at prior times. I believe that my findings will help to understand how individuals use vision to modify actions in order to avoid colliding with other people or other moving objects within the environment. And this knowledge will help elderly individuals to be better able to cope with walking \nin cluttered environments and avoid contacting other objects.

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)0.004
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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.010
GPT teacher head0.212
Teacher spread0.202 · 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

Citations0
Published2006
Admission routes1
Has abstractyes

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