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Record W3097414925 · doi:10.1167/jov.20.11.443

Look where you go: Humans intuitively track heading direction changes with their eyes

2020· article· en· W3097414925 on OpenAlexaff
Hiu Mei Chow, Jonas Knöll, Matthew Madsen, Miriam Spering

Bibliographic record

VenueJournal of Vision · 2020
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHeading (navigation)Eye movementComputer visionArtificial intelligenceStimulus (psychology)Fixation (population genetics)Fixation pointComputer scienceContrast (vision)Coherence (philosophical gambling strategy)Motion perceptionPerceptionCommunicationMotion (physics)MathematicsPsychologyGeodesyGeographyCognitive psychologyStatisticsNeuroscienceMedicinePopulation

Abstract

fetched live from OpenAlex

Successful performance of daily activities such as driving a car relies on the accurate perception of self-motion, such as the direction and speed of heading. Small groups of primates—humans, macaques, marmosets—can track heading direction with their eyes in the absence of any instruction and with only minimal training (Knöll et al. PNAS 2018). Here we investigated if this tracking behavior is generalizable to a larger group of human observers and sensitive to changes in motion signal strength. Observers (n=43) viewed a cloud of moving dots that appeared to converge to one point, resulting in perceived self-motion towards or receding from the focus of expansion (FOE). FOE location shifted across time in a random walk fashion. Observers were asked to freely view the stimulus; eye position was recorded using an Eyelink 1000 eye tracker. In Exp.1 (n=19), we verified if observers could track suprathreshold stimuli (coherence: 100%; contrast: 33%; speed: 2m/s). In Exp.2 (n=24), we tested the effect of motion signal strength by manipulating coherence (6.25-100%), contrast (3.6-90%), and speed (0.75-6m/s). Results show that observers intuitively track heading direction changes using a combination of saccades, fixation, and slow drift. In both experiments, more than 80% of observers tracked the FOE with highly correlated position trajectories (cross-correlation coefficient > 0.6) in response to high signal-strength stimuli. Spatial tracking error (eye position error between eye and FOE) increased with motion signal strength decreasing from the highest to the lowest level of coherence (32% error increase), dot contrast (24% error increase), and speed (44% error increase). Intuitive ocular tracking of heading direction is generalizable to a larger group of human observers, and is finely tuned to low-level motion signals. Future work will explore whether we can utilize eye movements as an indicator of self-motion perception in clinical populations

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)0.002
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 score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0020.000

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 head0.070
GPT teacher head0.336
Teacher spread0.266 · 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
Published2020
Admission routes1
Has abstractyes

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