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Record W1965085120 · doi:10.1167/3.9.532

Discriminating the direction of randomly positioned contrast-defined motion

2010· article· en· W1965085120 on OpenAlexaff
Harriet A. Allen, Robert F. Hess, Timothy Ledgeway

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill University
Fundersnot available
KeywordsLuminanceContrast (vision)OpticsPhysicsMotion perceptionPsychophysicsObserver (physics)Computer visionArtificial intelligenceMathematicsMotion (physics)Computer sciencePsychology

Abstract

fetched live from OpenAlex

We investigated whether positional uncertainty affected observe' ability to discriminate the direction of luminance-defined and contrast-defined motion. If the mechanisms that detect contrast-defined motion can't be simultaneously monitored, not knowing the position of contrast-defined motion will severely affect performance. Random dot kinematograms were presented on a circular field (radius10 deg) of low contrast 2D binary noise. Dots were either brighter (luminance-defined) or higher contrast (contrast-defined) than the noise and moved at 3 deg/sec. In a circular target area (radius 1deg) the dots moved either up or down. The remaining dots, surrounding the target area, moved randomly. The target area was centered 2 deg from fixation and there were no dot-density cues to its location. Observers discriminated the direction of motion in the target area (2AFC method) when they knew its position and when it was randomly in 1 of 4 positions. Experiment 1 measured the modulation depth (contrast-defined patterns) or contrast (luminance-defined patterns) required to discriminate motion direction. Experiment 2 measured the number of coherently moving dots required to perform the same task. Both experiments were carried out with stimulus durations of 250ms and 100ms Thresholds for the motion in randomly positioned areas ranged from 1.1 to 3.4 times the thresholds for the motion in the known position. In experiment 1 the increase in threshold was slightly larger at the shorter duration. For each condition and observer the size of the effect was almost identical for luminance-defined and contrast-defined motion. Mechanisms for contrast-defined motion are not differentially affected in their ability to process motion signals of uncertain position compared with those for luminance-defined motion. Previous findings showing poor performance with multiple patches of contrast-defined motion must reflect some other deficiency in the mechanisms for contrast-defined motion.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.331
Teacher spread0.302 · 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 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

Citations1
Published2010
Admission routes1
Has abstractyes

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