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Record W1968229971 · doi:10.1167/10.12.18

Size scaling compensates for sensitivity loss produced by a simulated central scotoma in a shape-from-texture task

2010· article· en· W1968229971 on OpenAlexafffund
Aaron Johnson, Rick Gurnsey

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsConcordia University
FundersCanadian Institutes of Health ResearchNatural Sciences and Engineering Research Council of CanadaMedical Research Council Canada
KeywordsBlind spotStimulus (psychology)Central scotomaFixation (population genetics)MagnificationSaccadeEccentricity (behavior)MathematicsOpticsEye movementArtificial intelligenceCommunicationAudiologyPhysicsPsychologyComputer scienceCognitive psychologyMedicinePopulationSocial psychology

Abstract

fetched live from OpenAlex

Studies of eccentricity-dependent sensitivity loss typically require participants to maintain fixation while making judgments about stimuli presented at a range of sizes and eccentricities. However, training participants to fixate can prove difficult, and as stimulus size increases, they become poorly localized and may even encroach on the fovea. In the present experiment, we controlled eccentricity of stimulus presentation using a simulated central scotoma of variable size. Participants were asked to perform a 27-alternative forced-choice shape-from-texture task in the presence of a simulated scotoma, with stimulus size and scotoma radius as the independent variables. The resulting psychometric functions for each simulated scotoma were shifted versions of each other on a log size axis. Therefore, stimulus magnification was sufficient to equate sensitivity to shape from texture for all scotoma radii. Increasing scotoma radius also disrupts eye movements, producing increases in fixation frequency and duration, as well as saccade length.

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)0.001
metaresearch head score (Gemma)0.002
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.154
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.329
Teacher spread0.308 · 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

Citations6
Published2010
Admission routes2
Has abstractyes

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