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Record W1997453008 · doi:10.1167/9.4.15

Interaction of spatial and temporal factors in psychophysical estimates of surround suppression

2009· article· en· W1997453008 on OpenAlexafffund
Jan Churan, A. G. Richard, Christopher C. Pack

Bibliographic record

VenueJournal of Vision · 2009
Typearticle
Languageen
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
FundersCanadian Institutes of Health Research
KeywordsStimulus (psychology)Second-order stimulusSurround suppressionPsychologyNeuroscienceVisual perceptionPerceptionCognitive psychology

Abstract

fetched live from OpenAlex

Recent psychophysical work has shown that performance on a direction discrimination task decreases with increasing stimulus size, provided the stimulus is high in contrast. This psychophysical surround suppression has been linked to the inhibitory spatial surrounds that have been observed throughout the primate visual system. In this work we have examined a temporal factor that may also contribute to psychophysical surround suppression. Consistent with previous work, we found that psychophysical surround suppression is strongest when a high-contrast motion stimulus is presented very briefly so that the appearance of the stimulus coincided with its motion. However, when a brief delay was inserted between the stimulus onset and the onset of motion, the counterintuitive effects of stimulus size disappeared. The effect of the motion onset asynchrony (MOA) was strongest when the stationary stimulus immediately preceded the stimulus motion and when stimulus orientation during the MOA was very similar to that during the motion presentation. We conclude that psychophysical surround suppression is partially linked to the temporal structure of the stimulus, more precisely to a masking effect caused by sudden stimulus onsets (and to a smaller degree stimulus offsets).

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)0.000
metaresearch head score (Gemma)0.000
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 score0.199
Threshold uncertainty score0.184

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.049
GPT teacher head0.391
Teacher spread0.341 · 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

Citations19
Published2009
Admission routes2
Has abstractyes

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