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Record W2062621423 · doi:10.1167/8.6.272

Dynamics of collinear facilitation: Fast yet sustained

2010· article· en· W2062621423 on OpenAlexaff
Pi‐Chun Huang, Robert F. Hess

Bibliographic record

VenueJournal of Vision · 2010
Typearticle
Languageen
FieldMedicine
TopicOcular and Laser Science Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsFacilitationFlankContrast (vision)PhysicsPsychologyNeuroscienceOpticsBiologyAnatomy

Abstract

fetched live from OpenAlex

Purpose. It is well established that the detection of a luminance-defined Gabor is improved if measured in the presence of two high contrast aligned flanking Gabors and this is termed collinear facilitation. Here the temporal properties in collinear facilitation were investigated in order to better the understanding of its underlying mechanism. Methods and Results. Collinear facilitation was measured at different onset times of the target (2 cpd, 1 octave bandwidth, 80ms presenting time) when the contrast of the flanks was modulated at 1 Hz (1 sec) and the results showed that facilitation occurred in the spatially out-of-phase condition, suggesting a long-lasting, sustained facilitatory effect. In experiment 2, the order between target and flanks in collinear facilitation was investigated by varying the ISI between target and flanks, both of which were presented for 50ms. Results were collected for 3 different target-flank distances (2, 3, 6 λ). The results showed that the amount of facilitation decreased with the time lag between target and flanks and the peak was shifted with the target-flanks distance. However, we also found the peak facilitatory effect occurred when the target preceded the flanks. The results showed that maximal facilitation occurs at or before (not after) flank presentation, suggesting fast dynamics. Conclusion. The dynamics of collinear facilitation are complex. Facilitation occurs rapidly (tens of milliseconds) lowering thresholds at and sometimes before flank presentation but its effects are sustained (hundreds of milliseconds).

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.736
Threshold uncertainty score0.171

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.011
GPT teacher head0.351
Teacher spread0.339 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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