Bibliographic record
Abstract
The Selective Tuning (ST) model (Tsotsos, 1995) proposed that the visual focus of attention is accompanied by a suppressive surround in spatial and feature dimensions. Follow-up studies have provided behavioural and neurophysiological evidence for this proposal (Carrasco, 2011; Tsotsos, 2011). ST also predicts that the size of the suppressive surround is determined by the level of processing within the visual hierarchy. We, thus, hypothesized that the size of the suppressive surround corresponds to the receptive field size of a neuron that best represents the attended stimulus. We conducted a free-viewing visual search task to test this hypothesis and used two different types of features processed at different levels – (early ventral) orientation and (late ventral) Greebles (Gauthier & Tarr, 1997). The sizes of search displays and stimuli were scaled depending on the feature levels to match the receptive field size of targeted neurons (V2 and LO). We tracked participants’ eye movements during free-viewing visual search to find rapid return saccades from a distractor to a target. During search, if attention falls on a distractor (D1) such that a target lies within its suppressive surround, that target is invisible until attention is released. An eye movement then reveals the target and triggers a return saccade (Sheinberg & Logothetis, 2001). In other words, shifting gaze from D1 to another distractor (D2) releases the target from the suppression and a short latency (return) saccade to target occurs. The distribution of distances between the target and D1 when return saccades occur provides a measure for the size of the suppressive surround. Searching for Greebles produced much larger suppressive surrounds than orientation. This indicates that the size of the suppressive surround reflects the processing level of the attended stimulus supporting ST’s original prediction. Meeting abstract presented at VSS 2015
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".