As the nose on your face: face-superiority context effect in a simple line orientation detection task
Bibliographic record
Abstract
Visual processing is classically modeled as a hierarchy of feedforward stages of increasing complexity (Hubel & Wiesel, 1962; Riesenhuber & Poggio, 1999). However, processing of complex stimuli may be performed initially at a coarse level in high-level visual areas, with reentrant processing to low-level areas to refine object representations (Mumford, 1992; Hochstein & Ahissar, 2002). Whether this is also true for a simple element, such as a single orientated bar, is not clear: it may be that it is processed before the detection of complex configurations, without any re-entrant processing. We tested a strong form of the re-entrant hypothesis (Gorea & Julesz, 1991), that even the detection of a line-element stimulus in a typical visual search task would be influenced by its insertion into a complex configuration. We measured the detection speed for vertical target elements embedded within arrays of 22.5° oriented noise elements, presented for a maximum of 3000ms. The vertical target element was clustered with three horizontal lines to form one of four patterns: an upright or inverted schematic face, or a symmetric or asymmetric non-face pattern. In other conditions the vertical line appeared outside the cluster. Finally, the vertical line could appear alone or with three randomly distributed horizontal lines (Fig1a). The target was detected faster when clustered with horizontal lines than when presented alone (p<0.01), outside the cluster (p<0.0001), or amongst random horizontal lines (p<0.0001). Most importantly, it was detected faster in the face-like pattern than in the asymmetric (p<0.0001), symmetric (p<0.0001), and inverted face-like pattern (p<0.05) (Fig1b). This novel face-superiority context effect points to a simultaneous activation of lower and higher visual processes. The results provide evidence for a non-hierarchical organisation in the visual system, suggesting that even simple perceptual decisions are reached through a recursive exchange of information between low and high processing levels. Meeting abstract presented at VSS 2013
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".