Revealing and suppressing the visual information for recognition
Bibliographic record
Abstract
A generic problem in vision is to know which information drives the perception of a stimulus. We address this problem in a case study that involves the perceptual reversal of an ambiguous image (here, Dali's painting the Slave Market with the Disappearing Bust of Voltaire, 1940). Ambiguous images, such as Dali's painting, are perfect stimuli for investigating the information in a stimulus that underlies its perception because the bottom-up information underlying the different interpretations is identical. In Experiment 1, we use Bubbles (Gosselin & Schyns, 2001) to disambiguate the image and to determine the specific visual information that drives each possible perception (here, the nuns vs. the bust of Voltaire). We found that the nature of this information is grounded in different spatial filters analyzing the image. Experiments 2 and 3 validate that this information does determine the selective perception of the ambiguous image. Using dynamic colored noise (computed by randomising the phase angles of the information driving each percept) observers adapted the spatial frequency channels mediating one of the two percepts, globally across the visual field in experiment 2 and locally in experiment 3. In a transfer phase, following global adaptation, we induce a perception of the ambiguous image that is orthogonal to the adapting frequency in experiment 2. Experiment 3 tests the locality of this spatial frequency information by confining it to the region underlying each percept, again observers experience a perception opposite to the adapting frequency. Together, the results of this local frequency-specific adaptation on the perception or recognition of complex figurative patterns suggests a new method to investigate the links between recognition and perception. This highlights the importance of understanding the information contained in a stimulus and how the use of this information modifies perception.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".