Illusory Contours Do Not Pass through the “Blind Spot”
Bibliographic record
Abstract
Our visual percepts are not fully determined by the physical stimulus input. That is why we perceive crisp bounding contours even in the absence of luminance-defined borders in visual illusions such as the Kanizsa figure. It is important to understand which neural processes are involved in creating these artificial visual experiences because this might tell us how we perceive coherent objects in natural scenes, which are characterized by mutual overlap. We have already shown using functional magnetic resonance imaging [Maertens, M., & Pollmann, S. fMRI reveals a common neural substrate of illusory and real contours in v1 after perceptual learning. Journal of Cognitive Neuroscience, 17, 1553-1564, 2005] that neurons in the primary visual cortex (V1) respond to these stimuli. Here we provide support for the hypothesis that V1 is obligatory for the discrimination of the curvature of illusory contours. We presented illusory contours across the portion of the visual field corresponding to the physiological "blind spot." Four observers were extensively trained and asked to discriminate fine curvature differences in these illusory contours. A distinct performance drop (increased errors and response latencies) was observed when illusory contours traversed the blind spot compared to when they were presented in the "normal" contralateral visual field at the same eccentricity. We attribute this specific performance deficit to the failure to build up a representation of the illusory contour in the absence of a cortical representation of the "blind spot" within V1. The current results substantiate the assumption that neural activity in area V1 is closely related to our phenomenal experience of illusory contours in particular, and to the construction of our subjective percepts in general.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".