Are you a visual ‘shader’ or a ‘bolder’?: Different visual routines create everyday hallucinations in ‘scaffolded attention’
Bibliographic record
Abstract
Visual processing of incoming sensory cues gives rise to the rich colours that fill contours and the contours that form objects. But people can also experience colours and contours in the *absence* of explicit sensory cues, albeit in more fleeting ways — as in the phenomenon of “scaffolded attention”. Consider a regular grid of squares. By definition, there is no structure there, but many people report seeing various shapes and patterns anyway (e.g., horizontal lines, block letters). But beyond *what* people see, perhaps more intriguing is *how* they experience it. Some describe the squares of perceived patterns to be brighter or differently shaded (i.e., “shaders”), while others note the squares as being ‘outlined’ or ‘traced’ (i.e., “bolders”). What determines when people experience one ‘type’ over another? Here observers reported which type they experienced, and reproduced the magnitude of bolding and/or shading through an interactive grid. We then explored the influence of *external* grid features (e.g., white squares with black outlines vs. black squares with white outlines), and *internal* factors (e.g., attentional breadth [via the ‘Functional Field of View task; FFOV], and sensitivity to figure-ground boundaries [via the Leuven Embedded Figures Test; LEFT]). First, the proportions of shaders and bolders overwhelmingly differed across grids, with reliably more bolders for black (93.2%) than for white grids (41.1%) — perhaps because the contrast differences change whether the squares or the lines are seen as figure or ground. Second, only the LEFT (and not FFOV) scores predicted whether people would be a ‘shader’ or ‘bolder,’ highlighting the role of segmentation processes in scaffolded attention. Thus, people’s everyday hallucinations can depend on what the mind selects — the squares on the white grids or the lines of black grids — which may be grouped together through different visual routines.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".