Looking at the Ebbinghaus illusion: differences in neurocomputational requirements, not gaze-mediated attention, explain a classic perception-action dissociation
Bibliographic record
Abstract
Perceiving and grasping an object present an animal with different sets of computational problems. The solution in primates entails the specialization of separate neural networks for visual processing with different object representations. This explains why the Ebbinghaus illusion minimally affects the grasping hand's in-flight aperture, which normally scales with target size, even though the size of the target disc remains misperceived. An attractive alternative account, however, posits that grasps are refractory to the illusion because participants fixate on the target and fail to attend to the surrounding context. To test this account, we tracked both limb and gaze while participants made forced-choice judgments of relative disc size in the Ebbinghaus illusion or did so in combination with grasping or manually estimating the size of one of the discs. We replicated the classic dissociation: grasp aperture was refractory to the measured illusory effect on perceived size, while judgments and manual estimates of disc size were not. Importantly, the number of display-wide saccades per second and the percentage of total fixation time or fixations directed at the selected disc failed to explain the dissociation. Our findings support the contention that object perception and goal-directed action rely on distinct visual representations. This article is part of a discussion meeting issue 'New approaches to 3D vision'.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".