A perception-action dissociation revealed through the interaction with blurred stimuli
Bibliographic record
Abstract
In the perception-action model of vision (Milner & Goodale, 1995), information is passed from lower visual (e.g. V1) areas into two processing streams: a dorsal stream mediating visually guided movement, and a ventral stream mediating object identification and awareness. Information processed prior to the bifurcation should therefore be available to both streams. This experiment examined how the quality of a luminance edge (processed at V1) would influence perception and action. A grasping task, and a perceptual size-matching task were performed under full-vision (FV) and 2-s delayed vision (DV). Stimuli varied in size (3), and edge-blur (4). Edges were blurred using a 2D Gaussian-filter. Maximum-grip-aperture (MGA) was the size-estimate for grasping, while perceptual-estimates (PE) required participants to estimate the size of stimuli by adjusting the size of a comparison stimulus. It was predicted that, the visual streams would use the same edge information, and thus motor and perceptual judgements would behave in a similar manner under both FV and DV. PE and MGA increased with increases of stimulus size, and decreased with increasing degree of blur. An analysis of the rate of scaling to size across conditions revealed that PES scaled at a higher rate to changes in size than MGA. Scaling based upon level of blur exhibited a two-way interaction between task and visual condition. MGA blur-scaling was not altered by DV, whereas PES blur-scaling became significantly more shallow. This violated the expectation that under DV, an increase in similarity between motor and perceptual responses would be found. Since PES became less influenced by edge blur following a delay while MGA scaling remained unchanged, there is evidence that the perception/action systems hold different representations of certain stimulus properties. Thus, the two-visual streams appear to generate their own representations of edge location even though similar edge information is available to both.
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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 | 0.000 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".