Lifting without Seeing: The Role of Vision in Perceiving and Acting upon the Size Weight Illusion
Bibliographic record
Abstract
BACKGROUND: Our expectations of an object's heaviness not only drive our fingertip forces, but also our perception of heaviness. This effect is highlighted by the classic size-weight illusion (SWI), where different-sized objects of identical mass feel different weights. Here, we examined whether these expectations are sufficient to induce the SWI in a single wooden cube when lifted without visual feedback, by varying the size of the object seen prior to the lift. METHODOLOGY/PRINCIPAL FINDINGS: Participants, who believed that they were lifting the same object that they had just seen, reported that the weight of the single, standard-sized cube that they lifted on every trial varied as a function of the size of object they had just seen. Seeing the small object before the lift made the cube feel heavier than it did after seeing the large object. These expectations also affected the fingertip forces that were used to lift the object when vision was not permitted. The expectation-driven errors made in early trials were not corrected with repeated lifting, and participants failed to adapt their grip and load forces from the expected weight to the object's actual mass in the same way that they could when lifting with vision. CONCLUSIONS/SIGNIFICANCE: Vision appears to be crucial for the detection, and subsequent correction, of the ostensibly non-visual grip and load force errors that are a common feature of this type of object interaction. Expectations of heaviness are not only powerful enough to alter the perception of a single object's weight, but also continually drive the forces we use to lift the object when vision is unavailable.
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 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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".