Living in A Material World: How Visual Cues to Material Properties Affect the Way That We Lift Objects and Perceive Their Weight
Bibliographic record
Abstract
The visual properties of an object provide many cues as to the tensile strength, compliance, and density of the material from which it is made. However, it is not well understood how these implicit associations affect our perceptions of these properties and how they determine the initial forces that are applied when an object is picked up. Here we examine the effects of these cues on such forces by using the classic "material-weight illusion" (MWI). Grip and load forces were measured in three experiments as participants lifted cubes made from metal, wood, and expanded polystyrene. These cubes were adjusted to have a different mass than would be expected for a particular material. For the initial lifts, the forces were scaled to the expected weight of each object, such that the metal block was gripped and lifted with more force than the polystyrene one. After a few lifts, however, participants scaled their forces to the actual weight of the blocks, implicitly disregarding the misleading visual cues to each block's composition (experiments 1 and 2). Despite this rapid rescaling, participants experienced a robust MWI throughout the duration of the experiments. In fact, the grip and load forces never matched the perception of weight until the differences in the visual surface properties between the blocks were removed (experiment 3). These findings are discussed in relation to recent debates about the underlying causes of weight-based illusions and the effect of top-down visual cues on perception and action.
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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 | 0.007 |
| 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 | 0.003 |
| 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".