Can shape information be transferred from hand to eye independently of semantics?
Bibliographic record
Abstract
Is visual object identification influenced by haptic information from objects being explored simultaneously with the hands? Previous approaches to this question have either used familiar objects, thereby not ruling out the semantic route, or used unfamiliar objects, thus neglecting the question of how the results are related to everyday object recognition. Using an adaptation of a visual priming method (Biederman & Cooper, 1991), we compare identity with category priming to separate semantic influences from direct shape transfer in haptic-visual priming with familiar objects. Participants (n=16) manually explored an object (haptic prime) while viewing photos that progressively revealed common objects they were tasked to name. Familiar objects belonged to 8 semantic categories, each represented by 2 differently shaped objects. To address conceptually-mediated versus direct shape priming, we compared identity (haptic prime and visual target share a label and shape) and category priming (same label but different shapes). Results showed that accuracy was greater for identity than category priming (90% vs. 79%), and a shared semantic label led to greater accuracy than in unrelated and neutral conditions (71%). Detailed analyses indicated that the haptic objects did not bias responses independently of these priming effects: (1) with unrelated primes, responses associated with the held object occurred no more (7.8%) than expected by chance (12.5%), and (2) across all conditions in which the haptic prime was a potential target, participants responded with that label only 59% (optimal guessing was 67%). This demonstrates that shape can be transferred from hand to eye for familiar objects, independent of the semantic priming previously demonstrated in haptic-to-visual priming (Reales & Ballesteros, 1999). Moreover, this new methodological approach adds to recent research using non-familiar objects (Ernst et.al., 2007; Ostrovsky et.al., 2011), because it indexes the haptic-visual transfer of shape for familiar, ecologically valid, objects. Meeting abstract presented at VSS 2012
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".