HOW MANIPULABILITY (GRASPABILITY AND FUNCTIONAL USAGE) INFLUENCES OBJECT IDENTIFICATION
Bibliographic record
Abstract
In our environment we do two things with objects: identify them, and act on them. Perhaps not coincidentally, research has shown that the brain appears to have two distinct visual streams, one that is engaged during the identification of objects, and one that is associated with action. Although these visual streams are distinct, there has been increasing interest in how the action and identification systems interact during grasping and identification tasks. In particular, the current research explored the role that previous motor experience with familiar manipulable objects might have on the time it takes healthy participants to identify these objects (relative to non-manipulable objects). Furthermore, previous research has shown that there are multiple, computationally and neuro-anatomically different, action systems. The current research was particularly interested in the action systems involved in 1) grasping, and 2) functionally using an object. Work began by developed a new stimulus set of black & white photographs of manipulable and non-manipulable objects, and collecting ‘graspability’ and ‘functional usage’ ratings (chapter 2). This stimulus set was then used to show that high manipulability was related to faster naming but slower categorization (chapter 3). In chapter 4, the nature of these effects was explored by extending a computational model by Yoon, Heinke and Humphreys (2002). Results from chapter 5 indicated independent roles of graspability and functional usage during tasks that required identification of objects presented either with or without a concurrent mask. Specifically, graspaility effects were larger for items that were not masked; and functional use effects were larger for items that were masked. Finally, chapter 6 indicated that action effects during identification tasks are partly based on how realistic the depictions of the objects are. That is, results from chapter 6 indicated the manipulability effects are larger for photographs than they are for line-drawings of the same objects. These results have direct implications for the design of future identification tasks, but, more broadly, they speak to the interactive nature of the human mind: Action representations can be invoked and measured during simple identification tasks, even where acting on the object is not required.
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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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".