How you use it matters: Object Function Guides Attention during Visual Search in Scenes
Bibliographic record
Abstract
How do we know where to look for objects in scenes? While it is true that we see objects within a larger context daily, it is also true that we interact with and use objects for specific purposes (object function). Many researchers believe that visual processing is not an end in itself, but is in the service of some larger goal, like the performance of an action (Gibson, 1979). In addition, previous research has shown that the action performed with an object can affect visual perception (e.g., Grèzes & Decety, 2002; Triesch et al., 2003). Here, we examined whether object function can affect attentional guidance during search in scenes. In Experiment 1, participants studied either the function (Function Group) or features (Feature Group) of a set of invented objects. In a subsequent search, studied objects were located faster than novel objects for the Function, but not the Feature group. In Experiment 2 invented objects were positioned in either function-congruent or function-incongruent locations. Search for studied objects was faster for function-congruent and impaired for function-incongruent locations relative to novel objects. These findings demonstrate that knowledge of object function can guide attention in scenes. We discuss implications for theories of visual cognition, cognitive neuroscience, as well as developmental and ecological psychology. Meeting abstract presented at VSS 2016
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".