Towards the Integration of Perceptual Organization and Visual Attention: The Inferential Attentional Allocation Model. Technical Report 2001-08
Bibliographic record
Abstract
Object-based models of visual attention purport to explain why it is easier to process \ninformation within one object or perceptual group than across two or more groups. Perceptual \ngroups are generally defined in terms of Gestalt grouping principles. These models of attention \nhave been used to explain the phenomenon of cognitive tunneling within Heads-Up Displays \n(HUDs), on the assumption that the symbology of a Heads-Up Display (HUD) in a cockpit forms \na single perceptual group and the outside scene forms another. \nDespite extensive empirical support, object-based models have various shortcomings. In \nparticular, the use of Gestalt grouping principles to define the notion of objects does not allow \nfor an operational measure of what an object is to the visual system. Also, the Gestalt principles \ndo not allow for a systematic distinction between spatial and object-based mechanisms of \nattention. Finally, it is generally assumed that Gestalt grouping occurs preattentively, whereas \nthere is evidence that perceptual grouping requires attentional resources. \nThe proposed line of research aims to develop an account of object-based attention that \ndoes not rely on these premises. Rather, it is assumed that the cost of dividing attention between \nobjects reflects the cost of perceptual organization itself. A qualitative model based on this \nassumption, called the “Inferential Attentional Allocation Model,” is given. A number of \nexperiments are proposed to test key aspects of the model, in particular the effects of motion and \ntop-down knowledge on perceptual organization and attention. It is expected that the results will \nfacilitate the development of a quantitative model of object-based attention, based on a \ncomputational characterization of perceptual organization as inference to the best explanation. \nFinally, the implications of this research for HUDs with dynamic elements are discussed.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".