Modeling of top-down object-based attention using probabilistic neural network
Bibliographic record
Abstract
Object-based attention theory posits that attention is directed towards one object at a time. This paper attempts to simulate top-down influences. Five components of top-down influences are modeled: structure of object representation for long-term memory (LTM), learning of object representations, deduction of task-relevant features, estimation of top-down biases, mediation between bottom-up and top-down fashions, and perceptual completion. This model builds a dual-coding object representation for LTM. It consists of local and global codings, characterizing internal properties and global attributes of an object. Probabilistic neural networks (PNNs) are used for object representation in that they can model probabilistic distribution of an object through combination of confident instances. A dynamically constructive learning algorithm is developed to train PNNs when an object is attended. Given a task-specific object, this proposed model recalls the corresponding object representation from PNNs, deduces the task-relevant feature dimensions and evaluates top-down biases. Bottom-up and top-down biases are mediated to yield a primitive grouping based saliency map. The most salient primitive grouping is finally put into the perceptual completion processing module to yield an accurate and complete object representation for attention. This model has been applied into the robotic task: detection of task-specific multi-part objects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".