Bibliographic record
Abstract
We address the question of what are the computational tasks that an executive controller for visual attention must solve. This question is posed in the context of the Selective Tuning model of attention (Tsotsos 2011); however other attention models are considered as well (for example, see Baluch and Itti 2011). The range of required computations go beyond the expected top-down attention signals or region-of-interest determinations, and must deal with parameter settings, timing and synchronization of processes, information routing, a range of recurrent mechanisms, coordination of bottom-up with top-down information, matching control to task, and more. Within Selective Tuning, the attentive mechanisms of suppression, restriction and selection are defined and several sub-classes of each. There are also 'beyond feed-forward' processes including recurrent localization, early dorsal-to-ventral recurrence, priming, and cueing. Overt and covert fixations are both supported. All of this must be coordinated, communications synchronized, and the results monitored to ensure the expected results are obtained, as the given task requires. We show how these play a role in the overall shaping of attentional modulation of the visual system so that it provides its best performance: the system is a dynamic one, tuning a general purpose processor to the task and input of the moment. The framework of computation that might suffice for an attention executive will be described. Importantly, one must also consider the communications to and from the attention executive. To accomplish this, the classic, seminal contribution of Ullman's Visual Routines is resurrected, re-defined to make it consistent with the modern neurobiology of vision, re-named Cognitive Programs, and developed to include the critical elements of Visual Task Executive, Attention Executive, and Working Memory. Meeting abstract presented at VSS 2013
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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 | 0.002 |
| 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".