Visual Tasks Lead to Unique Sequences of Cyclic Attentional Signals
Bibliographic record
Abstract
The enormous breadth of human visual tasks highlights the question of how the visual cortex achieves this incredible generality. Recently, models of vision and visual attention (e.g., Tsotsos 2011, Beuth & Hamker 2015) have incorporated specific solutions toward such generality resulting in the insight that different visual tasks require unique patterns of neural activations to occur with specific timings in order to provide unified response. For example, sub-tasks such as task-set specific priming of visual features, disengaging from a previous attentional focus, selection of a next focus, matching focus to task, eye movements, storing extracted information into working memory, recognition of task completion (or failure), and resetting for the next task need to be set up, initiated, monitored and terminated with precise timing and coordination. Each computation takes time, each transfer of information between representations takes time, network transmission speeds and neural distance travelled vary, motor actions take time and so on. It is a major question to determine how segregated computational operations are temporally coordinated. Appeals to attentional executive processes and (de-)centralized control operations have appeared in the literature, but the desired generality has remained elusive in concept as well as possible neurobiological realization. Here, we outline in the context of the Selective Tuning model (Tsotsos et al. 1995; Tsotsos 2011), how attentional control signals and their timing depend on the specific task being performed. We outline how tasks define sequences of computations with unique content and duration. We predict that these computational sequences give rise to periodic, oscillatory activity that is measured across visual and fronto-parietal networks implementing attentional control of vision. We show how the computational constraints underlying visual tasks suggest a temporally precise unfolding of neural activation that is likely evident in brief periods of oscillatory activity measured across visual and attention networks of the brain. 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".