The Cognitive Process and Formal Models of Human Attentions
Bibliographic record
Abstract
Attention is a complex mental function of humans in order to capture and serve the basic senses of vision, hearing, touch, smell, and taste, as well as internal motivations and perceptions. This paper presents a formal model and a cognitive process for rigorously explaining human attentions. Cognitive foundations of attentions and their relationships with consciousness and other perception processes are explored. The closed loop of attentions is identified that encompasses event capture and behavior reaction. Events for attention are classified into the categories of external stimuli and internal motivations. Behaviors as corresponding responses of attentions encompass recurrent, temporary, and reflex actions. Mathematical models of attentions are created as a foundation for rigorously describing the cognitive process of attentions in denotational mathematics. A wide range of applications of the unified attention model are identified in cognitive informatics, cognitive computing, and computational intelligence toward the mimic and simulation of human attention and perception in cognitive computers, cognitive robotics, and cognitive systems.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".