Bibliographic record
Abstract
From an engineering point-of-view, cognitive control is inspired by the prefrontal cortex of the human brain; cognitive control may therefore be viewed as the overarching function of a cognitive dynamic system. In this paper, we describe a new way of thinking about cognitive control that embodies two basic components: learning and planning, both of which are based on two notions: 1) two-state model of the environment and the perceptor and 2) perception-action cycle, which is a distinctive characteristic of the cognitive dynamic system. Most importantly, it is shown that the cognitive control learning algorithm is a special form of Bellman's dynamic programming. Distinctive properties of the new algorithm include the following: 1) optimality of performance; 2) algorithmic convergence to optimal policy; and 3) linear law of complexity measured in terms of the number of actions taken by the cognitive controller on the environment. To validate these intrinsic properties of the algorithm, a computational experiment is presented, which involves a cognitive tracking radar that is known to closely mimic the visual brain. The experiment illustrates two different scenarios: 1) the impact of planning on learning curves of the new cognitive controller and 2) comparison of the learning curves of three different controllers, based on dynamic optimization, traditional \(Q\) -learning, and the new algorithm. The latter two algorithms are based on the two-state model, and they both involve the use of planning.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".