Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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 it