Bibliographic record
Abstract
In my Point-of-View article, entitled “Cognitive dynamic systems,” Proceedings of the IEEE , November 2006, I included a footnote stating that a new book on this very topic was under preparation. At long last, here is the book that I promised then, over four years later. Just as adaptive filtering, going back to the pioneering work done by Professor Bernard Widrow and his research associates at Stanford University, represents one of the hallmarks of the twentieth century in signal processing and control, I see cognitive dynamic systems, exemplified by cognitive radar, cognitive control, and cognitive radio and other engineering systems, as one of the hallmarks of the twenty-first century. The key question is: How do we define cognition? In this book of mine, I look to the human brain as the framework for cognition. As such, cognition embodies four basic processes: perception–action cycle, memory, attention, and intelligence, each of which has a specific function of its own. In identifying this list of four processes. I have left out language, the fifth distinctive characteristic of human cognition, as it is outside the scope of this book. Simply put, there is no better framework than human cognition, embodying the above four processes, for the study of cognitive dynamic systems, irrespective of application.
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.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.427 | 0.265 |
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".