The Computational Evolution of Cognitive Architectures
Bibliographic record
Abstract
Abstract What is human mind, and how does it work? These questions have occupied humanity since antiquity, but only a hundred years ago became a topic of rigorous scientific investigation. Cognitive architectures are a part of the long-standing effort to understand how human mind and brain operate by designing and implementing computational models in software and hardware. The field of cognitive architectures emerged at the intersection artificial intelligence and cognitive science and in less than 50 years of its existence led to creation of hundreds of projects, many of which are still being developed. In this book, we trace the evolution of cognitive architectures, their abilities, and future prospects. To do so, we analyzed over 3000 publications on 80+ cognitive architectures and hundreds more surveys, research papers, and opinion pieces spanning disciplines from philosophy and cognitive science to computer science and robotics. We aggregate our findings into broad themes, such as common components of the architectures, their organization, interaction, and relation to human cognitive abilities. Besides theory, we discuss what cognitive architectures can actually do and how to evaluate their performance. Finally, in view of recent developments in AI, we consider the future of cognitive architectures and challenges they face. We hope that this book will be useful not only to researchers who develop cognitive architectures or are otherwise involved in the field, but also to anyone interested in learning about progress towards understanding and modeling human cognition.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".