Bibliographic record
Abstract
In the recent debate about the unity and integration of cognitive science (Núñez et al., 2019, 2020; see also commentaries in topiCS 11:4, introduced by Gray, 2019), one focus of the argument rested on the predominance of cognitive psychology and the displacement of smaller disciplines, such as anthropology and philosophy. Much less attention has been paid to the fact that another key player in the genesis of cognitive science has been withdrawing from the joint endeavor: artificial intelligence (AI) and computer science (Forbus, 2010; Goel, 2019). Although not framed in terms of the recent debate, the two topics in the current issue of topiCS are germane to this concern, as they both focus on cognitive modeling—arguably a signature approach of cognitive science and a natural link to AI. The call for rapprochement is clearest in the first topic, Cognition-Inspired Artificial Intelligence, edited by Daniel N. Cassenti (DEVCOM Army Research Laboratory), Vladislav D. Veksler (Caldwell University), and Frank E. Ritter (Pennsylvania State University). To showcase how cognitive science has not just benefitted from advances in AI, but can and should inspire AI development, Cassenti, Veksler, and Ritter bring together contributions from researchers actively using cognitive modeling to tackle a wide range of cognitive phenomena. Incidentally, the other topic in this issue seconds this call for greater attention to and consideration of cognitive models by presenting spearheading work in this very field. For their topic, Terrence C. Stewart (National Research Council Canada) and Joost de Jong (Maastricht University) have assembled revised and expanded versions of the five best papers presented at last year's 19th International Conference on Cognitive Modeling, a conference devoted to computational systems that are aimed at reflecting the internal processes of the mind. In their introduction, Stewart and de Jong point out how these papers, despite their diversity in content, still complement one another in terms of focus and approach: by refining and advancing computational models to better reflect empirical data, or by using such models to better explain data. Congratulations to their authors for their awards––we hope you continue the outstanding work you are doing! topiCS encourages letters and commentaries on all topics, as well as proposals for new topics. Letters are not longer than two published pages (ca. 400–1000 words). Commentaries (between 1000 and 2000 words) are often solicited by Topic Editors prior to the publication of their topic, but they may also be considered after publication. Letters and commentaries typically come without abstracts and with few references, if any. The Executive Editor and the Senior Editorial Board (SEB) are constantly searching for new and exciting topics for topiCS. Feel free to open communications with a short note to the Executive Editor ([email protected]) or a member of the SEB (for a list, see the publisher's homepage for topiCS: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1756-8765/homepage/EditorialBoard.html).
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.135 | 0.068 |
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".