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A Review of Cognitive Dynamic Systems and Its Overarching Functions

2022· review· en· W4283218279 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venue2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) · 2022
Typereview
Languageen
FieldEngineering
TopicArtificial Immune Systems Applications
Canadian institutionsAlberta Oil Sands Technology and Research AuthorityOntario Neurotrauma FoundationMcMaster University
Fundersnot available
KeywordsCognitionCognitive scienceComputer scienceLIDACognitive systemsCognitive roboticsPerceptionField (mathematics)Action (physics)Control (management)Cognitive neuroscienceCognitive architectureHuman–computer interactionArtificial intelligencePsychologyEmbodied cognitionNeuroscienceMathematics

Abstract

fetched live from OpenAlex

Cognitive dynamic systems are a new field of physical systems inspired by several areas of study such as neuroscience, cognitive science, computer science, mathematics, physics and engineering. Building on Fuster’s paradigm, a system is considered cognitive when it is capable of five fundamental processes to human cognition: the perception-action cycle, memory, attention, intelligence and language. With these capabilities, a cognitive dynamic system can sense its environment, interact with it, and learn from it through continued interactions. The goal of this paper is to provide a thorough review of the cognitive dynamic system framework, along with its theory, applications, and its two special functions: cognitive control and cognitive risk control.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.923
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.321
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it