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Record W2017267761 · doi:10.4018/jcini.2011100103

Cognitive Dynamic Systems

2011· article· en· W2017267761 on OpenAlexaff
S. Haykin

Bibliographic record

VenueInternational Journal of Cognitive Informatics and Natural Intelligence · 2011
Typearticle
Languageen
FieldComputer Science
TopicTarget Tracking and Data Fusion in Sensor Networks
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer scienceCognitionCognitive scienceCognitive roboticsScope (computer science)Cognitive modelLIDAPerceptionCognitive psychologyArtificial intelligenceEmbodied cognitionPsychology

Abstract

fetched live from OpenAlex

The main topics covered in this paper address the following four issues: 1) Distinction between how adaptation and cognition are viewed with respect to each other, 2) With human cognition viewed as the framework for cognition, the following cognitive processes are identified: the perception-action cycle, memory, attention, intelligence, and language. With language being outside the scope of the paper, detailed accounts of the other four cognitive processes are discussed, 3) Cognitive radar is singled out as an example application of cognitive dynamic systems that “mimics” the visual brain; experimental results on tracking are presented using simulations, which clearly demonstrate the information-processing power of cognition, and 4) Two other example applications of cognitive dynamic systems, namely, cognitive radio and cognitive control, are briefly described.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.026
GPT teacher head0.288
Teacher spread0.262 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2011
Admission routes1
Has abstractyes

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