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Record W2145528844 · doi:10.1109/coginf.2003.1225947

On information and knowledge representation in the brain

2004· article· en· W2145528844 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceRepresentation (politics)CognitionKnowledge representation and reasoningHuman brainObject (grammar)Cognitive modelArtificial intelligenceRelation (database)InformaticsCognitive scienceData miningPsychologyNeuroscience

Abstract

fetched live from OpenAlex

The cognitive models of information representation and the capacity of human memory are fundamental research areas in cognitive informatics, which help to reveal the mechanism and potential of the brain. This paper develops the object-attribute-relation (OAR) model for describing information representation and storage in the brain. According to the OAR model, the human memory and knowledge are represented by relations, i.e. connections of synapses between neurons, rather than by the neurons themselves as the traditional container metaphor described. Based on the OAR model, the memory capacity of the human brain is calculated as in the order of 10/sup 8432/ bits. The determination of the magnitude of human memory capacity is not only theoretically significant in cognitive informatics, but also practically useful to estimate the human potential, as well as the gap between the natural and machine intelligence.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.815
Threshold uncertainty score0.089

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.281
Teacher spread0.263 · 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

Quick stats

Citations8
Published2004
Admission routes2
Has abstractyes

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