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Record W1985044619 · doi:10.4018/ijcini.2014010103

Unveiling the Cognitive Mechanisms of Eyes

2014· article· en· W1985044619 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

VenueInternational Journal of Cognitive Informatics and Natural Intelligence · 2014
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCognitionMemorizationPerceptionComputer scienceConsciousnessCognitive scienceCognitive psychologyNeuropsychologyMechanism (biology)Dual (grammatical number)Eye movementInferenceFunction (biology)PsychologyArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Eyes as the unique organ possess intensively direct connections to the brain and dynamically perceptual accessibility to the mind. This paper analyzes the cognitive mechanisms of eyes not only as the sensory of vision, but also the browser of internal memory in thinking and perception. The browse function of eyes is created by abstract conditioning of the eye's tracking pathway for accessing internal memories, which enables eye movements to function as the driver of the perceptive thinking engine of the brain. The dual mechanisms of the eyes as both the external sensor of the brain and the internal browser of the mind are explained based on evidences and cognitive experiences in cognitive informatics, neuropsychology, cognitive science, and brain science. The finding on the experiment's internal browsing mechanism of eyes reveals a crucial role of eyes interacting with the brain for accessing internal memory and the cognitive knowledge base in thinking, perception, attention, consciousness, learning, memorization, and inference.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.936
Threshold uncertainty score0.364

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.013
GPT teacher head0.281
Teacher spread0.268 · 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