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Simulating alphabet recitation under thalamic lesions

2019· article· en· W3005956544 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

VenueExLing Conferences · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced Memory and Neural Computing
Canadian institutionsUniversity of WaterlooOntario Brain InstituteHospital for Sick Children
Fundersnot available
KeywordsBasal gangliaComputer scienceThalamusPointer (user interface)NeuroscienceNeurocognitiveWorking memoryAssociative propertyArtificial intelligenceAlphabetSpeech recognitionArtificial neural networkPsychologyCognitionMathematicsCentral nervous system

Abstract

fetched live from OpenAlex

We utilize the Semantic Pointer Architecture, a neurocognitive architecture in order to model language impairments. Constructed is a spiking neural network to investigate the effect of neural deficits in the basal ganglia and thalamus on the retrieval of an ordered sequence of unique symbols. The model includes four subnetworks: associative memory, working memory, basal ganglia and thalamus. A lesion is simulated by reducing the number of available neurons in the thalamus and attenuating its input from the basal ganglia. The model remains mostly successful in the ordered retrieval of the alphabet but ‘stutters’: working memory ‘forgets’ the current letter and ‘steps back’ several letters before continuing correctly.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.390

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 head0.034
GPT teacher head0.271
Teacher spread0.237 · 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