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Intact Learning of New Relations in Amnesia as Achieved through Unitization

2013· article· en· W1972097166 on OpenAlexafffund
Jennifer D. Ryan, S.N. Moses, Morgan D. Barense, R. Shayna Rosenbaum

Bibliographic record

VenueJournal of Neuroscience · 2013
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsHospital for Sick ChildrenYork UniversityUniversity of TorontoBaycrest Hospital
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMinistero dello Sviluppo EconomicoCanada Research ChairsOntario Ministry of Economic Development and InnovationAlfred P. Sloan Foundation
KeywordsAmnesiaTask (project management)Representation (politics)Cognitive psychologyPsychologySemantic memoryHippocampal formationRecallNeuroscienceComputer scienceArtificial intelligenceCognition

Abstract

fetched live from OpenAlex

Hippocampal amnesia is defined by deficits in the binding of relations among items--a deficit captured by the transverse patterning (TP) task. Unitization is a processing mechanism that may allow amnesic patients to compensate for relational memory deficits. Amnesic patient D.A. demonstrated intact TP, and performance was maintained 1 month following training. Successful acquisition of relations occurred only when D.A. fused or integrated objects into a unified representation. D.A. did not acquire relations when he did not generate such integrated scenarios, and acquisition of relations was slowed when integration had to occur for novel stimuli. Amnesic patients K.C. and R.F.R. were tested to provide comparative data; K.C. and R.F.R. did not benefit from unitization, perhaps due to additional cortical damage. We propose that unitization requires visual imagery of multiple items that are fused/integrated; through the benefit of extended on-line maintenance, this fused representation is anchored to existing representations in semantic memory.

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 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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.113
GPT teacher head0.335
Teacher spread0.222 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations58
Published2013
Admission routes2
Has abstractyes

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