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Record W2010605544 · doi:10.1126/science.1194780

Paradoxical False Memory for Objects After Brain Damage

2010· article· en· W2010605544 on OpenAlexaff
Stephanie M. McTighe, Rosemary A. Cowell, Boyer D. Winters, Timothy J. Bussey, Lisa M. Saksida

Bibliographic record

VenueScience · 2010
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsUniversity of Guelph
FundersBiotechnology and Biological Sciences Research Council
KeywordsMemory impairmentCognitive psychologyObject (grammar)Interference theoryComputer scienceCognitionCognitive impairmentVisual memoryPsychologyCognitive scienceMemory errorsNeuroscienceArtificial intelligenceWorking memory

Abstract

fetched live from OpenAlex

Novel or Familiar? Amnesia is characterized by a number of memory deficits, including the apparent inability to distinguish between novel and familiar stimuli. McTighe et al. (p. 1408 ; see the Perspective by Eichenbaum ) observed that the recognition memory of brain-damaged rats in a standard model of amnesia was impaired not because previously experienced objects seemed to be novel, but because objects not previously experienced seemed to be familiar. Furthermore, simply placing the animal in a visually deprived environment during the delay, reducing visual interference, completely rescued the impairment. This counterintuitive finding contradicts the predominant “multiple memory systems” model in which amnesia is usually considered and forces a reconsideration of fundamental assumptions underlying our understanding of amnesia.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.055
GPT teacher head0.338
Teacher spread0.283 · 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 designObservational
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

Citations148
Published2010
Admission routes1
Has abstractyes

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