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Record W2080510805 · doi:10.1080/13548500601164404

Remembering without a past: Individuals with anterograde memory impairment talk about their lives

2007· article· en· W2080510805 on OpenAlexafffund
Maria I. Medved

Bibliographic record

VenuePsychology Health & Medicine · 2007
Typearticle
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversity of Manitoba
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsAutobiographical memoryNarrativePsychologyStorytellingAnterograde amnesiaCognitive psychologyAmnesiaEyewitness memoryForgettingCompensation (psychology)Memory errorsLinguisticsSocial psychologyRecall

Abstract

fetched live from OpenAlex

This paper describes the linguistic resources people with anterograde amnesia draw on in conversational narratives. Because of their problems in recollecting post-morbid memories, it is particularly challenging for such individuals to refer to personal experiences. Seven patients with anterograde memory impairments due to neurotrauma were interviewed one year post-event. Among other topics, they were asked to talk about their new lives and selves, which was expected to be a precarious affair given that they did not have many or any autobiographical memories. Microanalyses of their narratives identified three readily available linguistic resources that participants used to facilitate their storytelling. These were categorized as "memory importation" (transplanting a past memory into the present), "memory appropriation" (taking another's memory as one's own), and "memory compensation" (searching for memories). It is argued that although these resources were not always efficiently used by participants and their use often violated conversational expectations, these linguistic techniques provided a helpful means to sustain the production of personal narratives, even in the absence of autobiographical 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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.241
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.403
Teacher spread0.368 · 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.

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

Citations9
Published2007
Admission routes2
Has abstractyes

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