MétaCan
Menu
← Back to cohort
Record W4309937181 · doi:10.4324/9781003357377-5

Relationship between déjà vu experiences and recognition-memory impairments in temporal-lobe epilepsy

2022· book-chapter· en· W4309937181 on OpenAlexafffund
Chris B. Martin, Seyed M. Mirsattari, Jens C. Pruessner, Jorge G. Burneo, Brent Hayman-Abello, Stefan Köhler

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsBaycrest HospitalLondon Health Sciences CentreUniversity of TorontoWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsDéjà vuEpilepsyTemporal lobePsychologyNeuroscienceCognitive psychologyAudiologyMedicine

Abstract

fetched live from OpenAlex

Déjà vu is characterised by feelings of familiarity and concurrent awareness that this familiarity is wrong.Previous neuropsychological research has linked déjà vu during seizures in individuals with unilateral temporal-lobe epilepsy (uTLE) to rhinal-cortex abnormalities, and to recognition-memory deଏcits that selectively aଏect familiarity assessment.Here, we examined whether bilateral TLE patients with déjà vu (bTLE) show a similar pattern of performance.Using two experimental tasks, we found that bTLE patients exhibit deଏcits not only for familiarity but also for recollection.Relative to uTLE, this broader impairment also involved hippocampal abnormalities.Our ଏndings conଏrm rhinal-cortex contributions to the generation of false familiarity in déjà vu that parallel its contributions to familiarity on recognition-memory tasks.While they do not rule out a role for recollection in identifying this familiarity as wrong, the deଏcits observed in bTLE patients weigh against the notion that any such role is necessary for déjà vu to occur.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.077
GPT teacher head0.325
Teacher spread0.248 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

Explore more

Same topicEpilepsy research and treatment→French-language works237,207→