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Total recall: Detecting autobiographical memory retrieval in the absence of behaviour

2025· article· en· W4408568551 on OpenAlexafffund
Matthew Kolisnyk, Geoffrey Laforge, Marie-Ève Gagnon, Jonathan Erez, Adrian M. Owen

Bibliographic record

VenueNeuropsychologia · 2025
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversité du Québec à Trois-RivièresWestern University
FundersCanadian Institutes of Health ResearchCanada Excellence Research Chairs, Government of Canada
KeywordsAutobiographical memoryPsychologyRecallCognitive psychologyReconstructive memoryEpisodic memoryChildhood memoryCognitionNeuroscience

Abstract

fetched live from OpenAlex

Functional neuroimaging has fundamentally changed our understanding of disorders of consciousness (DoC). While many DoC patients exhibit minimal to no behavioural responsiveness, a significant minority show neural evidence of awareness and preserved cognitive functioning. Although several cognitive functions have been explored in DoC patients, autobiographical memory -- the ability to form and retrieve personal memories -- has yet to be investigated. To address this gap, we used functional magnetic resonance imaging (fMRI) to investigate autobiographical memory in one DoC patient. The patient viewed video clips across three conditions: (1) Own - clips recorded from their perspective during a recent mall visit; (2) Other - clips from a healthy control's visit to the same mall; and (3) Bookstore - novel clips from an entirely different store that had not been visited. We trained a linear support vector classifier to associate fMRI activity in canonical autobiographical memory regions with each condition using data from twelve healthy participants. We then applied the trained model to the patient's data to 'decode' which condition their fMRI activity predicted. The model accurately distinguished between Own, Other, and Bookstore conditions in the patient (Balanced Accuracy = 0.448, p = .032), with performance within the control group range (p = .068). Similarly, the model distinguished between the Own and Other conditions above chance (Balanced Accuracy = 0.609, p = .032) and within the control group's distribution (p = .620), suggesting that the patient was still able to differentiate personal experiences from visually similar scenes, despite being behaviourally unable to report that this was the case. These findings provide preliminary evidence that autobiographical memory processes, critical to conscious awareness and identity, remain intact in some DoC patients, shedding further light on their covert capabilities and inner experiences.

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)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.322
Teacher spread0.286 · 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

Citations3
Published2025
Admission routes2
Has abstractyes

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