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Record W3160117489 · doi:10.31234/osf.io/pztv4

The relationship between eye movements and autobiographical recollection is mediated by individual differences in autobiographical capacity

2019· preprint· en· W3160117489 on OpenAlexaff
Michael J. Armson, Nicholas B. Diamond, Laryssa Levesque, Jennifer D. Ryan, Brian Levine

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsBaycrest HospitalUniversity of Toronto
Fundersnot available
KeywordsRecallAutobiographical memoryPsychologyEpisodic memoryCognitive psychologyFree recallPerspective (graphical)Eye movementCognitionComputer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

The precise role of visual mechanisms in recollection of personal past events is unknown. The present study addresses this question from the oculomotor perspective. Participants freely recalled past episodes while viewing a blank screen under free and fixed viewing conditions. Memory performance was quantified with the Autobiographical Interview, which separates internal (episodic) and external (non-episodic) details. In Study 1, fixation rate was predictive of the number of internal (but not external) details recalled across both free and fixed viewing. In Study 2, using an experimenter-controlled staged event, we again observed the effect of fixations on free recall of internal (but not external) details, but this was modulated by individual differences in AM, such that the coupling between fixations and internal details was greater for those endorsing higher than lower episodic AM. These results suggest that eye movements promote richness in autobiographical recall, particularly for those with strong AM.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
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 head0.104
GPT teacher head0.302
Teacher spread0.198 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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