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Record W4384826582 · doi:10.21203/rs.3.rs-3157762/v1

Characterizing the contributions of cue familiarity for the retrieval of autobiographical memories

2023· preprint· en· W4384826582 on OpenAlexafffund
Lauri Gurguryan, Haopei Yang, Stefan Köhler, Signy Sheldon

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsWestern UniversityMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAutobiographical memorySemantic memoryCognitive psychologyPsychologyEpisodic memoryAffect (linguistics)Computer scienceCognitionRecallCommunication

Abstract

fetched live from OpenAlex

<title>Abstract</title> Retrieving an autobiographical memory requires a cue to initiate processes related to accessing and then elaborating on a past personal experience. Prior work has shown that the familiarity of a cue can influence the autobiographical memory retrieval process. Extending on this work, we tested how different aspects of cue familiarity—i.e., amount of past exposure and amount of semantic knowledge associated with the cue concept—can affect how we access and remember in detail autobiographical memories. In Experiment 1, we measured reaction times to access and retrieve memories in response to cue words. In Experiment 2 we examined the details with which participants described memories in response to cues. For both experiments, participants provided estimates of lifetime exposure and semantic knowledge associated with each cue. In Experiment 1, we found lifetime exposure, independently of estimates of semantic knowledge, led to quicker memory access and in Experiment 2, we found both lifetime exposure and semantic knowledge interactively enhanced the ability to described detailed memories. These results provide new evidence that distinct features of familiar cues—lifetime exposure and semantic knowledge—differently contribute to how autobiographical memories are retrieved and described.

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)0.005
metaresearch head score (Gemma)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.191
GPT teacher head0.445
Teacher spread0.254 · 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 designBench or experimental
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

Citations1
Published2023
Admission routes2
Has abstractyes

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