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Autobiographical Memory

2024· book-chapter· en· W4400773750 on OpenAlexaff
Carina L. Fan, Stephanie Simpson, H. Moriah Sokolowski, Brian Levine

Bibliographic record

VenueOxford University Press eBooks · 2024
Typebook-chapter
Languageen
FieldPsychology
TopicIdentity, Memory, and Therapy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAutobiographical memoryPsychologyHistoryCognitive psychologyRecall

Abstract

fetched live from OpenAlex

Abstract Autobiographical memory—memory for one’s personal past—is a multifaceted mnemonic activity that evolves throughout the life span and interacts with numerous other cognitive functions. Retrieving personal past events engages processes of cue specification, search, and elaboration of details within the specified event. The retrieved content varies from specific episodes unique in time and place to more general representations of autobiographical facts (personal semantics). As expected given this complexity, autobiographical memory is mediated by distributed brain networks, with key regions in the medial temporal lobes and their connections to both anterior and posterior cortical regions supporting different levels of specificity in memory retrieval. These patterns only partially overlap with those evoked by laboratory-based episodic memory paradigms. Whereas most empirical work on autobiographical memory focuses on the recall of particular past events, more recent research concerns individual differences in the way that people tend to remember their past. The formal study of autobiographical memory dates to the 19th century, but research in this field is burgeoning, particularly in relation to brain network connectivity. New paradigms that bridge the gap between traditional laboratory memory tasks and rich, naturalistic autobiographical memories will enhance the understanding of memory as it operates in everyday life.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.026
GPT teacher head0.246
Teacher spread0.220 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2024
Admission routes1
Has abstractyes

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