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Record W2053966717 · doi:10.1002/acp.1629

Distinguishing true from false memories in forensic contexts: Can phenomenology tell us what is real?

2009· article· en· W2053966717 on OpenAlex
Tammy A. Marche, Charles J. Brainerd, Valerie F. Reyna

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueApplied Cognitive Psychology · 2009
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFalse memoryPsychologyPhenomenology (philosophy)NarrativeCognitive psychologySuggestibilityMemory errorsAutobiographical memorySocial psychologyRecallEpistemologyLinguistics

Abstract

fetched live from OpenAlex

Abstract We studied the extent to which subjective ratings of memory phenomenology discriminate true‐ and false‐memory responses, and whether degree of gist‐based processing influences false memory and phenomenology, in a classic forensic task, the Gudjonsson Suggestibility Scale (GSS). Participants heard a narrative of a robbery followed by suggestive questions about the content of the narrative. They were asked to rate the items they recognized as studied using the Memory Characteristics Questionnaire (MCQ). Consistent with studies of word lists, there were phenomenological differences between true and false memory responses: memory phenomenology was richer for true than for false memories, which supports opponent‐process accounts of false memory such as fuzzy‐trace theory. Thus, phenomenology is a useful means for differentiating experienced from non‐experienced events in forensic contexts. Copyright © 2009 John Wiley & Sons, Ltd.

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.

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)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.687
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.328
Teacher spread0.288 · 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