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Record W2153519114 · doi:10.3109/00207450109149749

Experimental Production of Illusory (False) Memories in Reconstructions of Narratives: Effect Size and Potential Mediation by Right Hemispheric Stimulation from Complex, Weak Magnetic Fields

2001· article· en· W2153519114 on OpenAlexaff
Faye Healey, Michael A. Persinger

Bibliographic record

VenueInternational Journal of Neuroscience · 2001
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsLaurentian University
Fundersnot available
KeywordsPsychologySuggestibilityFalse memoryNarrativeHypnosisMediationCognitive psychologyDevelopmental psychologyRecallAudiologyLiterature

Abstract

fetched live from OpenAlex

This experiment was designed to discern the proportion of false, inferential and verbatim memories that would be included in the reconstruction, one week later, of a 5 min narrative containing ambiguous but emotional content about a little boy. After 48 subjects were administered Spiegel's Hypnosis Induction Profile, they listened to the narrative, were exposed to one of four applications of transcerebral weak, complex magnetic fields for 30 min and then given either an accurate or inaccurate short summary of the story. One week later the group who received the erroneous summary reported more false memories about the original story than did the reference group; this treatment accommodated about 40% of the variance in numbers of false memories. Only an indicator of electrical lability within the temporal lobes (but not hypnotizability) was strongly associated with the numbers of inferential memories but not the numbers of false memories. The group that received transcerebral stimulation over the right hemisphere by a complex magnetic field and the erroneous summary reported three times the numbers of false memories compared to the other groups. Whereas verbatim memories showed a strong primacy effect inferential memories exhibited a strong recency effect (eta(2) =.66).

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 categoriesnone
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 score0.173
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.274
Teacher spread0.259 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2001
Admission routes1
Has abstractyes

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