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Record W3095231624 · doi:10.1016/j.actpsy.2013.01.017

Queasy does it: False alcohol memories lead to diminished alcohol preferences

2013· article· en· W3095231624 on OpenAlexaff
Seema L. Clifasefi, Daniel M. Bernstein, Antonia Mantonakis, Elizabeth F. Loftus

Bibliographic record

VenueArca (British Columbia Electronic Library Network) · 2013
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsBrock UniversityKwantlen Polytechnic University
FundersNational Institute on Alcohol Abuse and Alcoholism
KeywordsPsychologyAlcoholPsychological interventionAffect (linguistics)PreferenceSocial psychologyDevelopmental psychologyPsychiatryClinical psychology

Abstract

fetched live from OpenAlex

Studies have shown that false memories can be implanted via innocuous suggestions, and that these memories can play a role in shaping people's subsequent attitudes and preferences. The current study explored whether participants (N = 147) who received a false suggestion that they had become ill drinking a particular type of alcohol would increase their confidence that the event had occurred, and whether their new-found belief would subsequently affect their alcohol preferences. Results indicated that participants who received a suggestion that they had gotten sick drinking rum or vodka before the age of 16 reported increased confidence that the suggested experience had occurred. Moreover, participants who received a false alcohol suggestion also showed a strong trend to report diminished preference for the specified type of alcohol after the false suggestion. Implantation of a false memory related to one's past drinking experiences may influence current drink preferences and could be an important avenue for further exploration in the development of alcohol interventions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.235
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations20
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueArca (British Columbia Electronic Library Network)Same topicSubstance Abuse Treatment and OutcomesFrench-language works237,207