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Record W1897025992 · doi:10.1002/bsl.2115

The Cognitive Interview Buffers the Effects of Subsequent Repeated Questioning in the Absence of Negative Feedback

2014· article· en· W1897025992 on OpenAlexafffund
Lauren Wysman, Alan Scoboria, Julie Gawrylowicz, Amina Memon

Bibliographic record

VenueBehavioral Sciences & the Law · 2014
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaLeverhulme Trust
KeywordsRecallCognitionPsychologyConsistency (knowledge bases)InterviewSocial psychologyCognitive interviewQuality (philosophy)Face (sociological concept)Developmental psychologyCognitive psychologyPsychiatryComputer scienceSociology

Abstract

fetched live from OpenAlex

The Cognitive Interview (CI) is known to elicit high-quality information from cooperative witnesses. The present study examined whether the CI protects against two suggestive interview techniques: repeated questioning and negative feedback. Young adults (n = 98) watched one of two crime videos and were interviewed with either a CI or free recall. One week later, a second interviewer asked answerable questions (about information in the video) and unanswerable questions (about information not in the video). Half of the participants received negative feedback about their performance. All participants were then asked the questions a second time. The CI resulted in more correct responses to answerable questions and fewer errors to unanswerable questions at the first questioning. The CI produced the highest consistency for answerable questions in the face of repeated questioning in the absence of negative feedback, and resulted in the most changes in responses to answerable questions when negative feedback was applied. No effects were found for unanswerable questions. The CI protected against repeated questioning, but only in the absence of negative feedback.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.568
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.067
GPT teacher head0.341
Teacher spread0.273 · 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

Citations5
Published2014
Admission routes2
Has abstractyes

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