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Record W2902630834 · doi:10.1002/acp.3494

The efficacy of free‐recall, cognitive load, and closed‐ended questions when children are asked to falsely testify about a crime

2018· article· en· W2902630834 on OpenAlexafffund
Joshua Wyman, Ida Foster, Angela M. Crossman, Kevin Colwell, Victoria Talwar

Bibliographic record

VenueApplied Cognitive Psychology · 2018
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFalse accusationDenialPsychologyDeceptionRecallNarrativeSocial psychologyCognitionFalse memoryEyewitness testimonyFree recallStatement (logic)Cognitive loadDevelopmental psychologyCognitive psychologyLawPsychiatryPsychotherapist

Abstract

fetched live from OpenAlex

Summary The current study evaluated the benefits of free‐recall, cognitive load, and closed‐ended questions on children's (ages 6 to 11; N = 147) true and false eyewitness disclosures. Children witnessed an experimenter find a stranger's wallet and were then asked to make a false denial, false accusation, true denial, or true accusation regarding an alleged theft. Overall, the free‐recall question resulted in longer, more forthcoming and more detailed disclosures from older children and those who made a truthful accusation; however, children under the age of 9 and lie‐tellers mostly relied on the closed‐ended questions to discuss the theft. Although the cognitive load questions resulted in newly recalled information, there were no significant narrative differences between true and false statements on these questions. These findings suggest that forensic professionals should consider a child's developmental level, statement veracity, and disclosure‐type (denial vs. accusation) when examining the efficacy of these commonly used questioning strategies.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.028
GPT teacher head0.351
Teacher spread0.323 · 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; both teacher heads agree on what is shown here.

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

Citations18
Published2018
Admission routes2
Has abstractyes

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