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Record W4384698274 · doi:10.1002/acp.4115

Examining the effects of negative emotion and interviewing procedure on eyewitness recall

2023· article· en· W4384698274 on OpenAlexafffund
Mark Snow, Joseph Eastwood

Bibliographic record

VenueApplied Cognitive Psychology · 2023
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsOntario Tech University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyRecallEyewitness memoryWitnessContext (archaeology)Cognitive interviewEyewitness testimonyAutobiographical memoryInterviewCognitionAffect (linguistics)Cognitive psychologyArousalSuggestibilityMemory errorsDevelopmental psychologySocial psychologyCommunication

Abstract

fetched live from OpenAlex

Abstract Witnessing or experiencing a crime can be emotionally distressing and this emotional reaction can affect the formation and retrieval of event‐related memory. Extant eyewitness research, however, has generated inconsistent conclusions regarding the effects of emotional arousal on eyewitness memory. In the present experiment, we used a mock witness paradigm to attempt to remedy several methodological limitations that have persisted in the literature and shed light on the effects of emotional memory within an investigative interviewing context. Participants ( N = 132) viewed either a Negative or Neutral video and either immediately or one week later provided their account of the video in a virtual interview procedure, consisting of either cognitive interview‐ based instructions or a free recall. Negative emotion was associated with selectively enhanced recall for the central aspects of the video. Participants who viewed the Negative video reported more details that were central to the target video than did those who viewed the Neutral video. The present findings highlight the potential for negative emotional events to lead to focally enhanced recall performance.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.258
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.084
GPT teacher head0.351
Teacher spread0.267 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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