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Record W2626176996

Emotionality impairs memory for associations - eScholarship

2009· article· en· W2626176996 on OpenAlexaboutno aff
Jeremy B. Caplan, Esther Fujiwara, Christine Lau, Christopher R. Madan

Bibliographic record

VenueProceedings of the Annual Meeting of the Cognitive Science Society · 2009
Typearticle
Languageen
FieldNeuroscience
TopicMemory and Neural Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsAmygdalaPsychologyAssociation (psychology)EmotionalityHippocampal formationEpisodic memoryEmotional memoryCognitive psychologyHippocampusRecallDevelopmental psychologyNeuroscienceCognition
DOInot available

Abstract

fetched live from OpenAlex

Emotionality impairs memory for associations Christopher Madan University of Alberta Christine Lau University of Alberta Jeremy Caplan University of Alberta Esther Fujiwara University of Alberta Abstract: The neural mechanism implicated in the emotional memory enhancement is amygdala modulation of hippocampal learning. However, most emotional memory studies test memory for items only. Because memory for associations is hippocampal-dependent, one would predict an even greater emotional enhancement for association memory than for item memory. Touryan et al. (2007) found emotionality impaired incidental association learning between peripheral neutral objects and central emotional scenes, despite enhanced memory for the emotional scenes themselves. Here we ask whether this detriment might turn into an enhancement effect for associative memory, a hippocampal-dependent memory function, if the associated information is central to the task and association learning is intentional. Instead we found that the detrimental effect holds even in this stronger test of the amygdala- hippocampal circuit, suggesting that amygdala enhancement of hippocampal function can be overridden by a bias to process emotional items at the expense of associations.

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)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
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 score0.050
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.068
GPT teacher head0.331
Teacher spread0.262 · 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

Citations0
Published2009
Admission routes1
Has abstractyes

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