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Record W4241920389 · doi:10.31234/osf.io/jyu93

Exploring the generalisation of affect across related experiences: A study of affective bleed and memory precision

2021· preprint· en· W4241920389 on OpenAlexfundno aff
Christopher R. Madan, Elizabeth A. Kensinger

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsPsychologyValence (chemistry)Affect (linguistics)Cognitive psychologyContext (archaeology)Associative propertyContent-addressable memorySocial psychologyDevelopmental psychologyCommunicationComputer science

Abstract

fetched live from OpenAlex

When positive or negative events occur in a context, memory can be reflected in how positively or negatively we judge that context, and also by whether, upon later remembering that emotional event, we can bring to mind the specific context in which it occurred. We examined each of these forms of associative memory, comparing performance when positive, negative, or neutral stimuli were paired with a context. By doing so, we could contribute to debates about how emotion affects associative binding. Participants intentionally formed associations between famous places and positive, negative, or neutral pictures. In Experiment 1, we observed shifts in judgments for places as a function of associated valence; effects summated over accumulated experiences. In Experiment 2, memory precision was examined by manipulating whether lures on a five-alternative forced-choice recognition, included different places or alternate views of the target. Results revealed emotional impairments in associative memory and a selective decrease in precision for negative pairs. Eye-tracking showed more saccades between pictures for remembered pairs, but less of these inter-item saccades when pictures were emotional. Overall findings suggest that positive and negative affect are transferred similarly through episodic associations, although the specificity of context transfer may be lessened for negative content.

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 categoriesnone
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 score0.460
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.174
GPT teacher head0.350
Teacher spread0.175 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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