MétaCan
Menu
Back to cohort
Record W4225112707 · doi:10.1080/02699931.2022.2069683

“When did you see it?” The effect of emotional valence on temporal source memory in aging

2022· article· en· W4225112707 on OpenAlexaff
Irene Ceccato, Pasquale La Malva, Adolfo Di Crosta, Rocco Palumbo, Matteo Gatti, Davide Momi, Maria Grazia Logrieco, Mirco Fasolo, Nicola Mammarella, Erika Borella, Alberto Di Domenico

Bibliographic record

VenueCognition & Emotion · 2022
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsKrembil Foundation
FundersUniversità degli Studi G. d'Annunzio Chieti - Pescara
KeywordsPsychologyValence (chemistry)Emotional valenceDevelopmental psychologyYoung adultAffect (linguistics)CognitionCognitive psychologyAudiologyCommunicationNeuroscience

Abstract

fetched live from OpenAlex

Previous studies consistently showed age-related differences in temporal judgment and temporal memory. Importantly, emotional valence plays a crucial role in older adults' information processing. In this study, we examined the effects of emotions at the intersection between time and memory, analysing age-related differences in a temporal source memory task. Twenty-five younger adults (age range 18-35), 25 old adults (age range 65-74), and 25 old-old adults (age range 75-84) saw a series of emotional pictures in three sessions separated by a one-day rest period. In the fourth session, participants were asked to indicate in which session (1, 2, or 3) they saw each picture. Results showed that old-old adults tended to collocate negative pictures distant in time, while positive stimuli were remembered as more recent than real, compared to neutral pictures. To a lower extent, people over 65 showed the same pattern of results. In contrast, emotional valence did not affect younger adults' temporal positioning of stimuli. Current findings fit well with the Socio-Emotional Selectivity Theory's assumptions and extended the literature on the positivity effect to temporal source memory.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score0.915

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.034
GPT teacher head0.275
Teacher spread0.241 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueCognition & EmotionSame topicMemory Processes and InfluencesFrench-language works237,207