MétaCan
Menu
Back to cohort
Record W4297221143 · doi:10.3389/fnhum.2022.928583

How shifting visual perspective during autobiographical memory retrieval influences emotion: A change in retrieval orientation

2022· review· en· W4297221143 on OpenAlexafffund
Selen Küçüktaş, Peggy L. St. Jacques

Bibliographic record

VenueFrontiers in Human Neuroscience · 2022
Typereview
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsAutobiographical memoryPerspective (graphical)PsychologyCognitive psychologyEpisodic memoryAffect (linguistics)CognitionRecallNeuroscienceComputer scienceCommunication

Abstract

fetched live from OpenAlex

Visual perspective during autobiographical memory (AM) retrieval influences how people remember the emotional aspects of memories. Prior research in emotion regulation has also shown that shifting from an own eyes to an observer-like perspective is an efficient way of regulating the affect elicited by emotional AMs. However, the impact of shifting visual perspective is also dependent on the nature of the emotion associated with the event. The current review synthesizes behavioral and functional neuroimaging findings from the event memory and emotion regulation literature that examine how adopting particular visual perspectives and actively shifting across them during retrieval alters emotional experience, by primarily focusing on emotional intensity. We review current theories explaining why shifts in perspectives may or may not change the emotional characteristics of memories, then propose a new theory, suggesting that the own eyes and observer-like perspectives are two different retrieval orientations supported by differential neural activations that lead episodic details to be reconstructed in specific ways.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.100
GPT teacher head0.367
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations24
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueFrontiers in Human NeuroscienceSame topicMemory Processes and InfluencesFrench-language works237,207