MétaCan
Menu
Back to cohort
Record W3095767273 · doi:10.1167/jov.20.11.1261

Visual memories can recover from recognition-induced memory biases

2020· article· en· W3095767273 on OpenAlexaff
Joseph M. Saito, Keisuke Fukuda

Bibliographic record

VenueJournal of Vision · 2020
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRecallTask (project management)Encoding (memory)ColoredRecognition memoryPsychologyObject (grammar)Computer scienceCognitive psychologyArtificial intelligencePattern recognition (psychology)Cognition

Abstract

fetched live from OpenAlex

How do we retain accurate visual memories over a long time? Studies have demonstrated that successfully retrieving a memory increases the likelihood that it can be retrieved later. However, other studies shown that information provided during retrieval can alter how the original memory is reported (i.e., misinformation effect). These seemingly-contradictory findings suggest that retrieval calls the memory into a malleable state where it is augmented or modified by information available at that time. To test this, we first had participants encode 240 pictures of colored real objects. Then, memory for those objects was tested in two types of retrieval tasks (i.e., the recognition bias task and the baseline recall task) on the same day and the day after. In the recognition bias task, participants were first presented with a grayscale object image and indicated whether or not they remembered encoding its colored version. Subsequently, participants completed a two-second-long recognition practice in which they saw two colored versions of the same object and identified the one more similar to the encoded version. Participants then recalled the encoded object’s color. The baseline recall task was identical, except for the recognition practice, which was replaced by a two-second blank retention interval. We found that irrespective of retrieval type, memories retrieved on Day 1 were more likely to be retrieved on Day 2 than memories not tested on Day 1. Additionally, recall following recognition practice was biased towards the probe judged to be more similar to the encoded object. Interestingly, however, this recognition-induced memory bias was transient and did not influence memory recall on Day 2. Taken together, these data support our hypothesis that retrieval brings visual memories into a malleable state to be augmented or altered by memory-relevant information. Fortunately, recognition-induced memory biases may not permanently change the encoded representation.

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)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.109
GPT teacher head0.340
Teacher spread0.231 · 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 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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal of VisionSame topicMemory Processes and InfluencesFrench-language works237,207