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Record W2028100076 · doi:10.1037/a0028905

Intentional forgetting reduces color-naming interference: Evidence from item-method directed forgetting.

2012· article· en· W2028100076 on OpenAlexaff
Yuh‐Shiow Lee, Huang-mou Lee, Jonathan M. Fawcett

Bibliographic record

VenueJournal of Experimental Psychology Learning Memory and Cognition · 2012
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPsychologyForgettingMotivated forgettingPerceptionCognitive psychologyTask (project management)Colored

Abstract

fetched live from OpenAlex

In an item-method-directed forgetting task, Chinese words were presented individually, each followed by an instruction to remember or forget. Colored probe items were presented following each memory instruction requiring a speeded color-naming response. Half of the probe items were novel and unrelated to the preceding study item, whereas the remaining half of the probe items were a repetition of the preceding study item. Repeated probe items were either identical to the preceding study item (E1, E2), a phonetic reproduction of the preceding study item (E3), or perceptually matched to the preceding study item (E4). Color-naming interference was calculated by subtracting color-naming reaction times made in response to a string of meaningless symbols from that of the novel and repeated conditions. Across all experiments, participants recalled more to-be-remembered (TBR) than to-be-forgotten (TBF) study words. More importantly, Experiments 1 and 2 found that color-naming interference was reduced for repeated TBF words relative to repeated TBR words. Experiments 3 and 4 further found that this effect occurred at the perceptual rather than semantic level. These findings suggest that participants may bias processing resources away from the perceptual representation of to-be-forgotten information.

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)0.002
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 score0.017
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.081
GPT teacher head0.398
Teacher spread0.317 · 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
Published2012
Admission routes1
Has abstractyes

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