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Record W4309289670 · doi:10.3389/fpsyg.2022.1024498

The attentional boost effect and perceptual degradation: Assessing the influence of attention on recognition memory

2022· article· en· W4309289670 on OpenAlexaff
Mitchell R. P. LaPointe, Tamara M Rosner, Javier Ortiz-Tudela, L. Lorentz, Bruce Milliken

Bibliographic record

VenueFrontiers in Psychology · 2022
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsMcMaster UniversityMount Allison University
Fundersnot available
KeywordsPerceptionRedundancy (engineering)Cognitive psychologyEncoding (memory)PsychologyRecognition memoryCognitionComputer scienceNeuroscience

Abstract

fetched live from OpenAlex

Researchers have suggested that the recognition memory effects resulting from two separate attentional manipulations-attentional boost and perceptual degradation-may share a common cause; namely a transient up-regulation of attention at the time of encoding that leads to enhanced memory performance at the time of retrieval. Prior research has demonstrated that inducing two similar transient shifts of attention simultaneously produces redundant performance in memory. In the present study, we sought to evaluate the combined influence of the attentional boost and perceptual degradation on recognition memory. If these two effects share a common cause, then we ought to observe a redundancy in memory performance, such that these two factors interact. Yet, across four experiments we fail to observe such a redundancy in recognition memory. We evaluate these results using the limited resource model of attention and speculate on how combining transient shifts of attention may produce redundant memory performance in the one case, but non-redundant performance in the other case.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.034
GPT teacher head0.327
Teacher spread0.293 · 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 designObservational
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

Citations6
Published2022
Admission routes1
Has abstractyes

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