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Record W2618862595 · doi:10.3389/fpsyg.2017.00843

Prior Knowledge of Object Associations Shapes Attentional Templates and Information Acquisition

2017· review· en· W2618862595 on OpenAlexafffund
Rachel Wu, Jiaying Zhao

Bibliographic record

VenueFrontiers in Psychology · 2017
Typereview
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTask (project management)Selection (genetic algorithm)PsychologyCognitive psychologyCognitionObject (grammar)Computer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

Studies on attentional selection typically use unpredictable and meaningless stimuli, such as simple shapes and oriented lines. The assumption is that using these stimuli minimizes effects due to learning or prior knowledge, such that the task performance indexes a "pure" measure of the underlying cognitive ability. However, prior knowledge of the test stimuli and related stimuli acquired before or during the task impacts performance in meaningful ways. This mini review focuses on prior knowledge of object associations, because it is an important, yet often ignored, aspect of attentional selection. We first briefly review recent studies demonstrating that how objects are selected during visual search depends on the participant's prior experience with other objects associated with the target. These effects appear with both task-relevant and task-irrelevant knowledge. We then review how existing object associations may influence subsequent learning of new information, which is both a driver and a consequence of selection processes. These insights highlight the importance of one aspect of prior knowledge for attentional selection and information acquisition. We briefly discuss how this work with young adults may inform other age groups throughout the lifespan, as learners gradually increase their prior knowledge. Importantly, these insights have implications for developing more accurate measurements of cognitive abilities.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.053
GPT teacher head0.397
Teacher spread0.344 · 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.

Study designOther design
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

Citations10
Published2017
Admission routes2
Has abstractyes

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