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Record W1975201420 · doi:10.1080/13825580490904264

Aging and the Role of Working Memory Resources in Visuospatial Attention

2006· article· en· W1975201420 on OpenAlexaff
Wendy Loken Thornton, Naftali Raz

Bibliographic record

VenueAging Neuropsychology and Cognition · 2006
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsSimon Fraser University
FundersNational Institute on Aging
KeywordsWorking memoryPsychologyCognitive psychologyStroop effectCognitive loadAttentional controlCognitionCognitive resource theoryTask (project management)Cognitive agingDevelopmental psychologyNeuroscience

Abstract

fetched live from OpenAlex

Visuospatial attention has been shown to be robust to the effects of increasing age. Nonetheless, models linking individual differences in working memory capacity to attentional performance suggest that older adults may experience disruptions in visuospatial attention under conditions of resource load. Two experiments were conducted to investigate the effects of age and concurrent working memory load on two tasks that have been proposed to require posterior attentional processes. The findings suggest that loading working memory resources selectively disrupts performance on a nonintegrated Stroop task, whereas cue utilization remains intact. In addition, imposing a working memory load delays the deployment of visuospatial attention in both experiments. Regarding the effects of age, findings suggest that older adults can effectively perform both attentional tasks despite working memory load. Age differences did emerge in the time course of cue utilization. Findings point to the resilience of visuospatial attention in aging, even under conditions of significant cognitive load. We discuss these results and their implications for models postulating a role for working memory capacity in attentional behaviors.

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)0.000
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 score0.787
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.044
GPT teacher head0.301
Teacher spread0.257 · 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

Citations26
Published2006
Admission routes1
Has abstractyes

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