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Record W3198039721 · doi:10.1167/jov.21.9.2899

Isolating Working Memory Capacity Deficits from Sustained Attention Deficits in Patients with Schizophrenia Using a Single Behavioral Task

2021· article· en· W3198039721 on OpenAlexaff
Geoffrey Harrison, Chelsea Wood‐Ross, Mike Best, Jessie Eriksen, Daryl E. Wilson, Chris Bowie

Bibliographic record

VenueJournal of Vision · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeural and Behavioral Psychology Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsWorking memoryPsychologyTask (project management)CognitionCognitive psychologySchizophrenia (object-oriented programming)AudiologyDevelopmental psychologyNeuroscienceMedicinePsychiatry

Abstract

fetched live from OpenAlex

Recent advances in our understanding of working memory (WM) suggest that performance on tasks that assess WM confound an individual’s maximum WM capacity with trial-to-trial variations in their attentional control. This limitation regarding one of the most commonly assessed cognitive constructs in clinical psychology raises important questions regarding the true locus of performance deficits found in the assessment of clinical populations such as Schizophrenia Spectrum Disorders (SSD). Fortunately, new methodologies have been developed which can isolate and measure these two cognitive traits independently within a single task referred to as a discrete whole-report WM task. Within the task, participants are presented with six colored squares during the initial memory array and must make responses about each item. This design allows for a more detailed assessment of memory performance (0-6 items recalled correctly) which can be used to determine an individual’s complete memory performance on every trial. From this data, a computational model described in Hakim et al., (2020) determines an individual’s maximum WM capacity and their sustained attention capacity, a measure of the probability with which a participant achieves their maximum WM capacity throughout the experiment. The present study administered a whole-report WM task to 26 individuals, 15 with SSD and 11 healthy age-matched controls. Our results suggest participants with SSD were significantly impaired in both sustained attention capacity and maximum WM capacity, though to a larger extent in their sustained attention capacity. Moreover, removing trials where participants reported only 0 or 1 squares correctly significantly reduced differences in performance across the two groups in the whole report task. These results indicate that tracking and removing trials that reflect complete attentional lapses may provide more sensitive indices that better reflect true cognitive deficits.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.101
GPT teacher head0.333
Teacher spread0.232 · 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 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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