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Psychological Influence on the Effects of Cognitive Training in an Individual with Emotional Distress

2015· article· en· W2267875607 on OpenAlexaff
Ada W. S. Leung, Lauren Barrett

Bibliographic record

VenueInternational Journal of Clinical Psychiatry and Mental Health · 2015
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPsychologyPsychological distressCognitionDistressClinical psychologyTraining (meteorology)Emotional distressPsychotherapistAnxietyPsychiatryMental health

Abstract

fetched live from OpenAlex

Cognitive therapy can be an important part of a stroke survivor’s rehabilitation but the effects of psychological factors on its training outcome is unclear. This study investigated the neuroplastic effects of working memory training on a single stroke survivor who has emotional distress due to prolonged motor impairment. The participant completed a six-week auditory working memory training program at home. Neurocognitive tests and functional magnetic resonance imaging (fMRI) testing were conducted before and after training. Posttest neuroimaging results indicated increased neural efficiency for the trained task. However, there was no behavioral improvement on neurocognitive tests, and the participant’s motivation declined as the training progressed. This case study indicated that while the performance of the trained task could be improved due to automaticity of task practice, psychological factors might play a key role in limiting the transfer of learning to other cognitive skills. Nevertheless, more research is needed to determine if these findings can be replicated in a similar clinical population.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.128
GPT teacher head0.469
Teacher spread0.341 · 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
Published2015
Admission routes1
Has abstractyes

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