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Record W3161694028 · doi:10.31234/osf.io/syjnv

Exploring the sociodemographic, clinical and neuropsychological factors associated with relational memory in schizophrenia

2020· preprint· en· W3161694028 on OpenAlexaff
Ana Elisa Sousa, Martín Lepage, Jennifer D. Ryan

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsBaycrest HospitalMcGill UniversityDouglas Mental Health University Institute
Fundersnot available
KeywordsNeurocognitivePsychologySchizophrenia (object-oriented programming)NeuropsychologyWorking memoryCognitionTask (project management)Executive functionsClinical psychologyPopulationCognitive psychologyDevelopmental psychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

Episodic memory impairments in schizophrenia, including its relational memory component, are associated with significant clinical and functional variables, such as employment status, social and occupational functioning, and early and long-term remission. The Transverse Patterning (TP) task, a computer task designed to detect impairment in relational memory performance, has been used as a measure of relational binding deficit in this population. Individuals with schizophrenia often fail to learn TP with standard training and more than a quarter of patients fail the task even when extensive training is provided. TP failure may reflect multiple cognitive deficits (i.e. executive functions, working memory and visual memory). Identifying the neuropsychological factors, awareness distinctions, and strategy use differences between TP learners and non-learners can improve our understanding of underlying mechanisms required for successful performance in TP and, consequently, improve cognitive interventions that are targeted to ameliorate relational memory performance. The present study investigated the sociodemographic, clinical, neuropsychological and task-specific (i.e. task awareness and strategy use) factors associated with TP learning and impairment in schizophrenia. Sixty-nine participants with a diagnosis of schizophrenia or related psychosis were recruited for this study (66 completers). They completed two versions of the TP task (one semantically-rich and one relational-binding dependent) and answered a questionnaire to evaluate task awareness and strategy use in each condition and had sociodemographic and clinical data collected at screening. Twenty-six participants (38.8%) were unable to learn all the task rules after extensive training. In a subset of participants who underwent neurocognitive assessment (N = 29), learners had significantly superior verbal, visual and working memory, executive functions and overall cognitive functioning compared to non-learners. Group comparisons also suggested superior awareness of task rules and pairs relationships for learners compared to non-learners. Learners used cognitive strategies (such as memorizing how the objects interacted, naming the objects and qualifying their interactions with action verbs) more often than non-learners, and strategies seemed to be more elaborated for learners than for non-learners. This study confirmed previous findings that a subset of individuals with schizophrenia shows significant relational memory impairment assessed by the transverse-patterning paradigm which is not improved by stepwise TP training. It also brings new insight into factors associated with TP task performance, including neurocognitive markers that seem to contribute to TP learning. Finally, this study points to task awareness and strategy use components underlying successful TP learning. This knowledge could be useful for future interventions that are targeted to improve relational memory performance in schizophrenia when stepwise training is not sufficient.

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)0.003
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.321
GPT teacher head0.371
Teacher spread0.049 · 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
Published2020
Admission routes1
Has abstractyes

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