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Record W2937368830 · doi:10.1093/schbul/sbz019.324

T44. USING A VISUOSPATIAL MNEMONIC TO IMPROVE MEMORY IN PSYCHOSIS: A FEASIBILITY STUDY

2019· article· en· W2937368830 on OpenAlexaff
Ana Elisa Sousa, Yacine Mahdid, Martín Lepage

Bibliographic record

VenueSchizophrenia Bulletin · 2019
Typearticle
Languageen
FieldPsychology
TopicPsychoanalysis and Psychopathology Research
Canadian institutionsDouglas Mental Health University InstituteMcGill University
Fundersnot available
KeywordsMnemonicRecallPsychosisPsychologyEpisodic memoryPsychosocialEncoding (memory)Schizophrenia (object-oriented programming)Cognitive psychologyClinical psychologyPsychiatryCognition

Abstract

fetched live from OpenAlex

Episodic memory is severely impaired in psychosis, compromising daily living activities and psychosocial functioning. Despite being one of the strongest predictors of psychosocial functioning, memory performance shows only small to moderate improvement following psychosocial interventions. The use of encoding strategies (e.g., forming a story, mental imagery, or using categories to link words) results in a richer memory-trace that increases recall when compared to thinking about the word’s visual features or using repetition. However, people with psychosis tend not to spontaneously generate efficient strategies to facilitate recall. When trained in strategies such as semantic organization, they tend to show better outcomes in episodic memory performance. We investigated the effectiveness and feasibility of a visuospatial mnemonic strategy, the method of Loci (MoL), for improving episodic memory in psychosis. MoL takes advantage of mental visualization to facilitate encoding of disparate pieces of information, by relying on the “mental” allocation of the items to be memorized in several places. The efficacy of MoL and superiority over other mnemonics has been demonstrated in healthy subjects, and in older adults and depression; however, no study to date has evaluated the use of MoL in psychosis. We developed a short intervention to investigate whether MoL could improve memory performance in psychosis. It involved free recall of two 20-item (psychosis group) or 25-item (healthy control group) lists then, following MoL training, the encode and recall of two new lists using MoL. Forty healthy controls and five psychosis patients successfully completed the study. In healthy individuals, there was a significant improvement in recall of 2.8 items (SD = 4.1) after learning the MoL (t(39) = 4.2, p<0.001). No significant effect of MoL on recall was observed in the psychosis group (mean difference: 1.1, SD = 3.1; t(4) = 0.77, p>.05). All psychosis patients but one reported difficulty remembering their loci or their loci order during the post-test feasibility interview. In contrast to the control group, the psychosis group did not improve in episodic memory recall after learning the MoL. During the sessions, it was evident that the complexity of MoL, along with its heavy reliance on working memory and mental visualization, hindered mastering the strategy, resulting in fatigue and consequent stagnation or slight decrease in memory performance during the post-test. Our results resonate with some previous findings in which psychosis patients might benefit less than controls from visuospatial mnemonic strategies and that populations with reduced cognitive reserve capacity may fail to engage in task-appropriate processing that prevents them from successfully implementing MoL. We conclude that MoL might not be appropriate to people with psychosis experiencing specific cognitive deficits such as in executive functioning, and mental imagery. Future research should investigate MoL’s efficacy across a range of cognitive capacity in psychosis and assess less complex strategies (e.g. unitization), which are effective in populations with similar severity of cognitive deficits as those found in psychosis.

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)0.002
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: Non-randomized trial · Consensus signal: Non-randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.029
GPT teacher head0.362
Teacher spread0.333 · 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 designNon-randomized trial
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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