S66. USING UNITIZATION TO CIRCUMVENT RELATIONAL MEMORY DEFICITS IN SCHIZOPHRENIA
Notice bibliographique
Résumé
Abstract Background Relational memory (RM) is disproportionately affected in schizophrenia. Unitization is a cognitive strategy that compensates for RM deficits by facilitating the combination of disparate pieces of information to form a single functional unit. Unitization has been effective in circumventing RM impairment in hippocampal amnesia and older adults with RM deficits when performing a Transverse Patterning (TP) task. We developed a brief intervention using unitization to circumvent RM impairment in individuals with schizophrenia when learning arbitrary item-relations in a TP task. We subsequently developed the Relational Trip Task (RTT), which uses pairs of real-life stimuli (faces, places, objects) and a narrative frame for encoding to investigate if unitization generalizes to a more relatable real-life context. Methods Twenty-two individuals with an RM deficit and a diagnosis of schizophrenia or related disorder were pseudo-randomized to either the unitization or control condition, from which 19 completed all TP tasks and 17 completed RTT. TP performance was measured at screening. TP and RTT task performances were measured pre-post learning unitization. During the RTT-unitized task, participants created their own unitizations, with assistance (50% of the task) and on their own. The control group received unitization training following study participation. The TP-unitized control group results were included in the analysis. Results TP task performance (percentage of correct trials) did not significantly differ between control (M = 49%, SD = 13%) and unitization groups (M= 60%, SD = 18%) at screening (t(17) = -1.506, p = 0.15). A 2-way mixed analysis of variance (ANOVA) did not reveal a group-task interaction for unitization and control group accuracy in the four TP versions (F(3,51) = 2.38, p = 0.08). A main effect of task (F(3, 51) = 9.43, p > 0.001) was decomposed using Tukey HSD pairwise post-hoc analyses and showed significantly higher TP task accuracy following unitization (M = 85%, SD = 19%) compared to the TP task at screening (M = 55%, SD = 16% p > 0.001), before unitization (M = 61%, SD = 19%, p = 0.001) and when prompting self-unitization (M = 68%, SD = 28%, p = 0.04). No other significant differences in task accuracies were revealed. Group accuracies in the RTT were compared using a 2-way mixed ANOVA, and yielded a significant interaction between group and task accuracy (F(1,15) = 4.93, p = 0.042). Simple main effect analysis showed that accuracy in the RTT post unitization training (M = 90%, SD = 9%) was higher than before training (M = 77%, SD = 14%, p = 0.046), but performance between the same versions of the RTT did not significantly differ in the control group (M1 = 73%, SD = 19%; M2 = 70%, SD = 23%; p = 0.26). Discussion TP performance improved when the unitization strategy was provided, but not when the self-generation of unitization was encouraged. Improved RTT performance was limited to the unitization group, suggesting that effects were unitization-specific rather than lead by practice. Logic follows that this strategy may be generalizable to more relatable, real-life contexts. Self-generation of unitization was effective in improving task performance when assistance was provided rather than merely encouraged, suggesting that patients may benefit from guidance generating their own unitizations rather than integrating this strategy on their own. These findings should be replicated in a larger sample, and strategies to provide effective self-generation of unitization should be explored. Moreover, the extent to which the RTT can detect differential relational memory impairment in individuals with schizophrenia when compared to healthy controls warrants further investigation.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».