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Record W4391318202 · doi:10.1111/ggi.14809

<scp>Mild cognitive impairment</scp> decreases the accuracy of own memory monitoring

2024· letter· en· W4391318202 on OpenAlexaboutno aff
Yoshifumi Takahashi, Kenichiro Sato, Daichi Yamashiro, Susumu Ogawa, Yan Li, Tomoki Furuya, Yuho Shimizu, Daisuke Cho, Tomoya Takahashi, Hiroyuki Suzuki, Yoshinori Fujiwara

Bibliographic record

VenueGeriatrics and gerontology international/Geriatrics & gerontology international · 2024
Typeletter
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsCognitive impairmentMemory impairmentCognitionPsychologyCognitive psychologyComputer scienceAudiologyMedicineNeuroscience

Abstract

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Do individuals with mild cognitive impairment (MCI) with impaired memory function also have impaired judgments about their own memories? The monitoring of one's memory is called metamemory and has been a popular topic in psychology for half a century.1 Memory monitoring is measured by a memory monitoring task.1 In the memory monitoring task, participants are first asked to learn items (either words or pictures), and immediately after learning each item, they are asked to provide their judgments of learning (JOLs; namely, judgments of how well a remembered item can be recalled later) ranging from 0% (I will never recall the item on a later test) to 100% (I will definitely recall the item on a later test). Finally, participants are asked to recall the items. The findings from these paradigms have shown that JOLs do not necessarily match actual memory performance.2 Age has also been examined as a factor in this discrepancy between memory performance and memory monitoring,3, 4 and, surprisingly, no differences in the correlation between memory performance and JOLs (i.e., accuracy of memory monitoring) were found between older and younger adults.3, 4 However, the accuracy of memory monitoring in older adults remains controversial. Many studies have examined age factors in memory monitoring and are limited to comparing younger and healthy older adults, whereas very few studies have examined differences in memory monitoring in relation to cognitive function in older adults. In particular, older adults with MCI have not only impaired memory function but also subjective complaints of forgetfulness, which suggests that memory monitoring is also impaired. Therefore, in this study, we aimed to determine the relationship between manipulated MCI by cognitive function test score and the accuracy of memory monitoring. We hypothesize that older adults with MCI show reduced accuracy in memory monitoring compared with healthy older adults. Participants were 113 older Japanese individuals (mean age = 71.56 years, age range = 65–85 years, women = 91%) aged 65 years or older who applied for health programs offered by Japanese local governments in 2021 and 2022. Cognitive function was measured by the Japanese version of the Montreal Cognitive Assessment (MoCA-J), with 25/26 points used as the cutoff point for MCI.5, 6 Metamemory monitoring was measured based on previous studies. The learning phase consisted of 60 pictures, which were presented for 1 s each, and participants were required to learn the pictures. Immediately, after each picture presentation, participants were asked to provide a judgment of learning with a key response (0–100%). After the learning phase, a recognition test was conducted with 120 pictures. In the recognition test, participants were asked to make a yes/no judgment by pressing a key, and participants responded “yes” to pictures presented in the learning phase. A total of 52 participants were assigned to the MCI group (mean age = 72.31, age range = 65–85 years) and 75 to the control group (mean age = 70.93, age range = 65–82 years) as a result of the MoCA. Recognition performance was evaluated as the percentage of correct hits (i.e., the percentage of “yes” responses to items presented in the learning phase). We conducted a t-test between the MCI and control groups for the correct hit rate. The findings showed that the MCI group had a significantly lower percentage of correct hits than the control group [t (107) = 2.36, padj = 0.02, Cohen's d = 0.46]. Goodman–Kruskal's gamma was used to measure the accuracy of memory monitoring.7 The Goodman-Kruskal gamma is one of the indicators of rank correlation.8 In this study, the correlation between JOLs and memory performance ranges from −1 (complete mismatch between JOLs and memory performance) to 1 (complete match between JOLs and memory performance). The analysis showed that the MCI group (gamma = 0.04, SD = 0.16) was less accurate in memory monitoring than the control group (gamma = 0.10, SD = 0.16) [t (107) = 2.12, padj = 0.04, Cohen's d = 0.42]. The results of the analysis of memory performance and memory monitoring are presented in Table 1. The present study suggests that participants with MCI have impaired memory performance and memory monitoring. Despite this significant result, some issues still need to be solved. First, most participants in the health program were female, so future studies should include greater sex diversity. Second, MCI was manipulatively defined in this study using the MoCA. A comprehensive understanding of MCI may be obtained by examining the relationship between MCI selected by various criteria and memory monitoring. This study was a correlational study. Therefore, We cannot determine causality. Further studies are needed to evaluate whether a causal relationship exists between MCI and impaired memory monitoring. This would contribute to providing evidence for the prevention of MCI. This work was supported by JSPS KAKENHI Grant Number JP 22H01098. The authors have no conflicts of interest to disclose. Data supporting the findings of this study are available from the corresponding author upon reasonable request.

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)0.004
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 score0.014
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.038
GPT teacher head0.347
Teacher spread0.309 · 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".

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Citations0
Published2024
Admission routes1
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