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Record W2897881029 · doi:10.1016/j.jalz.2018.06.1207

P2‐513: LACK OF METACOGNITION DOES NOT PREDICT WORST COGNITIVE PERFORMANCE

2018· article· en· W2897881029 on OpenAlexaboutno aff
Rafael Mattos Tavares, Alvaro Teixeira da Costa, Renata Brant de Souza Melo, Aline Curcio de Moraes, Carlos Ausbert Quintela Chagas Filho, Danielle Christine Ribeiro Viana, Raquel Vassão Araújo, Antônio Pereira Gomes Neto, Ronnielly Melo Tavares

Bibliographic record

VenueAlzheimer s & Dementia · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
Fundersnot available
KeywordsMetacognitionPsychologyMetamemoryCognitionVerbal fluency testCognitive psychologyPopulationFluencyDevelopmental psychologyTest (biology)NeuropsychologyMedicinePsychiatry

Abstract

fetched live from OpenAlex

Metacognition is defined as the knowledge and reflective capacities one has concerning one's own cognitive functioning.In recent years, metacognition studies have also advanced to older adults and the efforts are based on the idea that self-reflection can serve as a “bridge between decision making and memory, between learning and motivation, between learning and cognitive development”.Recent studies suggest that unawareness of memory impairment reflects not only damage to self-referential networks but also to memory and to the connections between these networks.This evidence hints a bidirectional relationship between self-reflection about memory and memory performance.The authors aim to verify if lack of metacognition predicts worst cognitive performance in a population-based study. The data derived from a study previously published underwent a post-hoc analysis. In this referred study,a metacognitive questionnaire(Memory Assessment Clinic-Q,MAC-Q) and a brief cognitive assessment[semantic verbal fluency test of animals(SVFTa) and fruits(SVFTf), and Montreal cognitive assessment(MoCA)] were applied in a volunteer sample of community-dwelling middle-aged and older adults(above 40y), with high educational level (12y or more).Participants were divided into four groups:Normal Group(NG), Subjective Memory Complaints(SMC), Lack of Metacognition(LM) and Objective Cognitive Impairment(OCI).The groups LM(n=24) and OCI(n=18), selected due to lowest cognitive scores, were the only ones below cutoff.The LM group has altered cognitive tests without memory complaints, showing memory unawareness and the OCI group has memory complaints with altered cognitive tests, denoting self-awareness about its memory.The analysis was adjusted for age, since it was the only significant variable of distinction between the groups(among others, such as gender, schooling and marital status).These two groups were compared(Mann-Whitney test) for outcomes in the brief cognitive battery. Significant differences in cognitive performance were not found.In the middle-aged (40–64y) subgroup, the scores between LM vs. OCI were:SVFTf(median/standard deviation,12±2,8 vs. 15±4;p=0,573),SVFTa (15,5±6,4 vs. 20±11,7;p=0,811) and MoCA(23,5±2,8 vs. 25±2;p=0,469).Significance was also not found in the elderly (above 64y) subgroup:SVFTf(12,5±4,8 vs. 12±3,3;p=0,591),SVFTa (15,5±5,3 vs. 16±4,7;p=0,533) and MoCA(21±3,8 vs. 21±4,8;p=0,747). Lack of metacognition does not predict worst cognitive performance,in a community-dwelling middle-aged and elderly population with high educational level, although in the middle-aged subgroup, there is a consistent tendency to lower scores.

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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.054
GPT teacher head0.342
Teacher spread0.288 · 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
Published2018
Admission routes1
Has abstractyes

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