Understanding Temporal Patterns of Metacognitive Monitoring and Control in 5- to 6-Year-Olds: A Latent Class Vector-Autoregression Analysis.

Efficient metacognition relies on a fine-tuned interplay between monitoring and control. However, these temporal dynamics between monitoring and control are still poorly understood, which limits our understanding of why some children succeed and other children struggle with metacognition. We assesse...

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Publicado en:Developmental Psychology Vol. 62; no. 4; pp. 753 - 765
Autores principales: Buehler, Florian Jonas, Oeri, Niamh
Formato: Artículo
Publicado: American Psychological Association Apr2026
Materias:
Acceso en línea:Ver este registro en EBSCOhost
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      dt: Apr2026
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      pub: American Psychological Association
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        10.1037/dev0002071
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        atl: Understanding Temporal Patterns of Metacognitive Monitoring and Control in 5- to 6-Year-Olds: A Latent Class Vector-Autoregression Analysis.
      aug:
        au:
          Buehler, Florian Jonas
          Oeri, Niamh
        affil: Department of Developmental Psychology, University of Bern
      su:
        Education of parents
        Task performance
        Executive function
        Sex distribution
        Age distribution
        Child development
        Child behavior
        Children
        Cognitive testing
        Research funding
        Cluster analysis (Statistics)
        Data analysis
        Scientific observation
        Kruskal-Wallis Test
        Time series analysis
        Structural equation modeling
        Descriptive statistics
        Intraclass correlation
        Statistics
        Data analysis software
      sug:
        subj:
          Education of parents
          Task performance
          Executive function
          Sex distribution
          Age distribution
          Child development
          Child behavior
          Children
          Cognitive testing
          Research funding
          Cluster analysis (Statistics)
          Data analysis
          Scientific observation
          Kruskal-Wallis Test
          Time series analysis
          Structural equation modeling
          Descriptive statistics
          Intraclass correlation
          Statistics
          Data analysis software
      keyword:
        latent class vector autoregression
        metacognitive control
        metacognitive monitoring
        off-task
        temporal patterns
        latent class vector autoregression
        metacognitive control
        metacognitive monitoring
        off-task
        temporal patterns
      ab: Efficient metacognition relies on a fine-tuned interplay between monitoring and control. However, these temporal dynamics between monitoring and control are still poorly understood, which limits our understanding of why some children succeed and other children struggle with metacognition. We assessed metacognitive monitoring, control, and off-task behavior in an unsolvable task in which participants built a wooden snake according to a plan. Participants were N = 123 typically developing 5- to 6-year-olds (M = 5.45 years, SD = 0.59, 52% female). We coded monitoring, control, and off-task behavior in 5-s intervals, resulting in a total of 6,150 observations. Based on children's monitoring, control, and off-task behaviors, we ran a latent class vector-autoregression analysis. We identified four latent clusters. The four clusters display distinct behavioral patterns over time, marked by varying levels of monitoring, control, and off-task behaviors. Subsequent analyses showed that younger children showed less stable metacognition than older children. Understanding differences in metacognitive dynamics is particularly important when trying to understand why children have metacognitive difficulties and may have important implications for tailoring interventions to the metacognitive needs of children. Public Significance Statement: The study suggests that the temporal dynamics between metacognitive behaviors can be reliably observed in 5- to 6-year-olds. Based on the behavior children show during a problem-solving task, four different subgroups could be identified. The subgroups differed in the temporal associations of monitoring, control, and off-task behavior.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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