Prior studies explored the early development of memory monitoring and control. However, little work has examined cross-cultural similarities and differences in metacognitive development in early childhood. In the present research, we investigated a total of 100 Japanese and German preschool-aged children's memory monitoring and control in a visual perception task. After seeing picture items, some of which were repeated, children were presented with picture pairs, one of which had been presented earlier and the other was a novel item. They then were asked to identify the previously presented picture. Children were also asked to evaluate their confidence about their selection, and to sort the responses to be used for being awarded with a prize at the end of the test. Both groups similarly expressed more confidence in the accurately remembered items than in the inaccurately remembered items, and their sorting decision was based on their subjective confidence. Japanese children's sorting more closely corresponded to memory accuracy than German children's sorting, however. These findings were further confirmed by a hierarchical Bayesian estimation of metacognitive efficiency. The present findings therefore suggest that early memory monitoring and control have both culturally similar and diverse aspects. The findings are discussed in light of broader sociocultural influences on metacognition.

Download full-text PDF

Source
http://dx.doi.org/10.3758/s13421-021-01263-1DOI Listing

Publication Analysis

Top Keywords

memory monitoring
16
monitoring control
16
japanese german
8
presented picture
8
remembered items
8
children's sorting
8
memory
5
control
4
control japanese
4
german preschoolers
4

Similar Publications

Groundwater monitoring is a crucial part of groundwater remediation that produces data from various strategically placed wells to maintain a water quality standard. Using the United States Department of Energy's Hanford 100-HRD area well data, recurrent neural networks are trained in the form of one-dimensional Convolutional Neural Networks (CNNs), Long Short Term Memory (LSTM) networks, and Dual-stage Attention-based LSTM (DA-LSTM) networks to reduce monitoring costs and increase data sampling responsiveness that is subject to laboratory analysis delays, with the best network being DA-LSTM achieving an R score of 0.82.

View Article and Find Full Text PDF

Air pollution monitoring and modeling are the most important focus of climate and environment decision-making organizations. The development of new methods for air quality prediction is one of the best strategies for understanding weather contamination. In this research, different air quality parameters were forecasted, including Carbon Monoxide (CO), Nitrogen Monoxide (NO), Nitrogen Dioxide (NO), Ozone (O), Sulphur Dioxide (SO), Fine Particles Matter (PM), Coarse Particles Matter (PM), and Ammonia (NH).

View Article and Find Full Text PDF

: Controlling hypertension may reduce the risk of cognitive impairment. A marker for the identification of hypertensive patients who are more likely to suffer cognitive impairment would be of clinical benefit. In our research, 105 patients with newly diagnosed primary hypertension were assessed at the Department of Neurology, the University of Debrecen.

View Article and Find Full Text PDF

Background: Anxiety and depression represent prevalent yet frequently undetected mental health concerns within the older population. The challenge of identifying these conditions presents an opportunity for artificial intelligence (AI)-driven, remotely available, tools capable of screening and monitoring mental health. A critical criterion for such tools is their cultural adaptability to ensure effectiveness across diverse populations.

View Article and Find Full Text PDF

A novel hybrid model for air quality prediction via dimension reduction and error correction techniques.

Environ Monit Assess

December 2024

School of Big Data and Statistics, Anhui University, Hefei, 230601, Anhui, China.

The monitoring of air pollution through the air quality index (AQI) is a fundamental tool in ensuring public health protection. Accurate prediction of air quality is necessary for the timely implementation of measures to control and manage air pollution, thereby mitigating its detrimental impact on human health. A novel hybrid prediction model is proposed, which is EMD-KMC-EC-SSA-VMD-LSTM.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!