The use of landmarks for navigation develops throughout childhood. Here, we examined the developmental trajectory of egocentric and allocentric navigation based on landmark information in an on-screen virtual environment in 39 5-6-year-olds, 43 7-8-year-olds, and 41 9-10-year-olds. We assessed both categorical performance, indicating the notion of location changes based on the landmarks, as well as metrical performance relating to the precision of the representation of the environment. We investigated whether age, sex, spatial working memory, verbal working memory, and verbal production of left and right contributed to the development of navigation skills. In egocentric navigation, Categorical performance was already above chance at 5 years of age and was positively related to visuo-spatial working memory and the production of left/right, whereas metrical performance was only related to age. Allocentric navigation started to develop between 5 and 8 years of age and was related to sex, with boys outperforming girls. Both boys and girls seemed to rely more on directional landmark information as compared to positional landmark information. To our knowledge, this study is the first to give insight into the relative contribution of different cognitive abilities to navigation skills in school-aged children.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9221540PMC
http://dx.doi.org/10.3390/brainsci12060776DOI Listing

Publication Analysis

Top Keywords

working memory
16
age sex
12
allocentric navigation
8
categorical performance
8
metrical performance
8
memory verbal
8
navigation skills
8
years age
8
navigation
7
age
5

Similar Publications

Pattern memory cannot be completely and truly realized in deep neural networks.

Sci Rep

December 2024

School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China.

The unknown boundary issue, between superior computational capability of deep neural networks (DNNs) and human cognitive ability, has becoming crucial and foundational theoretical problem in AI evolution. Undoubtedly, DNN-empowered AI capability is increasingly surpassing human intelligence in handling general intelligent tasks. However, the absence of DNN's interpretability and recurrent erratic behavior remain incontrovertible facts.

View Article and Find Full Text PDF

Cerebellar-driven cortical dynamics can enable task acquisition, switching and consolidation.

Nat Commun

December 2024

Computational Neuroscience Unit, Intelligent Systems Labs, Faculty of Engineering, University of Bristol, Bristol, UK.

The brain must maintain a stable world model while rapidly adapting to the environment, but the underlying mechanisms are not known. Here, we posit that cortico-cerebellar loops play a key role in this process. We introduce a computational model of cerebellar networks that learn to drive cortical networks with task-outcome predictions.

View Article and Find Full Text PDF

Trivalent recombinant protein vaccine induces cross-neutralization against XBB lineage and JN.1 subvariants: preclinical and phase 1 clinical trials.

Nat Commun

December 2024

Laboratory of Aging Research and Cancer Drug Target, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.

The immune escape capacities of XBB variants necessitate the authorization of vaccines with these antigens. In this study, we produce three recombinant trimeric proteins from the RBD sequences of Delta, BA.5, and XBB.

View Article and Find Full Text PDF

RNA-ModX: a multilabel prediction and interpretation framework for RNA modifications.

Brief Bioinform

November 2024

In-Service Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, 250 Wuxing Street, 110, Taipei, Taiwan.

Accurate prediction of RNA modifications holds profound implications for elucidating RNA function and mechanism, with potential applications in drug development. Here, the RNA-ModX presents a highly precise predictive model designed to forecast post-transcriptional RNA modifications, complemented by a user-friendly web application tailored for seamless utilization by future researchers. To achieve exceptional accuracy, the RNA-ModX systematically explored a range of machine learning models, including Long Short-Term Memory (LSTM), Gated Recurrent Unit, and Transformer-based architectures.

View Article and Find Full Text PDF

Deep Learning-Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording.

Adv Sci (Weinh)

December 2024

Department of Biomedical Engineering, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Kowloon, Hong Kong SAR, China.

The patch clamp technique is a fundamental tool for investigating ion channel dynamics and electrophysiological properties. This study proposes the first artificial intelligence framework for characterizing multiple ion channel kinetics of whole-cell recordings. The framework integrates machine learning for anomaly detection and deep learning for multi-class classification.

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!