A defining feature of children's cognition is the especially slow development of their attention. Despite a rich behavioral literature characterizing the development of attention, little is known about how developing attentional abilities modulate neural representations in children. This information is critical to understanding how attentional development shapes the way children process information. One possibility is that attention might be less likely to shape neural representations in children as compared with adults. In particular, representations of attended items may be less likely to be enhanced relative to unattended items. To investigate this possibility, we measured brain activity using fMRI while children (seven to nine years; male and female) and adults (21-31 years; male and female) performed a one-back task in which they were directed to attend to either motion direction or an object in a display where both were present. We used multivoxel pattern analysis to compare decoding accuracy of attended and unattended information. Consistent with attentional enhancement, we found higher decoding accuracy for task-relevant information (i.e., objects in the object-attended condition) than for task-irrelevant information (i.e., motion in the object-attended condition) in adults' visual cortices. However, in children's visual cortices, both task-relevant and task-irrelevant information were decoded equally well. What is more, whole-brain analysis showed that the children represented task-irrelevant information more than adults in multiple regions across the brain, including the prefrontal cortex. These findings show that (1) attention does not modulate neural representations in the child visual cortex, and (2) developing brains can, and do, represent more information than mature brains. Children have been shown to struggle with maintaining their attention to specific information, and at the same time, can show better learning of "distractors." While these are critical properties of childhood, their underlying neural mechanisms are unknown. To fill in this critical knowledge gap, we explored how attention shapes what is represented in children's and adults' brains using fMRI while both were asked to focus on just one of two things (objects and motion). We found that unlike adults, who prioritize the information they were asked to focus on, children represent both what they were asked to prioritize and what they were asked to ignore. This shows that attention has a fundamentally different impact on children's neural representations.
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http://dx.doi.org/10.1523/JNEUROSCI.0159-23.2023 | DOI Listing |
Mol Inform
January 2025
Faculty of Information Technology, HUTECH University, 700000, Ho Chi Minh City, Vietnam.
In recent times, graph representation learning has been becoming a hot research topic which has attracted a lot of attention from researchers. Graph embeddings have diverse applications across fields such as information and social network analysis, bioinformatics and cheminformatics, natural language processing (NLP), and recommendation systems. Among the advanced deep learning (DL) based architectures used in graph representation learning, graph neural networks (GNNs) have emerged as the dominant and highly effective framework.
View Article and Find Full Text PDFBioinformatics
January 2025
School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun, 130117, Jilin China.
Motivation: Most drugs start on their journey inside the body by binding the right target proteins. This is the reason that numerous efforts have been devoted to predicting the drug-target binding during drug development. However, the inherent diversity among molecular properties, coupled with limited training data availability, poses challenges to the accuracy and generalizability of these methods beyond their training domain.
View Article and Find Full Text PDFSci Rep
January 2025
School of New Media, Peking University, Beijing, China.
This paper intends to solve the limitations of the existing methods to deal with the comments of tourist attractions. With the technical support of Artificial Intelligence (AI), an online comment method of tourist attractions based on text mining model and attention mechanism is proposed. In the process of text mining, the attention mechanism is used to calculate the contribution of each topic to text representation on the topic layer of Latent Dirichlet Allocation (LDA).
View Article and Find Full Text PDFSci Rep
January 2025
Shenzhen City Polytechnic, Shenzhen, 518055, China.
In the rapidly evolving field of personalized news recommendation, capturing and effectively utilizing user interests remains a significant challenge due to the vast diversity and dynamic nature of user interactions with news content. Existing recommendation models often fail to fully integrate candidate news items into user interest modeling, which can result in suboptimal recommendation accuracy and relevance. This limitation stems from their insufficient ability to jointly consider user history and the characteristics of candidate news items in the modeling process.
View Article and Find Full Text PDFNeurosurgery
January 2025
Department of Neurosciences, Experimental Neurosurgery and Neuroanatomy, KU Leuven, Leuven, Belgium.
Background And Objectives: It remains a challenge to monitor cerebrovascular autoregulation (CA) reliably and dynamically in an intensive care unit. The objective was to build a proof-of-concept active CA model exploiting advances in representation learning and the full complexity of the arterial blood pressure (ABP) and intracranial pressure (ICP) signal and outperform the pressure reactivity index (PRx).
Methods: A porcine cranial window CA data set (n = 20) was used.
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