Although plasticity in the neural system underlies working memory, and working memory can be improved by training, there is thus far no evidence that children with developmental dyslexia can benefit from working-memor...Although plasticity in the neural system underlies working memory, and working memory can be improved by training, there is thus far no evidence that children with developmental dyslexia can benefit from working-memory training. In the present study, thirty dyslexic children aged 8-11 years were recruited from an elementary school in Wuhan, China. They received working-memory training including training in visuospatial memory, verbal memory, and central executive tasks. The difficulty of the tasks was adjusted based on the performance of each subject, and the training sessions lasted 40 minutes per day, for 5 weeks. The results showed that working-memory training significantly enhanced performance on the nontrained working memory tasks such as the visuospatial, the verbal domains, and central executive tasks in children with developmental dyslexia. More importantly, the visual rhyming task and reading fluency task were also significantly improved by training. Progress on working memory measures was related to changes in reading skills. These experimental findings indicate that working memory is a pivotal factor in reading development among children with developmental dyslexia, and interventions to improve working memory may help dyslexic children to become more proficient in reading.展开更多
Electroencephalographic studies using graph theoretic analysis have found aberrations in functional connectivity in children with developmental dyslexia.However,how the training with visual tasks can change the functi...Electroencephalographic studies using graph theoretic analysis have found aberrations in functional connectivity in children with developmental dyslexia.However,how the training with visual tasks can change the functional connectivity of the semantic network in developmental dyslexia is still unclear.We looked for differences in local and global topological properties of functional networks between 21 healthy controls and 22 dyslexic children(8–9 years old)before and after training with visual tasks in this prospective case-control study.The minimum spanning tree method was used to construct the subjects’brain networks in multiple electroencephalographic frequency ranges during a visual word/pseudoword discrimination task.We found group differences in the theta,alpha,beta and gamma bands for four graph measures suggesting a more integrated network topology in dyslexics before the training compared to controls.After training,the network topology of dyslexic children had become more segregated and similar to that of the controls.In theθ,αandβ1-frequency bands,compared to the controls,the pre-training dyslexics exhibited a reduced degree and betweenness centrality of the left anterior temporal and parietal regions.The simultaneous appearance in the left hemisphere of hubs in temporal and parietal(α,β1),temporal and superior frontal cortex(θ,α),parietal and occipitotemporal cortices(β1),identified in the networks of normally developing children was not present in the brain networks of dyslexics.After training,the hub distribution for dyslexics in the theta and beta1 bands had become similar to that of the controls.In summary,our findings point to a less efficient network configuration in dyslexics compared to a more optimal global organization in the controls.This is the first study to investigate the topological organization of functional brain networks of Bulgarian dyslexic children.Approval for the study was obtained from the Ethics Committee of the Institute of Neurobiology and the Institute for Population and Human Studies,Bulgarian Academy of Sciences(approval No.02-41/12.07.2019)on March 28,2017,and the State Logopedic Center and the Ministry of Education and Science(approval No.09-69/14.03.2017)on July 12,2019.展开更多
Deep reinforcement learning has shown remarkable capabilities in visual tasks,but it does not have a good generalization ability in the context of interference signals in the input images;this approach is therefore ha...Deep reinforcement learning has shown remarkable capabilities in visual tasks,but it does not have a good generalization ability in the context of interference signals in the input images;this approach is therefore hard to be applied to trained agents in a new environment.To enable agents to distinguish between noise signals and important pixels in images,data augmentation techniques and the establishment of auxiliary networks are proven effective solutions.We introduce a novel algorithm,namely,saliency-extracted Q-value by augmentation(SEQA),which encourages the agent to explore unknown states more comprehensively and focus its attention on important information.Specifically,SEQA masks out interfering features and extracts salient features and then updates the mask decoder network with critic losses to encourage the agent to focus on important features and make correct decisions.We evaluate our algorithm on the DeepMind Control generalization benchmark(DMControl-GB),and the experimental results show that our algorithm greatly improves training efficiency and stability.Meanwhile,our algorithm is superior to state-of-the-art reinforcement learning methods in terms of sample efficiency and generalization in most DMControl-GB tasks.展开更多
基金supported by grants from the National Natural Science Foundation of China, No. 30872132
文摘Although plasticity in the neural system underlies working memory, and working memory can be improved by training, there is thus far no evidence that children with developmental dyslexia can benefit from working-memory training. In the present study, thirty dyslexic children aged 8-11 years were recruited from an elementary school in Wuhan, China. They received working-memory training including training in visuospatial memory, verbal memory, and central executive tasks. The difficulty of the tasks was adjusted based on the performance of each subject, and the training sessions lasted 40 minutes per day, for 5 weeks. The results showed that working-memory training significantly enhanced performance on the nontrained working memory tasks such as the visuospatial, the verbal domains, and central executive tasks in children with developmental dyslexia. More importantly, the visual rhyming task and reading fluency task were also significantly improved by training. Progress on working memory measures was related to changes in reading skills. These experimental findings indicate that working memory is a pivotal factor in reading development among children with developmental dyslexia, and interventions to improve working memory may help dyslexic children to become more proficient in reading.
基金The study was supported by the National Science Fund of the Ministry of Education and Science(project DN05/14-2016,to JAD).
文摘Electroencephalographic studies using graph theoretic analysis have found aberrations in functional connectivity in children with developmental dyslexia.However,how the training with visual tasks can change the functional connectivity of the semantic network in developmental dyslexia is still unclear.We looked for differences in local and global topological properties of functional networks between 21 healthy controls and 22 dyslexic children(8–9 years old)before and after training with visual tasks in this prospective case-control study.The minimum spanning tree method was used to construct the subjects’brain networks in multiple electroencephalographic frequency ranges during a visual word/pseudoword discrimination task.We found group differences in the theta,alpha,beta and gamma bands for four graph measures suggesting a more integrated network topology in dyslexics before the training compared to controls.After training,the network topology of dyslexic children had become more segregated and similar to that of the controls.In theθ,αandβ1-frequency bands,compared to the controls,the pre-training dyslexics exhibited a reduced degree and betweenness centrality of the left anterior temporal and parietal regions.The simultaneous appearance in the left hemisphere of hubs in temporal and parietal(α,β1),temporal and superior frontal cortex(θ,α),parietal and occipitotemporal cortices(β1),identified in the networks of normally developing children was not present in the brain networks of dyslexics.After training,the hub distribution for dyslexics in the theta and beta1 bands had become similar to that of the controls.In summary,our findings point to a less efficient network configuration in dyslexics compared to a more optimal global organization in the controls.This is the first study to investigate the topological organization of functional brain networks of Bulgarian dyslexic children.Approval for the study was obtained from the Ethics Committee of the Institute of Neurobiology and the Institute for Population and Human Studies,Bulgarian Academy of Sciences(approval No.02-41/12.07.2019)on March 28,2017,and the State Logopedic Center and the Ministry of Education and Science(approval No.09-69/14.03.2017)on July 12,2019.
基金Project supported by the Academic and Technical Leaders and Backup Candidates Program of Anhui Province,China(No.2019h211)the Natural Science Foundation of Anhui Province,China(No.2208085ME128)。
文摘Deep reinforcement learning has shown remarkable capabilities in visual tasks,but it does not have a good generalization ability in the context of interference signals in the input images;this approach is therefore hard to be applied to trained agents in a new environment.To enable agents to distinguish between noise signals and important pixels in images,data augmentation techniques and the establishment of auxiliary networks are proven effective solutions.We introduce a novel algorithm,namely,saliency-extracted Q-value by augmentation(SEQA),which encourages the agent to explore unknown states more comprehensively and focus its attention on important information.Specifically,SEQA masks out interfering features and extracts salient features and then updates the mask decoder network with critic losses to encourage the agent to focus on important features and make correct decisions.We evaluate our algorithm on the DeepMind Control generalization benchmark(DMControl-GB),and the experimental results show that our algorithm greatly improves training efficiency and stability.Meanwhile,our algorithm is superior to state-of-the-art reinforcement learning methods in terms of sample efficiency and generalization in most DMControl-GB tasks.
