...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Intelligence: A Multidisciplinary Journal >A neurocomputational model of developmental trajectories of gifted children under a polygenic model: When are gifted children held back by poor environments?
【24h】

A neurocomputational model of developmental trajectories of gifted children under a polygenic model: When are gifted children held back by poor environments?

机译：多基因模型下有天赋儿童发育轨迹的神经科学模型：贫困环境剩下的有限儿童何时举行？

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

From the genetic side, giftedness in cognitive development is the result of contribution of many common genetic variants of small effect size, so called polygenicity (Spain a al., 2016). From the environmental side, educationalists have argued for the importance of the environment for sustaining early potential in children, showing that bright poor children are held back in their subsequent development (Feinstein, 2003a). Such correlational data need to be complemented by mechanistic models showing how gifted development results from the respective genetic and environmental influences. A neurocomputational model of cognitive development is presented, using artificial neural networks to simulate the development of a population of children. Variability was produced by many small differences in neurocomputational parameters each influenced by multiple artificial genes, instantiating a polygenic model, and by variations in the level of stimulation from the environment. The simulations captured several key empirical phenomena, including the non-linearly of developmental trajectories, asymmetries in the characteristics of the upper and lower tails of the population distribution, and the potential of poor environments to hold back bright children. M a computational level, 'gifted' networks tended to have higher capacity, higher plasticity, less noisy neural processing, a lower impact of regressive events, and a richer environment. However, individual instances presented heterogeneous contributions of these neurocomputational factors, suggesting giftedness has diverse causes.
机译：从遗传方面，认知发展的天才是许多常见遗传变异的贡献的结果，所谓的多种子质（西班牙A Al。，2016）。从环境方面，教育家们认为环境对儿童早期潜力的重要性，表明明亮的贫困儿童被留在其随后的发展中（Feinstein，2003A）。这种相关数据需要由机械模型互补，显示有天然的发展如何从各自的遗传和环境影响方面产生。提出了一种认知发展的神经计算机模型，利用人工神经网络来模拟群体的发展。通过多种人工基因的诸如多种人造基因的许多小差异产生的变异性，实例化多种子型模型，并通过环境的刺激水平的变化。仿真捕获了几个关键的经验现象，包括发展轨迹的非线性，在人口分布的上下尾部的特征中的不对称，以及贫困环境的潜力，以阻止亮的孩子。 M一个计算层面，“天赋”网络倾向于具有更高的容量，更高的可塑性，噪音较小的神经处理，较低的回归事件的影响，以及更丰富的环境。然而，个别实例呈现了这些神经检查因素的异质贡献，建议天才具有多种原因。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号