Danyang Huang
Biographic Data
| ID | 7183310 |
|---|---|
| NAME | Danyang Huang |
| GIVEN NAMES | Danyang |
| FAMILY NAME | Huang |
| SIGNATURE | HUANG D |
| AFFILIATIONS | Renmin University of China |
| ORCID | 0000-0002-4053-2680 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2014 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Asymmetry of diagnostic information integration in audiovisual category learning
摘要: 本研究采用家族相似性类别学习范式, 探究视听双通道诊断性信息在多维度类别学习中的整合机制及其不对称性。两个实验共招募153名大学生, 结合计算模型分析学习后的类别表征。实验1考察听觉信息诊断性对视觉类别学习的影响, 操纵听觉诊断性为高(0.9)、一致(0.75)和低(0.6)三种水平。结果发现, 当视听诊断性一致时, 学习者对双通道特征维度的加工最为充分, 表现出跨通道整合的增益效应。实验2则考察视觉信息诊断性对听觉类别学习的影响, 发现诊断性一致条件下的整合增益没有出现, 视觉诊断性对听觉特征维度的学习没有显著影响, 体现视听信息加工的相对独立性。综上, 多维度类别学习中的跨通道信息整合存在不对称性:视觉任务可以整合听觉信息, 而听觉任务对视觉信息的整合深度有限。本研究首次揭示了多维度听觉类别学习的学习效率与表征策略, 并讨论了视听类别学习在表征机制上的差异。
Network Gradient Descent Algorithm for Decentralized Federated Learning
We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the NGD method, only statistics (e.g., parameter estimates) need to be communicated, minimizing the risk of privacy. Meanwhile, different clients communicate with each other directly according to a carefully designed networ…
Influence of self-serving leadership on employees' helping behavior
We explored the effect of self-serving leadership on employees' helping behavior, focusing on the mediating role of moral disengagement and the moderating role of prosocial motivation. We recruited employees and direct supervisors of six companies in China, and analyzed data from 295 participants using structural equation modeling and hierarchical regression. Self-serving leadership had a significant negative impact on employees' helping behavior…
A Statistical Model for Social Network Labeling
We consider a social network from which one observes not only network structure (i.e., nodes and edges) but also a set of labels (or tags, keywords) for each node (or user). These labels are self-created and closely related to the user’s career status, life style, personal interests, and many others. Thus, they are of great interest for online marketing. To model their joint behavior with network structure, a complete data model is developed. The…
Feature Screening for Ultrahigh Dimensional Categorical Data With Applications
Ultrahigh dimensional data with both categorical responses and categorical covariates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable statistical tool. We propose a Pearson chi-square based feature screening procedure for categorical response with ultrahigh dimensional categorical covariates. The proposed procedure can be directly applied for detection of important interaction effect…
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Feature Screening for Ultrahigh Dimensional Categorical Data With Applications
Ultrahigh dimensional data with both categorical responses and categorical covariates are frequently encountered in the analysis of big data, for which feature screening has become an indispensable statistical tool. We propose a Pearson chi-square based feature screening procedure for categorical response with ultrahigh dimensional categorical covariates. The proposed procedure can be directly applied for detection of important interaction effect…
A Statistical Model for Social Network Labeling
We consider a social network from which one observes not only network structure (i.e., nodes and edges) but also a set of labels (or tags, keywords) for each node (or user). These labels are self-created and closely related to the user’s career status, life style, personal interests, and many others. Thus, they are of great interest for online marketing. To model their joint behavior with network structure, a complete data model is developed. The…
Influence of self-serving leadership on employees' helping behavior
We explored the effect of self-serving leadership on employees' helping behavior, focusing on the mediating role of moral disengagement and the moderating role of prosocial motivation. We recruited employees and direct supervisors of six companies in China, and analyzed data from 295 participants using structural equation modeling and hierarchical regression. Self-serving leadership had a significant negative impact on employees' helping behavior…
Network Gradient Descent Algorithm for Decentralized Federated Learning
We study a fully decentralized federated learning algorithm, which is a novel gradient descent algorithm executed on a communication-based network. For convenience, we refer to it as a network gradient descent (NGD) method. In the NGD method, only statistics (e.g., parameter estimates) need to be communicated, minimizing the risk of privacy. Meanwhile, different clients communicate with each other directly according to a carefully designed networ…
Asymmetry of diagnostic information integration in audiovisual category learning
摘要: 本研究采用家族相似性类别学习范式, 探究视听双通道诊断性信息在多维度类别学习中的整合机制及其不对称性。两个实验共招募153名大学生, 结合计算模型分析学习后的类别表征。实验1考察听觉信息诊断性对视觉类别学习的影响, 操纵听觉诊断性为高(0.9)、一致(0.75)和低(0.6)三种水平。结果发现, 当视听诊断性一致时, 学习者对双通道特征维度的加工最为充分, 表现出跨通道整合的增益效应。实验2则考察视觉信息诊断性对听觉类别学习的影响, 发现诊断性一致条件下的整合增益没有出现, 视觉诊断性对听觉特征维度的学习没有显著影响, 体现视听信息加工的相对独立性。综上, 多维度类别学习中的跨通道信息整合存在不对称性:视觉任务可以整合听觉信息, 而听觉任务对视觉信息的整合深度有限。本研究首次揭示了多维度听觉类别学习的学习效率与表征策略, 并讨论了视听类别学习在表征机制上的差异。
Artificial Intelligence (3 works) · Computer Science (3 works) · Mathematics (2 works) · Statistical Methods and Inference (2 works) · Statistics (2 works) · Algorithm (1 work) · Artificial neural network (1 work) · Asymmetry (1 work) · Bayesian Methods and Mixture Models (1 work) · Categorical variable (1 work)