Knowledge Commons of Institute of Automation,CAS
How Does the Experimental Setting Affect the Conclusions of Neural Encoding Models? | |
Xiaohan Zhang1,2![]() ![]() ![]() | |
2022 | |
会议名称 | Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022) |
会议日期 | 2022 |
会议地点 | Marseille |
摘要 | Recent years have witnessed the tendency of neural encoding models on exploring brain language processing using naturalistic stimuli. Neural encoding models are data-driven methods that require an encoding model to investigate the mystery of brain mechanisms hidden in the data. As a data-driven method, the performance of encoding models is very sensitive to the experimental setting. However, it is unknown how the experimental setting further affects the conclusions of neural encoding models. This paper systematically investigated this problem and evaluated the influence of three experimental settings, i.e., the data size, the cross-validation training method, and the statistical testing method. Results demonstrate that inappropriate cross-validation training and small data size can substantially decrease the performance of encoding models, especially in the temporal lobe and the frontal lobe. And different null hypotheses in significance testing lead to highly different significant brain regions. Based on these results, we suggest a block-wise cross-validation training method and an adequate data size for increasing the performance of linear encoding models. We also propose two strict null hypotheses to control false positive discovery rates. |
七大方向——子方向分类 | 自然语言处理 |
国重实验室规划方向分类 | 语音语言处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52049 |
专题 | 多模态人工智能系统全国重点实验室_自然语言处理 |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, CAS 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.CAS Center for Excellence in Brain Science and Intelligence Technology |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Xiaohan Zhang,Shaonan Wang,Chengqing Zong. How Does the Experimental Setting Affect the Conclusions of Neural Encoding Models?[C],2022. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
zxh_2022.lrec.pdf(5576KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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