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可解释的人工智能在认知研究中的应用:一个范围审查
Shakran Mahmood1, Colin Teo1,2,3, Jeremy Sim2
1Lee Kong Chian School of Medicine Nanyang Technological University Singapore Singapore.
Ibrain
|September 30, 2024
概括
这篇评论探讨了认知神经科学中可解释的人工智能 (XAI) 方法. 对于理解认知,XAI技术是有前途的,但在因果关系和可重现性方面面临挑战.
科学领域:
- 认知神经科学 认知神经科学
- 人工智能的人工智能
- 可解释的人工智能
背景情况:
- 人工智能 (AI) 的进步需要值得信赖的AI,导致可解释AI (XAI) 的兴起.
- 最近的神经科学研究强调了XAI在研究认知过程中的关键作用.
- 了解认知功能和功能障碍需要强大的方法来解释AI模型.
研究的目的:
- 系统地审查和分析应用于认知神经科学的XAI方法.
- 确定用于调查认知机制的流行XAI技术.
- 为在认知神经科学研究中应用XAI开发一个框架.
主要方法:
- 根据乔安娜·布里格斯研究所和PRISMA-ScR指导方针进行范围审查.
- 搜索的主要数据库:MEDLINE,Embase,科学网,Cochrane,谷歌学者.
- 质量评估涉及两个独立审查员进行数据选,提取和专题分析.
主要成果:
- 包括过去十年的12项实验研究.
- 75%的研究集中在正常认知 (感知,记忆等) 上. ),25%的认知功能受损.
- 内在的XAI (58.3%) 是最常见的,其次是基于归因 (41.7%) 和基于示例 (8.3%) 的方法;可解释性是局部的 (66.7%) 或全球的 (33.3%).
结论:
- 在认知神经科学中,XAI方法提供了预测能力和稳定性.
- 限制包括过度简化,混因素和不一致性.
- 未来的研究需要解决XAI应用中的因果关系和可重现性挑战.
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