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选择解释而不是表现:基于机器学习的洞察力,从大脑连接性来预测人类智能的洞察力

Jonas A Thiele¹, Joshua Faskowitz², Olaf Sporns²

¹Department of Psychology I - Clinical Psychology and Psychotherapy, Würzburg University, Marcusstr. 9-11, 97070 Würzburg, Germany.

|December 11, 2024

概括

通过关注可解释的大脑特征,从大脑连接中预测认知能力得到了改善. 了解哪些功能性大脑连接预测智力,可以提供超出预测表现的神经生物学见解.

科学领域:

认知神经科学是一种认知神经科学.
神经成像是一种神经成像.
心理测量心理测量心理测量

背景情况:

研究越来越多地将认知能力与大脑特征联系起来,特别是功能性大脑连接.
当前的预测模型经常达到统计学意义,但缺乏神经生物学解释性.
识别特定的预测性大脑特征对于理解认知的神经基础至关重要.

研究的目的:

调查哪些功能性大脑连接可以预测一般,结晶和流体智能.
在预测建模中强调可解释性,以增强对人类认知的概念理解.
探索特定连接模式在情报预测中的作用.

主要方法:

使用了预先注册的研究设计.
分析了806名健康成年人的功能性大脑连接数据,复制了322名成年人的复制样本.
预测建模专注于识别可解释的功能性大脑连接,与智力的不同方面相关.

主要成果:

智能的特定组件 (一般,结晶,流体) 和连接测量期间的任务显著影响了预测.
智能可以从各种系统范围的功能性大脑连接的组合中预测,而不是单一的集合.
确定了预测性连接模式,补充了有关智力和大脑区域的现有理论.

结论:

优先考虑对预测性大脑特征的系统评估,提高了认知预测研究的解释价值.
预测建模中的解释性是推动我们对人类智能的神经生物学理解的关键.
未来的研究应该平衡预测性能与识别有意义的认知神经相关的认知.

关键词:

认知能力认知能力认知能力.功能性大脑连接性功能性大脑连接性情报情报情报的情报情报的情报.机器学习是机器学习.预测建模预测建模

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