Causal inference in cognitive neuroscience
Causal inference in cognitive neuroscience
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DOI：
10.1002/wcs.1650
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发表时间：
2023-04-09
影响因子：
3.9
通讯作者：
Davis, Isaac
中科院分区：
文献类型：
--
作者：
Danks, David;Davis, Isaac
因果推理是认知科学和神经科学，特别是认知神经科学领域许多研究工作的关键一步。统计知识足以进行预测和诊断，但行动和干预需要因果知识。大多数统计学课程和教科书都强调因果推理的困难，重点关注“相关并不意味着因果关系”的格言：可能存在多种因果可能性，通常有很多，与给定的观察到的统计数据一致。本文重点关注因果推理和其他类型推理所面临的概念问题和假设，主要关注认知神经科学。我们将推理方法与目标和挑战联系起来，并就如何为科学任务选择适当的工具提供具体指导。本文分类如下：心理学 > 理论和方法哲学 > 认知科学基础
Causal inference is a key step in many research endeavors in cognitive science and neuroscience, and particularly cognitive neuroscience. Statistical knowledge is sufficient for prediction and diagnosis, but causal knowledge is required for action and intervention. Most statistics courses and textbooks emphasize the difficulty of causal inference, focusing on the maxim that "correlation does not mean causation": there can be multiple causal possibilities, often many of them, consistent with given observed statistics. This paper focuses instead on the conceptual issues and assumptions that confront causal and other kinds of inference, primarily focusing on cognitive neuroscience. We connect inference methods with goals and challenges, and provide concrete guidance about how to select appropriate tools for the scientific task.This article is categorized under:Psychology > Theory and MethodsPhilosophy > Foundations of Cognitive Science