• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

相似文献

1
Keep it real: rethinking the primacy of experimental control in cognitive neuroscience.保持真实:重新思考认知神经科学中实验控制的首要地位。
Neuroimage. 2020 Nov 15;222:117254. doi: 10.1016/j.neuroimage.2020.117254. Epub 2020 Aug 13.
2
Towards an ecologically valid naturalistic cognitive neuroscience of memory and event cognition.迈向记忆和事件认知的生态有效自然主义认知神经科学。
Neuropsychologia. 2024 Oct 10;203:108970. doi: 10.1016/j.neuropsychologia.2024.108970. Epub 2024 Aug 13.
3
Overcoming boundaries: Interdisciplinary challenges and opportunities in cognitive neuroscience.跨越边界:认知神经科学中的跨学科挑战与机遇。
Neuropsychologia. 2024 Jul 29;200:108903. doi: 10.1016/j.neuropsychologia.2024.108903. Epub 2024 May 13.
4
The convergence of naturalistic paradigms and cognitive neuroscience methods to investigate memory and its development.自然主义范式和认知神经科学方法的融合,用于研究记忆及其发展。
Neuropsychologia. 2024 Apr 15;196:108779. doi: 10.1016/j.neuropsychologia.2023.108779. Epub 2023 Dec 27.
5
The need for a cognitive neuroscience of naturalistic social cognition.自然主义社会认知的认知神经科学之必要性。
Ann N Y Acad Sci. 2009 Jun;1167:16-30. doi: 10.1111/j.1749-6632.2009.04601.x.
6
Is neuroimaging measuring information in the brain?神经成像技术是在测量大脑中的信息吗?
Psychon Bull Rev. 2016 Oct;23(5):1415-1428. doi: 10.3758/s13423-016-1002-0.
7
Cognition of Time and Thinking Beyond.时间认知与超越思维
Adv Exp Med Biol. 2024;1455:171-195. doi: 10.1007/978-3-031-60183-5_10.
8
Understanding Mental Health and Cognitive Restructuring With Ecological Neuroscience.借助生态神经科学理解心理健康与认知重构
Front Psychiatry. 2021 Jun 18;12:697095. doi: 10.3389/fpsyt.2021.697095. eCollection 2021.
9
Improving precision in neuropsychological assessment: Bridging the gap between classic paper-and-pencil tests and paradigms from cognitive neuroscience.提高神经心理学评估的精度:弥合经典纸笔测试与认知神经科学范式之间的差距。
Clin Neuropsychol. 2019 Feb;33(2):357-368. doi: 10.1080/13854046.2018.1518489. Epub 2018 Nov 5.
10
A Naturalistic Setup for Presenting Real People and Live Actions in Experimental Psychology and Cognitive Neuroscience Studies.自然主义设置在实验心理学和认知神经科学研究中呈现真实人物和现场动作。
J Vis Exp. 2023 Aug 4(198). doi: 10.3791/65436.

引用本文的文献

1
Exploring Neural Evidence of Attention in Classroom Environments: A Scoping Review.探索课堂环境中注意力的神经证据:一项范围综述。
Brain Sci. 2025 Aug 13;15(8):860. doi: 10.3390/brainsci15080860.
2
Neural and behavioral reinstatement jointly reflect retrieval of narrative events.神经和行为恢复共同反映叙事事件的检索。
Nat Commun. 2025 Aug 23;16(1):7865. doi: 10.1038/s41467-025-62375-9.
3
Social inference brain networks in autistic adults during movie-viewing: functional specialization and heterogeneity.成年自闭症患者在观看电影时的社会推理脑网络:功能特化与异质性
Mol Autism. 2025 Aug 22;16(1):42. doi: 10.1186/s13229-025-00669-x.
4
Spacetop: A multimodal fMRI dataset unifying naturalistic processes with a rich array of experimental tasks.Spacetop:一个将自然主义过程与丰富的实验任务相结合的多模态功能磁共振成像数据集。
Sci Data. 2025 Aug 22;12(1):1465. doi: 10.1038/s41597-025-05154-x.
5
How much is "enough"? Considerations for functional connectivity reliability in pediatric naturalistic fMRI.多少才算“足够”?儿科自然主义功能磁共振成像中功能连接可靠性的考量
Imaging Neurosci (Camb). 2025 Aug 19;3. doi: 10.1162/IMAG.a.117. eCollection 2025.
6
The neural basis of event segmentation: Stable features in the environment are reflected by neural states.事件分割的神经基础:环境中的稳定特征由神经状态反映出来。
Imaging Neurosci (Camb). 2025 Jan 15;3. doi: 10.1162/imag_a_00432. eCollection 2025.
7
Comparing reliability-based measures of functional connectivity between movie and rest: An ROI-based approach.比较基于可靠性的电影与静息状态下功能连接测量:一种基于感兴趣区域的方法。
Imaging Neurosci (Camb). 2025 Jan 2;3. doi: 10.1162/imag_a_00411. eCollection 2025.
8
Toward a Causal Science of Early Play?迈向早期游戏的因果科学?
Infancy. 2025 Jul-Aug;30(4):e70033. doi: 10.1111/infa.70033.
9
Measuring neurodevelopment of inhibitory control in children using naturalistic virtual reality.使用自然主义虚拟现实测量儿童抑制控制的神经发育情况。
Sci Rep. 2025 Jul 24;15(1):26944. doi: 10.1038/s41598-025-10974-3.
10
Reading comprehension in L1 and L2 readers: neurocomputational mechanisms revealed through large language models.第一语言和第二语言阅读者的阅读理解:通过大语言模型揭示的神经计算机制
NPJ Sci Learn. 2025 Jul 10;10(1):46. doi: 10.1038/s41539-025-00337-y.

本文引用的文献

1
The "Narratives" fMRI dataset for evaluating models of naturalistic language comprehension.用于评估自然语言理解模型的“叙事” fMRI 数据集。
Sci Data. 2021 Sep 28;8(1):250. doi: 10.1038/s41597-021-01033-3.
2
Endogenous variation in ventromedial prefrontal cortex state dynamics during naturalistic viewing reflects affective experience.在自然观看过程中,腹内侧前额叶皮层状态动态的内源性变化反映了情感体验。
Sci Adv. 2021 Apr 23;7(17). doi: 10.1126/sciadv.abf7129. Print 2021 Apr.
3
The generalizability crisis.普遍性危机。
Behav Brain Sci. 2020 Dec 21;45:e1. doi: 10.1017/S0140525X20001685.
4
The revolution will not be controlled: natural stimuli in speech neuroscience.这场革命无法被控制:言语神经科学中的自然刺激
Lang Cogn Neurosci. 2018 Jul 22;35(5):573-582. doi: 10.1080/23273798.2018.1499946. eCollection 2020.
5
What Is the Test-Retest Reliability of Common Task-Functional MRI Measures? New Empirical Evidence and a Meta-Analysis.常见任务态功能磁共振测量的重测信度如何?新的实证证据和荟萃分析。
Psychol Sci. 2020 Jul;31(7):792-806. doi: 10.1177/0956797620916786. Epub 2020 Jun 3.
6
Hyperalignment: Modeling shared information encoded in idiosyncratic cortical topographies.超对齐:建模独特皮质拓扑中编码的共享信息。
Elife. 2020 Jun 2;9:e56601. doi: 10.7554/eLife.56601.
7
Narratives for Neuroscience.神经科学叙事
Trends Neurosci. 2020 May;43(5):271-273. doi: 10.1016/j.tins.2020.03.003.
8
Studying the visual brain in its natural rhythm.研究大脑的视觉在其自然节奏中。
Neuroimage. 2020 Aug 1;216:116790. doi: 10.1016/j.neuroimage.2020.116790. Epub 2020 Apr 8.
9
Why Are Self-Report and Behavioral Measures Weakly Correlated?为什么自陈式测量和行为测量相关性较弱?
Trends Cogn Sci. 2020 Apr;24(4):267-269. doi: 10.1016/j.tics.2020.01.007. Epub 2020 Feb 17.
10
Direct Fit to Nature: An Evolutionary Perspective on Biological and Artificial Neural Networks.直接契合自然:生物和人工神经网络的进化视角。
Neuron. 2020 Feb 5;105(3):416-434. doi: 10.1016/j.neuron.2019.12.002.

保持真实:重新思考认知神经科学中实验控制的首要地位。

Keep it real: rethinking the primacy of experimental control in cognitive neuroscience.

机构信息

Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.

Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.

出版信息

Neuroimage. 2020 Nov 15;222:117254. doi: 10.1016/j.neuroimage.2020.117254. Epub 2020 Aug 13.

DOI:10.1016/j.neuroimage.2020.117254
PMID:32800992
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7789034/
Abstract

Naturalistic experimental paradigms in neuroimaging arose from a pressure to test the validity of models we derive from highly-controlled experiments in real-world contexts. In many cases, however, such efforts led to the realization that models developed under particular experimental manipulations failed to capture much variance outside the context of that manipulation. The critique of non-naturalistic experiments is not a recent development; it echoes a persistent and subversive thread in the history of modern psychology. The brain has evolved to guide behavior in a multidimensional world with many interacting variables. The assumption that artificially decoupling and manipulating these variables will lead to a satisfactory understanding of the brain may be untenable. We develop an argument for the primacy of naturalistic paradigms, and point to recent developments in machine learning as an example of the transformative power of relinquishing control. Naturalistic paradigms should not be deployed as an afterthought if we hope to build models of brain and behavior that extend beyond the laboratory into the real world.

摘要

神经影像学中的自然主义实验范式源于一种压力,即需要检验我们从高度受控实验中得出的模型在现实环境中的有效性。然而,在许多情况下,这些努力导致人们意识到,在特定实验操作下开发的模型无法捕捉到操作环境之外的大量变化。对非自然主义实验的批判并不是最近才出现的;它呼应了现代心理学史上一个持续存在且具有颠覆性的线索。大脑是为了在一个具有许多相互作用变量的多维世界中指导行为而进化的。假设人为地分离和操纵这些变量将导致对大脑的满意理解,这可能是站不住脚的。我们提出了一个论点,即自然主义范式的首要地位,并指出机器学习的最新发展是放弃控制的变革力量的一个例子。如果我们希望构建超越实验室进入现实世界的大脑和行为模型,那么自然主义范式就不应该被当作事后的想法。