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使用一种新颖的脑电图机器学习分析方法来测试负量化的两步模型。

Testing two-step models of negative quantification using a novel machine learning analysis of EEG.

作者信息

Ramotowska S, Archambeau K, Augurzky P, Schlotterbeck F, Berberyan H S, Van Maanen L, Szymanik J

机构信息

Institute for Logic, Language and Computation, University of Amsterdam, Amsterdam, The Netherlands.

Université Libre de Bruxelles, Bruxelles, Belgium.

出版信息

Lang Cogn Neurosci. 2024 Apr 30;39(5):632-656. doi: 10.1080/23273798.2024.2345302. eCollection 2024.

DOI:10.1080/23273798.2024.2345302
PMID:39040138
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11261742/
Abstract

The sentences " of the students passed the exam" and " of the students failed the exam" describe the same set of situations, and yet the former results in shorter reaction times in verification tasks. The two-step model explains this result by postulating that negative quantifiers contain hidden negation, which involves an extra processing stage. To test this theory, we applied a novel EEG analysis technique focused on detecting cognitive stages (HsMM-MVPA) to data from a picture-sentence verification task. We estimated the number of processing stages during reading and verification of quantified sentences (e.g. " of the dots are blue") that followed the presentation of pictures containing coloured geometric shapes. We did not find evidence for an extra step during the verification of sentences with . We provide an alternative interpretation of our results in line with an expectation-based pragmatic account.

摘要

“有……比例的学生通过了考试”和“有……比例的学生考试不及格”这两句话描述的是同一组情况,但在验证任务中,前者的反应时间更短。两步模型通过假设否定量词包含隐藏的否定来解释这一结果,这涉及到一个额外的处理阶段。为了验证这一理论,我们将一种专注于检测认知阶段的新型脑电图分析技术(HsMM-MVPA)应用于一个图片-句子验证任务的数据。我们估计了在呈现包含彩色几何形状的图片后,对量化句子(例如“有……比例的点是蓝色的”)进行阅读和验证时的处理阶段数量。我们没有找到在验证包含……的句子时存在额外步骤的证据。我们根据基于预期的语用解释对我们的结果提供了另一种解读。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/b5291a437db6/PLCP_A_2345302_F0007_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/7dacb042b5dc/PLCP_A_2345302_F0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/950c0174f4e5/PLCP_A_2345302_F0002_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/74602bfd0b80/PLCP_A_2345302_F0003_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/e6f2d6b66076/PLCP_A_2345302_F0004_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/77dda707428d/PLCP_A_2345302_F0005_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/dac594fa8e38/PLCP_A_2345302_F0006_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/b5291a437db6/PLCP_A_2345302_F0007_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/7dacb042b5dc/PLCP_A_2345302_F0001_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/950c0174f4e5/PLCP_A_2345302_F0002_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/74602bfd0b80/PLCP_A_2345302_F0003_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/e6f2d6b66076/PLCP_A_2345302_F0004_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/77dda707428d/PLCP_A_2345302_F0005_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/dac594fa8e38/PLCP_A_2345302_F0006_OC.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc12/11261742/b5291a437db6/PLCP_A_2345302_F0007_OC.jpg

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本文引用的文献

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Cognitive processing stages in mental rotation - How can cognitive modelling inform HsMM-EEG models?心理旋转中的认知加工阶段 - 认知建模如何为 HsMM-EEG 模型提供信息?
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2
Uncovering the Structure of Semantic Representations Using a Computational Model of Decision-Making.利用决策的计算模型揭示语义表示的结构。
Cogn Sci. 2023 Jan;47(1):e13234. doi: 10.1111/cogs.13234.
3
Negative sentences exhibit a sustained effect in delayed verification tasks.
否定句在延迟验证任务中表现出持续的效应。
J Exp Psychol Learn Mem Cogn. 2022 Jan;48(1):122-141. doi: 10.1037/xlm0001059.
4
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Brain Cogn. 2021 Oct;153:105786. doi: 10.1016/j.bandc.2021.105786. Epub 2021 Aug 9.
5
EEG-based Identification of Evidence Accumulation Stages in Decision-Making.基于脑电图的决策证据积累阶段识别。
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6
Correlated Brain Indexes of Semantic Prediction and Prediction Error: Brain Localization and Category Specificity.语义预测和预测误差的相关脑指标:大脑定位和类别特异性。
Cereb Cortex. 2021 Feb 5;31(3):1553-1568. doi: 10.1093/cercor/bhaa308.
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Informative use of "not" is N400-blind.“不”的信息性使用是 N400 盲的。
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Brain Inhibitory Mechanisms Are Involved in the Processing of Sentential Negation, Regardless of Its Content. Evidence From EEG Theta and Beta Rhythms.大脑抑制机制参与句子否定的处理,无论其内容如何。来自脑电图θ波和β波节律的证据。
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