逻辑复杂性的电分析:逻辑有效/无效演绎推理的探索性脑电图研究。

Q1 Computer Science
Francisco Salto, Carmen Requena, Paula Alvarez-Merino, Víctor Rodríguez, Jesús Poza, Roberto Hornero
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引用次数: 1

摘要

简介:逻辑上有效的演绎论证是命题或概率上抽象递归计算过程的清晰例子。然而,目前尚不清楚大脑中最终实现逻辑论证的皮层耗时推理过程是否与其他类型的推理过程在物理上有所不同。方法:为了确定是否存在电脑电图可识别的逻辑推理模式,提出了一种新的实验范式,对比具有完全相同内容(相同前提和相同关系变量)和不同逻辑复杂性(命题真-函数算子)的逻辑有效推理和无效推理。采用两工况模式(每工况100次试验)获取19例(24.2±3.3岁)受试者的脑电图信号。在最初的一般分析之后,在β -2波段的一次又一次的试验方法不仅可以发现诱发的,而且可以发现试验之间的相异步活动。结果表明:(1)具有相同内容的演绎推理在逻辑有效和逻辑无效条件下引起的反应模式相同;(2)逻辑有效推理的平均反应时间比逻辑有效推理高61.54%;(3)逻辑有效推理经历了早期(400 ms)和后期(600 ms)的再加工,并被两个不同的β -2激活验证(p值)。结果提出了一些逻辑上有效的推理是递归或计算皮层事件的假设。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Electrical analysis of logical complexity: an exploratory eeg study of logically valid/invalid deducive inference.

Electrical analysis of logical complexity: an exploratory eeg study of logically valid/invalid deducive inference.

Electrical analysis of logical complexity: an exploratory eeg study of logically valid/invalid deducive inference.

Electrical analysis of logical complexity: an exploratory eeg study of logically valid/invalid deducive inference.

Introduction: Logically valid deductive arguments are clear examples of abstract recursive computational procedures on propositions or on probabilities. However, it is not known if the cortical time-consuming inferential processes in which logical arguments are eventually realized in the brain are in fact physically different from other kinds of inferential processes.

Methods: In order to determine whether an electrical EEG discernible pattern of logical deduction exists or not, a new experimental paradigm is proposed contrasting logically valid and invalid inferences with exactly the same content (same premises and same relational variables) and distinct logical complexity (propositional truth-functional operators). Electroencephalographic signals from 19 subjects (24.2 ± 3.3 years) were acquired in a two-condition paradigm (100 trials for each condition). After the initial general analysis, a trial-by-trial approach in beta-2 band allowed to uncover not only evoked but also phase asynchronous activity between trials.

Results: showed that (i) deductive inferences with the same content evoked the same response pattern in logically valid and invalid conditions, (ii) mean response time in logically valid inferences is 61.54% higher, (iii) logically valid inferences are subjected to an early (400 ms) and a late reprocessing (600 ms) verified by two distinct beta-2 activations (p-value < 0,01, Wilcoxon signed rank test).

Conclusion: We found evidence of a subtle but measurable electrical trait of logical validity. Results put forward the hypothesis that some logically valid deductions are recursive or computational cortical events.

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来源期刊
Brain Informatics
Brain Informatics Computer Science-Computer Science Applications
CiteScore
9.50
自引率
0.00%
发文量
27
审稿时长
13 weeks
期刊介绍: Brain Informatics is an international, peer-reviewed, interdisciplinary open-access journal published under the brand SpringerOpen, which provides a unique platform for researchers and practitioners to disseminate original research on computational and informatics technologies related to brain. This journal addresses the computational, cognitive, physiological, biological, physical, ecological and social perspectives of brain informatics. It also welcomes emerging information technologies and advanced neuro-imaging technologies, such as big data analytics and interactive knowledge discovery related to various large-scale brain studies and their applications. This journal will publish high-quality original research papers, brief reports and critical reviews in all theoretical, technological, clinical and interdisciplinary studies that make up the field of brain informatics and its applications in brain-machine intelligence, brain-inspired intelligent systems, mental health and brain disorders, etc. The scope of papers includes the following five tracks: Track 1: Cognitive and Computational Foundations of Brain Science Track 2: Human Information Processing Systems Track 3: Brain Big Data Analytics, Curation and Management Track 4: Informatics Paradigms for Brain and Mental Health Research Track 5: Brain-Machine Intelligence and Brain-Inspired Computing
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