亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Application of Functional Near-Infrared Spectroscopy to Measure Engineering Decision-Making and Design Cognition: Literature Review and Synthesis of Methods

功能近红外光谱 计算机科学 认知 任务(项目管理) 人工智能 人机交互 系统工程 工程类 心理学 神经科学 前额叶皮质
作者
Mo Hu,Tripp Shealy
出处
期刊:Journal of Computing in Civil Engineering [American Society of Civil Engineers]
卷期号:33 (6) 被引量:35
标识
DOI:10.1061/(asce)cp.1943-5487.0000848
摘要

New and disruptive building technologies will require new and disruptive ways of thinking about decision-making and design in engineering. The emergence of a novel neuroimaging technique, called functional near-infrared spectroscopy (fNIRS), provides a new approach to quantify engineering cognition. To introduce fNIRS, a systematic review was conducted to provide an overview of methods and findings. Researchers interested in measuring decision-making during infrastructure finance negotiations, coordination among stakeholders, or interaction between the built environment and human cognition will benefit from this synthesis. The review includes 32 experiments, and the mean sample size of human participants was 28. Three methods for experimental design include block, event-related, and mixed. Out of these three, block design was used in over half of the experiments. Most studies adopted band-pass or low-pass filters to remove noise and process fNIRS raw data. The most frequently used data-analysis technique to compare variables was segmenting changes in oxy-hemoglobin into different condition periods (e.g., baseline or task) or blocks (e.g., Task A or Task B) and measuring mean values, peak amplitudes, or area under the curve from different brain regions over a specific time period. However, more sophisticated statistical techniques such as General Linear Model, brain network, and interpersonal neural synchronization provide a richer explanation of cognition. This review not only introduces fNIRS as a radically new approach to study cognition in engineering but offers a guide for designing future studies. These results can be used to perform power analyses, develop hypotheses, and more quickly narrow the brain regions of interest in future empirical studies.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
4秒前
claud完成签到 ,获得积分0
8秒前
33发布了新的文献求助10
10秒前
11秒前
14秒前
LJX完成签到,获得积分10
15秒前
数据女工发布了新的文献求助10
15秒前
今夕何夕完成签到,获得积分10
15秒前
16秒前
17秒前
19秒前
Lze发布了新的文献求助10
20秒前
sunzy发布了新的文献求助10
21秒前
哭泣若剑发布了新的文献求助10
22秒前
今夕何夕发布了新的文献求助30
22秒前
24秒前
28秒前
一粟完成签到 ,获得积分10
28秒前
烧鸭饭完成签到,获得积分10
29秒前
寒梦难敌完成签到,获得积分10
31秒前
鲍安琪发布了新的文献求助10
33秒前
Ava应助lww采纳,获得10
33秒前
ding应助lww采纳,获得10
33秒前
33秒前
李健的小迷弟应助lww采纳,获得10
33秒前
华仔应助lww采纳,获得10
34秒前
SciGPT应助lww采纳,获得10
34秒前
科研通AI6.3应助lww采纳,获得10
34秒前
完美世界应助lww采纳,获得10
34秒前
思源应助lww采纳,获得10
34秒前
科研通AI6.4应助lww采纳,获得10
35秒前
科研通AI6.4应助lww采纳,获得10
35秒前
35秒前
可靠的绿凝完成签到 ,获得积分10
36秒前
哭泣若剑完成签到,获得积分10
37秒前
JamesPei应助科研通管家采纳,获得10
37秒前
共享精神应助科研通管家采纳,获得10
37秒前
小马甲应助科研通管家采纳,获得10
38秒前
WML发布了新的文献求助10
40秒前
高分求助中
Markov Chain Monte Carlo 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Common Foundations of American and East Asian Modernisation: From Alexander Hamilton to Junichero Koizumi 5000
Advanced Weaponeering Fourth Edition, Volume 2 1000
Weaponeering: An Introduction Fourth Edition, Volume 1 1000
悉尼大学博士学位论文,题目:Modelling and testing of one-sided stitched laminated composites. 作者:Kristopher P. Plain 700
Matrix Methods in Data Mining and Pattern Recognition Second Edition 610
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7556428
求助须知:如何正确求助?哪些是违规求助? 9138757
关于积分的说明 19533588
捐赠科研通 7147141
什么是DOI,文献DOI怎么找? 3261177
关于科研通互助平台的介绍 2427667
邀请新用户注册赠送积分活动 2250336