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21世纪心理学专业前沿丛书·神经人因学:工作中的脑

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作为一个新兴领域,神经人因学的研究正处于蓬勃发展中。《21世纪心理学专业前沿丛书·神经人因学:工作中的脑》首次将已有成果整合在一起,力图展示对人类脑功能的理解如何给人因学以启发,从而设计安全、有效、愉悦的工作场景。《神经人因学:工作中的脑》展示了神经人因学如何基于现代神经科学和人因心理学与工程学,突破标准的实验室研究,走到自然或仿自然情境中,帮助我们理解日常生活中各种复杂任务下的脑功能和行为。
Author Description

Raja Parasuraman,乔治梅森大学心理学教授,从事人因学和认知神经科学研究。
Matt Rizzo,爱荷华大学神经学、工程学和公共政策教授,神经人因学学部主任,行为神经学和认知神经科学掌部高级会员。他还是一家位于纽约的制作电视纪录片的传媒公司的业务发展经理和科学顾问。
Comments

“对于我们有些在认知心理学领域成长的人来说,一个必然的遗憾就是,我们的同事通常更感兴趣于通过严格控制的实验室研究来完善研究方法和理论概念,而不关心将我们的研究结果应用于现实工作环境。(神经人因学)提出的这些问题,包括脑与现实世界的相互作用.不仅为人因学家们提供了一种不同于传统行为评估、主观报告的研究途径.也增强了基础神经科学的研究。每一位真正关心本领域未来发展的人困学与功效学研究者都应该读一读这本书,它告诉了我们本领域的前景以及为实现这些前景所需要做出的努力。”
——Marvin J.Dainoff,迈阿密大学心理学荣誉教授,人因学研究中心主任

“这是一本不容错过的好书--Parasuraman,Rizzo和他们的同事为我们建立了一个新的概念框架,概括了与这一概念相关的各种理论、方法和实践。我们已经见诳了科技的突飞猛进所带来的迷茫,现代社会似乎对于人类能否充分利用其自身能力的优势,并理解、掌控其在各方面不可避免的风险也已不抱幻想。(神经人因学),我们对人类的认知和感觉加工有吏基础性的理解,重新把我们拉回到了对现代科学进步的积极探索,作为各种科技的集成,这本书存今后的几年定将会广受关注。本书呈现了神经人因学在自动集成系统、“智能机器人”、以及复杂动态人机交互系统中应用的例子,行文流畅,可读性强,堪称经典的参考书,值得各领域各学科专家们一读。”
——Rene Jos.de Pontbriand,美国陆军研究实验室副主任

“人因领域和来自于认知神经科学、脑成像和分子遗传学的新方法、新结果在这本书里碰撞到了一起。本书告诉我们,利学与应用的结合是如何指引我们设计新技术,并让科技更好的服务于人类。”
——Michael Posner,俄勒冈大学荣誉教授,Sackler研究所Weill医学院兼职教授

“Parasuraman和Rizzo让我们以一个全新的方式来思考工作中的人。最初我列“神经人因学”这个名词持着怀疑态度,不过读完这本书,我意识到了从脑出发考察工作这一独特视角的重要性和及时性。在这本书掣,各个领域的专家齐聚一堂,为神经人因学的发展做出了卓越的贡献。
——Thomas B.Sheridan,麻省理工学院航空航天系工程与应用心理学(机械工程)教授
Catalogue

第一篇 引言
第1章 神经人因学概述

第二篇 神经人因学研究方法
第2章 EEG在神经人因学中的应用
第3章 神经人因学中的事件相关电位(ERP)
第4章 功能性磁共振成像--高级方法及其在驾驶研究中的应用
第5章 脑功能光学成像
第6章 经颅多普勒超声
第7章 眼动--探索认知加工过程的窗口
第8章 自然世界中的脑--在自然场景里追踪人类行为

第三篇 知觉、认知和情绪
第9章 空间巡航
第10章 脑血流动力学与警戒
第11章 执行功能
第12章 情绪和情感的神经学及其在决策中的作用

第四篇 应激、疲劳和体力工作
第13章 应激与神经人因学
第14章 神经行为功能的睡眠与昼夜节律控制
第15章 人体神经人因学

第五篇 技术的应用
第16章 自适应自动化
第17章 虚拟现实与神经人因学
第18章 情感唤起能力对于关系机器人的作用
第19章 神经工程学

第六篇 特殊群体问题
第20章 基于EEG的脑机接口
第21章 人工视觉
第22章 神经康复机器人技术与神经假体技术
第23章 医疗安全与神经人因学
第七篇 总结
第24章 神经人因学的展望
术语表
Book Abstract

机器人当前的情感状态(由唤起、效价和立场的净值所定义)在这个空间中占据一个唯一点。当机器人的情感状态改变时,这个点在这个空间中移动,而机器人的面部表情和身体姿势将改变从而与该点相映射。当正的效价增加时,机器人会以更轻快的感觉移动。如果面部表情显示在屏幕上,那么嘴唇会向上弯。而当效价降低时,姿势会下垂,表达一种沉重而消沉的感觉。如果睫毛显示在屏幕上则会皱眉头。沿着唤起的维度,机器人移动得更快,有种更像冲刺的感觉(正性唤起),或者在唤起降低时更多地表现得无力。沿着立场的维度,机器人向用户倾斜(立场变强)或退缩(立场变弱)。当情感状态向情感空间中的极值移动时,表达出来的这些运动就变得更强烈。
计算机可以通过听觉通路表达自己。这些听觉表达在本质上设计得和星球大战中虚构的R2D2机器人相似,不是基于语言,而是用作动作的补充,例如表达惊奇的电子声音。如果任务需要,并不排除使用语音合成,但是我们最初的重点是使用不会外显的唤起人类高级能力的交流模式。
已有初步研究对能够进行物理运动的计算机微小表情的可读性进行了评估(Liu&Picard,2003)。在这个初步研究中,19名被试观看15段计算机的动画片段视频剪辑或者听传达某种表达性行为的音频序列(例如,欢迎、悲伤、好奇、疑惑、惊奇)。这些表达性行为仅仅通过身体活动表达--计算机的LCD屏幕是空的。被试被要求以七点量表评价仅有视频、仅有音频和视频音频结合三种方式下每种表情的强度。双尾,检验显示,对表达好奇、悲伤和惊奇的行为序列的识别均达到了显著。这些初步的研究很鼓舞人心,我们在继续提炼表达性的动作和.声音。5.2知觉系统
我们对于去感知那些在长时间学习任务中起重要作用的情感和注意状态很感兴趣。检测像兴趣、厌倦、疑惑和兴奋之类的情感状态很重要。我们的目标是以一种不为人注意的方式感知这些情感和认知因素。学习者的姿势、手势、眼睛注视和面部表情等线索能帮助专家教师识别出学习者是否正专心于任务。例如,Rich等人(1994)定义了象征性姿势,以表达坐在办公室中使用者行动的特定含义,例如兴趣、厌倦、思考、坐着、放松、防御和自信。向计算机屏幕倾斜可能是注意的表现(专心于任务),而瘫坐在椅子里或坐立不安表明挫折或厌倦(分心)。
我们开发了一套使用自定义模式识别功能的姿势传感系统,观察参与计算机学习任务的孩子们的姿势,并学习他们的姿势运动和兴趣程度之间的关联--高、低或“休息一下”(一种前后转换的状态,有时手在头上伸展,容易在老师意识到孩子厌倦之前频繁发生)。
……

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