Engineering psychology in the era of artificial intelligence

doi:10.3724/SP.J.1042.2020.01409

Abstract

Abstract:

Intelligent technology provides new opportunities for the research and applications of engineering psychology. To this end, a work framework of engineering psychology in the intelligent era is proposed. The framework includes the objectives of engineering psychology research and applications, core problem space, disciplinary philosophy, focus of research and applications, methods and so on. The human-machine relationship in the intelligent era has presented a new form: the human-machine cooperation in the form of human-machine teaming. “Human-centered artificial intelligence” should be the discipline philosophy of engineering psychology in the intelligent era. Aiming at intelligent technology, engineering psychology researchers have recently begun to carry out research work on the theoretical framework and basic issues surrounding the new human-machine relationship, mental construct, shared decision-making between human and machine, and the interaction design for intelligent systems. In order to effectively support the research and development of intelligent systems, new and enhanced methods of engineering psychology are summarized. Finally, specific suggestions are given for addressing the challenges faced by engineering psychology.

Key words: Engineering psychology, human factors, human-machine teaming, intelligent systems, autonomy

CLC Number:

B849

XU Wei, GE Liezhong. Engineering psychology in the era of artificial intelligence[J]. Advances in Psychological Science, 2020, 28(9): 1409-1425.

Figures/Tables 7

References 95

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	第一次浪潮(上世纪50~70年代)	第二次浪潮(上世纪80~90年代)	第三次浪潮(2006年~ )
主要技术和方法	早期“符号主义和联结主义”学派, 产生式系统, 知识推理, 专家系统	统计模型在语音识别、机器翻译的研究, 神经网络的初步应用, 专家系统	深度学习技术在语音识别、数据挖掘、自然语言处理、模式识别等方面的突破, 大数据, 计算力等
用户需求	无法满足	无法满足	开始提供有用的、解决实际问题的AI应用解决方案
工作重点	技术探索	技术提升	技术提升, 应用落地场景, 伦理化设计, 前端应用, 人机交互技术等
阶段特征	学术主导	学术主导	技术提升+应用+以人为中心

	第一次浪潮(上世纪50~70年代)	第二次浪潮(上世纪80~90年代)	第三次浪潮(2006年~ )
主要技术和方法	早期“符号主义和联结主义”学派, 产生式系统, 知识推理, 专家系统	统计模型在语音识别、机器翻译的研究, 神经网络的初步应用, 专家系统	深度学习技术在语音识别、数据挖掘、自然语言处理、模式识别等方面的突破, 大数据, 计算力等
用户需求	无法满足	无法满足	开始提供有用的、解决实际问题的AI应用解决方案
工作重点	技术探索	技术提升	技术提升, 应用落地场景, 伦理化设计, 前端应用, 人机交互技术等
阶段特征	学术主导	学术主导	技术提升+应用+以人为中心

工程心理学特征	自动化	半自主化(针对特定场景、任务)	全自主化
	实例: 一般办公软件, 自动化生产线, 自动化飞机驾驶舱	实例: 智能音箱, 智能决策系统, 自动驾驶车(L2及以上)	实例: 科幻电影《终结者》中的Skynet机器人
感应环境的能力	比较有限	先进的多模态感应	更先进的多模态感应
认知能力(知觉整合、模式识别、学习、推理、决策等)	没有	有部分	有 (包括自主设定目标、调整策略、资源分配等)
执行操作的能力	人工激活操作, 根据预定不变的规则执行操作	人工激活操作, 独立执行操作	自主激活操作、独立执行操作等
对不可预测环境的自适应能力	没有	有部分	有
系统操作结果	具确定性	具不确定性	具不确定性
系统运行中对人工操作的需求	需要(特别是设计无法预料的操作场景, 非正常、应急状态)	需要(设计无法预料的操作场景,非正常、应急状态)	一般不需要(人应是系统最终决策者)

工程心理学特征	自动化	半自主化(针对特定场景、任务)	全自主化
	实例: 一般办公软件, 自动化生产线, 自动化飞机驾驶舱	实例: 智能音箱, 智能决策系统, 自动驾驶车(L2及以上)	实例: 科幻电影《终结者》中的Skynet机器人
感应环境的能力	比较有限	先进的多模态感应	更先进的多模态感应
认知能力(知觉整合、模式识别、学习、推理、决策等)	没有	有部分	有 (包括自主设定目标、调整策略、资源分配等)
执行操作的能力	人工激活操作, 根据预定不变的规则执行操作	人工激活操作, 独立执行操作	自主激活操作、独立执行操作等
对不可预测环境的自适应能力	没有	有部分	有
系统操作结果	具确定性	具不确定性	具不确定性
系统运行中对人工操作的需求	需要(特别是设计无法预料的操作场景, 非正常、应急状态)	需要(设计无法预料的操作场景,非正常、应急状态)	一般不需要(人应是系统最终决策者)

工程心理学特征	人机交互	人机组队
主动性	只有人主动地启动任务、行动, 机器被动接受	人机双方均可主动地启动任务和行动
方向性	只有人对机器的单向信任、情景意识、决策	人机双向的信任、情景意识、意图, 分享的决策控制权(人应拥有最终控制权)
互补性	人与机之间无智能互补	机器智能(模式识别、推理等能力)与人的生物智能(人的信息加工等能力)之间的互补, 优化智能系统设计
预测性	只有人类操作员拥有这些特征	人机双方借助行为、情景意识等模型, 预测对方行为、环境和系统的状态
自适应性	只有人类操作员拥有这些特征	人机双向适应对方以及操作场景的行为
目标性	只有人类操作员拥有这些特征	人机双向均可设置或调整目标
替换性	机器借助于自动化等技术主要替换人的体力任务	机器可以替换人的认知、体力任务(人机双向可主动或被动地接管、委派任务)
合作性	有限的人机合作	更大范围的人机合作