智能时代人因科学研究的新范式取向及重点

doi:10.3724/SP.J.1041.2024.00363

摘要/Abstract

摘要：

本文首先提出了“人因科学”这一创新的学科群概念来表征工程心理学、人因工程、工效学、人机交互等相近领域。尽管这些领域的研究角度不一样, 但是它们分享共同的研究理念、对象以及目的。我们近期的研究表明, 人工智能(AI)新技术带来了一系列新的人因问题, 而作为人因科学研究对象的人机关系呈现出从“人机交互”向“人智组队式合作”的跨时代演进。这些变化对人因科学研究提出了新问题和新挑战, 需要我们重新审视基于非智能技术的人因科学研究范式和重点。在此背景下, 本文梳理人因科学研究范式取向跨时代的演进, 总结我们近5年所提出的一系列用于丰富人因科学研究范式的新概念模型和框架, 其中包括人智协同认知系统、人智协同认知生态系统以及智能社会技术系统的模型和框架。本文进一步从人因科学研究范式取向的角度进一步提升这些概念模型和框架, 提出智能时代人因科学研究的三种新范式取向, 分析相应的应用意义, 并展望今后的研究方向。同时, 针对智能时代人因科学研究重点的跨时代转移新特征, 本文从“人智交互” “智能人机界面” “人智组队合作”三个方面展望了今后人因科学的研究重点, 揭示出人因科学新研究范式取向对未来研究重点的作用。我们认为, 人因科学的研究范式取向和研究重点互为影响, 互为促进, 智能时代的人因科学研究需要多样化、创新的研究范式取向, 从而进一步推动人因科学的发展。

关键词: 人因科学, 工程心理学, 人因工程, 研究范式取向, 人智组队

Abstract:

This paper first proposes the innovative concept of “human factors science” to characterize engineering psychology, human factors engineering, ergonomics, human-computer interaction, and other similar fields. Although the perspectives in these fields differ, they share a common goal: optimizing the human-machine relationship by applying a “human-centered design” approach. AI technology has brought in new characteristics, and our recent research reveals that the human-machine relationship presents a trans-era evolution from “human-machine interaction” to “human-AI teaming.” These changes have raised questions and challenges for human factors science, compelling us to re-examine current research paradigms and agendas. In this context, this paper reviews and discusses the implications of the following three conceptual models and frameworks that we recently proposed to enrich the research paradigms for human factors science. (1) human-AI joint cognitive systems: this model differs from the traditional human-computer interaction paradigm and regards an intelligent system as a cognitive agent with a certain level of cognitive capabilities. Thus, a human-AI system can be characterized as a joint cognitive system in which two cognitive agents (human and intelligent agents) work as teammates for collaboration. (2) human-AI joint cognitive ecosystems: an intelligent ecosystem with multiple human-AI systems can be represented as a human-AI joint cognitive ecosystem. The overall system performance of the intelligent ecosystem depends on optimal collaboration and design across the multiple human-AI systems. (3) intelligent sociotechnical systems (iSTS): human-AI systems are designed, developed, and deployed in an iSTS environment. From a macro perspective, iSTS focuses on the interdependency between the technical and social subsystems. The successful design, development, and deployment of a human-AI system within an iSTS environment depends on the synergistic optimization between the two subsystems. This paper further enhances these frameworks from the research paradigm perspective. We propose three new research paradigms for human factors science in the intelligence ear: human-AI joint cognitive systems, human-AI joint cognitive ecosystems, and intelligent sociotechnical systems, enabling comprehensive human factors solutions for AI-based intelligent systems. Further analyses show that the three new research paradigms will benefit future research in human factors science. Furthermore, this paper looks forward to the future research agenda of human factors science from three aspects: “human-AI interaction,” “intelligent human-machine interface,” and “human-AI teaming.” We believe the proposed research paradigms and the future research agenda will mutually promote each other, further advancing human factors science in the intelligence era.

Key words: Human factors science, engineering psychology, human factors engineering, research paradigm, human- AI teaming

中图分类号:

B849

许为, 高在峰, 葛列众. (2024). 智能时代人因科学研究的新范式取向及重点. 心理学报, 56(3), 363-382.

XU Wei, GAO Zaifeng, GE Liezhong. (2024). New research paradigms and agenda of human factors science in the intelligence era. Acta Psychologica Sinica, 56(3), 363-382.

图/表 10

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产生时代	研究范式取向	范式取向描述	代表性领域或框架	代表性方法

计算机、智能时代	基于人类认知神经活动	在神经层面了解人机环境中认知加工的神经机制与工作绩效之间的关系	神经人因学	脑机接口技术和设计, 脑电测量、特征分析和建模
计算机时代	基于人类认知信息加工活动	从人类心理活动(感知、记忆、认知负荷等)层面了解人机环境中认知加工与工作绩效间的关系, 优化人机系统设计	工程心理学	在人机操作环境中, 采用工作绩效测量(反应时、错误率等)和主观评价方法来评价人类心理活动与绩效间的关系、人机系统设计的有效性
机械时代	基于人机能力差异性和互补性	人与机器能力的差异化及互补性, 优化人机功能和任务分配, 人适应于机器	早期的工效学、人因工程	人类体力作业分析, 时间动作分析, 人机功能和任务分析及分配等
计算机时代	基于机器作为辅助人类作业(工具)的人机交互	通过人机交互技术、设计、测试和实现达到机器适应于人、人机交互优化、最佳用户体验	人机交互	用户心理模型和需求研究及分析、人机交互认知建模和界面概念化、基于心理学方法的可用性测试
智能时代	基于人和智能体两个认知体之间的协同合作(单一人机系统)	机器智能体成为与人类合作的团队队友, 人与智能体是协同认知系统中的两个认知体, 通过组队合作达到最佳整体系统绩效	基于人智组队合作的人智协同认知系统	基于人−人团队合作、协同认知系统等理论, 建模人机双向式情景意识、心理模型、信任、决策, 实现人机协同合作的人机交互技术和设计
智能时代	基于跨人−智系统之间的协同合作(多重协同认知系统)	多智能体系统(协同认知系统)形成智能协同认知生态系统, 整体系统绩效取决于各协同认知系统间的协同合作和优化设设计	人智协同认知生态系统	基于生态系统的建模、设计、技术, 包括跨多智能体系统间的协同合作、多智能体系统间的群体知识迁移、自组织与自适应协同、分布式情景意识、人机交互和协同决策等
智能时代	基于社会和智能技术子系统间的协同合作	通过实现人、组织、社会、技术子系统之间的相互作用和协同优化, 达到最佳的整体系统绩效	智能社会技术系统	系统化方法, 社会技术系统方法, 工作系统重新设计, 组织优化设计, 工程、社会行为科学等跨学科方法

产生时代	研究范式取向	范式取向描述	代表性领域或框架	代表性方法

计算机、智能时代	基于人类认知神经活动	在神经层面了解人机环境中认知加工的神经机制与工作绩效之间的关系	神经人因学	脑机接口技术和设计, 脑电测量、特征分析和建模
计算机时代	基于人类认知信息加工活动	从人类心理活动(感知、记忆、认知负荷等)层面了解人机环境中认知加工与工作绩效间的关系, 优化人机系统设计	工程心理学	在人机操作环境中, 采用工作绩效测量(反应时、错误率等)和主观评价方法来评价人类心理活动与绩效间的关系、人机系统设计的有效性
机械时代	基于人机能力差异性和互补性	人与机器能力的差异化及互补性, 优化人机功能和任务分配, 人适应于机器	早期的工效学、人因工程	人类体力作业分析, 时间动作分析, 人机功能和任务分析及分配等
计算机时代	基于机器作为辅助人类作业(工具)的人机交互	通过人机交互技术、设计、测试和实现达到机器适应于人、人机交互优化、最佳用户体验	人机交互	用户心理模型和需求研究及分析、人机交互认知建模和界面概念化、基于心理学方法的可用性测试
智能时代	基于人和智能体两个认知体之间的协同合作(单一人机系统)	机器智能体成为与人类合作的团队队友, 人与智能体是协同认知系统中的两个认知体, 通过组队合作达到最佳整体系统绩效	基于人智组队合作的人智协同认知系统	基于人−人团队合作、协同认知系统等理论, 建模人机双向式情景意识、心理模型、信任、决策, 实现人机协同合作的人机交互技术和设计
智能时代	基于跨人−智系统之间的协同合作(多重协同认知系统)	多智能体系统(协同认知系统)形成智能协同认知生态系统, 整体系统绩效取决于各协同认知系统间的协同合作和优化设设计	人智协同认知生态系统	基于生态系统的建模、设计、技术, 包括跨多智能体系统间的协同合作、多智能体系统间的群体知识迁移、自组织与自适应协同、分布式情景意识、人机交互和协同决策等
智能时代	基于社会和智能技术子系统间的协同合作	通过实现人、组织、社会、技术子系统之间的相互作用和协同优化, 达到最佳的整体系统绩效	智能社会技术系统	系统化方法, 社会技术系统方法, 工作系统重新设计, 组织优化设计, 工程、社会行为科学等跨学科方法

跨时代转移的新特征	智能技术的人因新问题	人因科学研究重点(部分)
从“可预期”到“潜在不可预期”的机器行为	$\cdot$智能系统可带来不确定机器行为、独特的机器行为演变, 导致系统输出偏差(Rahwan et al., 2019) $\cdot$现有软件测试方法缺乏对智能机器行为的考虑 $\cdot$智能系统的行为拥有进化、社会交互等特征	$\cdot$研究机器行为的行为科学方法 $\cdot$在数据收集、训练和算法测试中减小系统输出偏差的迭代式设计和用户测试方法(Amershi et al., 2014) $\cdot$用户参与式设计、“以人为中心”式机器学习(Kaluarachchi et al., 2021)
从“人机交互”到“人智组队合作”	$\cdot$机器(智能体)从工具到与人类合作的队友 $\cdot$人−智如何合作 $\cdot$如何建模人机协作(人机共信、共享式态势感知、心理模型、决策和控制等)	$\cdot$基于人智组队合作范式的理论、方法等 $\cdot$人−AI合作理论、模型、团队绩效评价系统(Bansal et al., 2019)
从“人类智能”到基于“人机智能互补”的人机混合增强智能	$\cdot$机器无法模仿高阶人类认知能力, 孤立地开发机器智能遇到瓶颈效应(Zheng et al., 2017) $\cdot$无法确保将人类角色整合到智能系统, 从而实现人类可控AI (Zanzotto, 2019)	$\cdot$人机混合增强智能的认知架构 $\cdot$基于协同认知生态系统范式的人−多智能体系统协同合作 $\cdot$“人在环中” “人在环上”智能系统及交互设计 $\cdot$人类高阶认知能力模型、知识表征和图谱
从“以人为中心自动化”到“人类可控自主化”	$\cdot$人类可能失去对智能自主系统的最终控制权 $\cdot$自主化技术潜在的负面影响(不确定性等) (Kaber, 2018) $\cdot$对自动化与自主化概念的混淆会导致低估自主化技术潜在的负面影响	$\cdot$针对自主化技术的人机交互设计范式 $\cdot$人类可控自主化的人因科学方法 $\cdot$人机共享的自主化控制设计 $\cdot$人−自主化交互
从“非智能”到“智能”人机交互”	$\cdot$如何提升智能用户界面的自然性 $\cdot$如何有效设计智能人机交互(Google PAIR, 2019) $\cdot$普适计算环境中人类感知能力和认知资源的瓶颈效应(王巍等, 2014)	$\cdot$智能人机交互和界面设计新范式 $\cdot$多通道自然式用户界面设计 $\cdot$新型人机交互技术和设计(情感交互、意图识别、脑机接口等) $\cdot$针对智能技术研发的人因科学设计标准
从“体验需求”到“伦理化AI”	$\cdot$用户新需求(隐私、道德、公平、技能发展、决策权等) (IEEE, 2019) $\cdot$智能系统可能的输出偏差和意外结果 $\cdot$滥用智能系统(歧视、隐私等问题) $\cdot$缺乏对智能系统故障的追溯和问责机制	$\cdot$基于人因科学方法的伦理化AI跨学科设计 $\cdot$基于协同认知生态系统范式的方法 $\cdot$基于智能社会技术系统范式的方法 $\cdot$有意义的人类控制(Santoni & van den Hoven, 2018) $\cdot$透明化设计
从“体验式”到“系统化”交互设计	$\cdot$基于用户体验、可用性设计方法的局限性 $\cdot$如何有效开展智能系统原型化设计和可用性测试 $\cdot$人因科学人员没能早期介入智能系统开发	$\cdot$基于人因科学理念的智能系统开发流程 $\cdot$用户体验驱动的智能创新设计 $\cdot$有效的智能交互设计方法(Holmquist, 2017) $\cdot$系统化人因科学方法(Xu et al., 2019)
从“实体物理”到“虚实融合元宇宙”空间的交互	$\cdot$虚实融合空间的人机交互新需求(史元春, 2021) $\cdot$元宇宙空间中人机交互的沉浸感、交互性、平行孪生环境的新体验 $\cdot$元宇宙空间中的多模连续、交互数据模糊为交互意图推理带来的新问题	$\cdot$元宇宙空间的自然人机交互模式和技术 $\cdot$人机交互的虚拟化、远程化和多映射关系 $\cdot$元宇宙交互空间的伦理、信息呈现、脑机融合等 $\cdot$元宇宙交互空间的人−人、人−智间的社会关系

跨时代转移的新特征	智能技术的人因新问题	人因科学研究重点(部分)
从“可预期”到“潜在不可预期”的机器行为	$\cdot$智能系统可带来不确定机器行为、独特的机器行为演变, 导致系统输出偏差(Rahwan et al., 2019) $\cdot$现有软件测试方法缺乏对智能机器行为的考虑 $\cdot$智能系统的行为拥有进化、社会交互等特征	$\cdot$研究机器行为的行为科学方法 $\cdot$在数据收集、训练和算法测试中减小系统输出偏差的迭代式设计和用户测试方法(Amershi et al., 2014) $\cdot$用户参与式设计、“以人为中心”式机器学习(Kaluarachchi et al., 2021)
从“人机交互”到“人智组队合作”	$\cdot$机器(智能体)从工具到与人类合作的队友 $\cdot$人−智如何合作 $\cdot$如何建模人机协作(人机共信、共享式态势感知、心理模型、决策和控制等)	$\cdot$基于人智组队合作范式的理论、方法等 $\cdot$人−AI合作理论、模型、团队绩效评价系统(Bansal et al., 2019)
从“人类智能”到基于“人机智能互补”的人机混合增强智能	$\cdot$机器无法模仿高阶人类认知能力, 孤立地开发机器智能遇到瓶颈效应(Zheng et al., 2017) $\cdot$无法确保将人类角色整合到智能系统, 从而实现人类可控AI (Zanzotto, 2019)	$\cdot$人机混合增强智能的认知架构 $\cdot$基于协同认知生态系统范式的人−多智能体系统协同合作 $\cdot$“人在环中” “人在环上”智能系统及交互设计 $\cdot$人类高阶认知能力模型、知识表征和图谱
从“以人为中心自动化”到“人类可控自主化”	$\cdot$人类可能失去对智能自主系统的最终控制权 $\cdot$自主化技术潜在的负面影响(不确定性等) (Kaber, 2018) $\cdot$对自动化与自主化概念的混淆会导致低估自主化技术潜在的负面影响	$\cdot$针对自主化技术的人机交互设计范式 $\cdot$人类可控自主化的人因科学方法 $\cdot$人机共享的自主化控制设计 $\cdot$人−自主化交互
从“非智能”到“智能”人机交互”	$\cdot$如何提升智能用户界面的自然性 $\cdot$如何有效设计智能人机交互(Google PAIR, 2019) $\cdot$普适计算环境中人类感知能力和认知资源的瓶颈效应(王巍等, 2014)	$\cdot$智能人机交互和界面设计新范式 $\cdot$多通道自然式用户界面设计 $\cdot$新型人机交互技术和设计(情感交互、意图识别、脑机接口等) $\cdot$针对智能技术研发的人因科学设计标准
从“体验需求”到“伦理化AI”	$\cdot$用户新需求(隐私、道德、公平、技能发展、决策权等) (IEEE, 2019) $\cdot$智能系统可能的输出偏差和意外结果 $\cdot$滥用智能系统(歧视、隐私等问题) $\cdot$缺乏对智能系统故障的追溯和问责机制	$\cdot$基于人因科学方法的伦理化AI跨学科设计 $\cdot$基于协同认知生态系统范式的方法 $\cdot$基于智能社会技术系统范式的方法 $\cdot$有意义的人类控制(Santoni & van den Hoven, 2018) $\cdot$透明化设计
从“体验式”到“系统化”交互设计	$\cdot$基于用户体验、可用性设计方法的局限性 $\cdot$如何有效开展智能系统原型化设计和可用性测试 $\cdot$人因科学人员没能早期介入智能系统开发	$\cdot$基于人因科学理念的智能系统开发流程 $\cdot$用户体验驱动的智能创新设计 $\cdot$有效的智能交互设计方法(Holmquist, 2017) $\cdot$系统化人因科学方法(Xu et al., 2019)
从“实体物理”到“虚实融合元宇宙”空间的交互	$\cdot$虚实融合空间的人机交互新需求(史元春, 2021) $\cdot$元宇宙空间中人机交互的沉浸感、交互性、平行孪生环境的新体验 $\cdot$元宇宙空间中的多模连续、交互数据模糊为交互意图推理带来的新问题	$\cdot$元宇宙空间的自然人机交互模式和技术 $\cdot$人机交互的虚拟化、远程化和多映射关系 $\cdot$元宇宙交互空间的伦理、信息呈现、脑机融合等 $\cdot$元宇宙交互空间的人−人、人−智间的社会关系

跨时代转移的新特征	智能技术的人因新问题	人因科学研究重点
从“单向式”到“人机合作双向式”界面	$\cdot$系统不再被动地接受用户输入, 并根据固定规则做出预期输出 $\cdot$智能体可主动感知来捕获和理解用户生理、认知、情感、意图等状态, 主动启动人机交互和推送服务	$\cdot$基于人−智机组队合作范式的人机交互模型 $\cdot$用户情景意识、生理、认知、情感、意图状态的认知模型
从“可用性”到“可解释AI”界面	$\cdot$AI“黑匣子”效应可导致不可解释和不可理解的系统输出(Muelle et al., 2019) $\cdot$AI“黑匣子”效应引发AI信任问题	$\cdot$创新的人机界面技术(如可视化)和设计 $\cdot$“以人为中心”的可解释和可理解AI (Ehsan et al., 2021) $\cdot$心理学解释理论的转化(Mueller et al., 2019)
从“简单属性”到“情境化”交互界面	$\cdot$系统输入除了简单人、机、物等感知属性(如目标位置)以外, 还有“情境化”输入目标(如使用场景上下文、用户行为数据)	$\cdot$基于操作场景上下文、用户行为等数据, 对“情景化”特征建模、智能推演(如用户行为体征、消费行为画像) $\cdot$适合用户需求和使用场景的个性化设计
从“精准输入式”到“模糊推理式”交互界面	$\cdot$用户输入不仅仅是单一精准形式(如键盘、鼠标), 还可是多模态、模糊交互(如用户意图) $\cdot$应用场景中的模糊交互问题(如随机交互信号和环境噪声)	$\cdot$从不确定性条件下推理出用户交互意图的方法和模型(易鑫等, 2018) $\cdot$模糊状态下人机交互的自然性和有效性
从“交互式”到“合作式”认知界面	$\cdot$用户界面既要支持人机交互, 也要支持人智组队合作 $\cdot$支持有效人机合作的人机界面	$\cdot$人机合作式认知界面的设计范式和模型 $\cdot$基于智能人机交互的界面设计标准 $\cdot$有效支持人机合作的交互设计(如应急场景中的人机控制交接)