HUMAN FACTORS人因
HUMAN FACTORS(英文缩写 HUM FACTORS),ISSN 0018-7208,eISSN 1547-8181,中文译名:人因 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-11-01 至 2026-10-31，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 3.598 | Q2 |
| 2022 | 3.300 | Q2 |
| 2023 | 2.900 | Q1 |
| 2024 | 3.300 | Q1 |
| 2025 | 3.600 | Q1 |
HUMAN FACTORS 最新收录文献
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1. Haptic Shared Planning and Control: Enabling Coordination of Future Actions in Human-Autonomous Vehicle Teams Through a Haptic Negotiation Interface.
PMID:日期:2026-09-24ObjectiveWe examine how human-automation shared steering control combined with a haptic shape-changing interface for communicating and negotiating future intent affects driving performance and trust recovery.BackgroundSteering fights in haptic shared control can negatively impact driving performance. Existing solutions do not feature automation explicitly communicating its intent nor supporting intent negotiation with the driver. Trust recovery after faults is also expected to improve with explicit intent communication.MethodThirty participants took part in a continuous driving task consisting of 39 turns at intersections in which the pre-planned turn direction either matched or differed. Haptic communication of intent between driver and automation was metered under three conditions: From automation to driver (One-Way), in both directions (Two-Way), or not available (No-Way). Road obstacles were present at certain intersections to test handling of contingencies and faults. Automation faults were restricted to a second experimental period, leaving a third period to examine trust before and after a period of faults.ResultsDrivers in Two-Way communication displayed significantly improved driving performance when turn intents did not match. After the period of automation faults, drivers in Two-Way communication displayed significantly better overall trust recovery than drivers in One-Way or No-Way communication.ConclusionNegotiation is a viable means to avoid driver overrides and leads to improved trust recovery. Negotiation through a haptic interface should be considered an effective strategy to ensure function and cohesion in human-automation teams.ApplicationHuman-autonomy planning and team building.
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2. Human-Centered AI for Older Adults.
2. 以老年人为中心的人工智能PMID:日期:2026-09-24ObjectiveTo identify the needs of older adults when interacting with artificial intelligence (AI) technologies, and to propose design recommendations that can inform the development of future AI systems.BackgroundAI technologies are increasingly integrated into healthcare, education, and social applications for older adults. However, many AI systems fail to account for age-related cognitive, perceptual, and emotional changes, resulting in usability barriers, low trust, and limited adoption.MethodA systematic review of 93 studies categorized by application area identified challenges and user needs through thematic analysis, leading to proposed design recommendations. Two AI-powered platforms were then benchmarked against these design recommendations to assess their suitability for older adults.ResultsFindings highlighted older adults' concerns related to usability, personalization, privacy, and explainability of these technologies. Older adults preferred systems that respect human autonomy, adapt to user context and emotions, and offer clear, trustworthy explanations. A set of actionable design recommendations was proposed to improve AI-powered technologies for older adults. Analysis of two selected AI platforms suggested that these systems only partially aligned with the proposed recommendations.ConclusionThis study offers a set of design recommendations that can support the development of AI tools attuned to older adults' needs.ApplicationThis review identifies critical design gaps and offers actionable design recommendations. Findings can inform future participatory, interdisciplinary efforts in age-inclusive AI development.
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3. A New Look at the Relationship Between Individual Differences in Cognitive Abilities and Automation-Assisted Performance.
3. 认知能力个体差异与自动化辅助绩效关系的新视角PMID:日期:2026-09-23ObjectiveThis study explored whether and which cognitive abilities are supported by various levels of automation.BackgroundParasuraman's (2000) model of automation provided guidance for designing automation to augment cognitive tasks. Subsequent mixed empirical findings and evolving understanding of attention control in complex tasks suggested an evaluation of how automation supports cognition within a task was needed.MethodParticipants completed cognitive ability tests then a targeting task either manually or with information automation or decision automation.ResultsDecision automation supported working memory only when reliable, while attention control predicted performance regardless of automation level.ConclusionResults suggest automation can support specific processes like working memory, while attention control demands may persist across automation levels. However, because the task involved a relatively constrained form of automation, future research should examine whether these effects hold with more flexible, adaptive systems.ApplicationAutomation should target specific cognitive functions while recognizing that attention control demands may persist. Future research might investigate how other automation designs can better support this foundational ability.
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4. Modeling Driver Adaptation Patterns During Remote Driving.
4. 远程驾驶中驾驶员适应模式建模PMID:日期:2026-09-22ObjectiveTo identify driving maneuvers in remote driving under end-to-end latency and examine operator adaptation through within-session maneuver transitions.BackgroundRemote driving systems enable operators to control highly automated vehicles when systems encounter operational limits. End-to-end latency disrupts adaptation and vehicle control, although operators may adjust driving behavior. Research on within-session adaptation clusters and maneuvers emerging under latency remains limited.MethodThirty-six participants completed nine remote driving simulator sessions, navigating construction zones under end-to-end latencies (0-640 ms). Hidden Markov Models identified driving maneuvers from vehicle control data (speed, steering, throttle). Dynamic time warping and K-means clustering classified drives by maneuver-transition sequences. Chi-square tests and ANOVAs examined associations among adaptation clusters, participant-specific differences, experimental conditions, and human-factors measures, while participant-clustered factorial analysis examined maneuver composition.ResultsFour maneuvers (adapted, transitional, overcompensation, and compensatory) and three adaptation clusters (No adaptation, Rapid adaptation, and Gradual adaptation) emerged. Adaptation clusters were associated with participant-specific differences rather than latency conditions or repeated trials. Rapid adaptation drives were associated with lower workload and higher trust than No adaptation drives and showed a trend toward increasing trust, whereas trust significantly declined in No adaptation cluster.ConclusionRemote driving operators exhibit adaptation clusters characterized by maneuver-transition sequences. Transitional maneuvers reveal intermediate stages. Cluster distributions were more strongly associated with participant-specific differences than tested experimental conditions, with adaptation patterns differing in trust and workload.ApplicationTraining programs should assess adaptation capabilities and provide interventions rather than uniform protocols. Early cluster identification enables training on maneuver transitions that facilitate progression toward adaptation.
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5. The Unseen Guide: Visual Salience Affects Product Recognition Memory.
5. 无形引导：视觉显著性影响产品识别记忆PMID:日期:2026-09-21ObjectiveComputationally defined visual salience can predict attentional deployment in complex, e-commerce displays but less is known about the effect of salience on later cognitive processes. For instance, can salience also predict later memory for e-commerce products? If so, visual salience could be leveraged to inform design.BackgroundOnline shopping environments provide a wealth of visual information, with numerous products competing for attention. When consumers visit e-commerce pages, they may not have a specific purchase goal. Understanding attentional guidance in these complex displays is critical. Visual salience may influence what products consumers notice and later remember.MethodsHigh- and low-salience products were identified in naturalistic e-commerce webpages using a computational model. During the experiment, participants viewed the webpages and were told their memory for all products would be tested. Recognition memory for individual products was then tested.ResultsParticipants demonstrated better recognition memory for products that had high visual salience, compared to low, within their studied e-commerce page. These memory results mirror the relationship that exists between overt attention and visual salience and extend them to show a persisting effect of salience on information processing.ConclusionVisual salience enhances product recognition memory in naturalistic e-commerce pages. A computational salience model can, therefore, be useful in predicting subsequent memory.ApplicationStudy results offer insights for e-commerce designers. A computational salience model can be leveraged to improve product visibility and memorability. The model is particularly useful for quantifying salience in complex displays where images vary across feature dimensions.
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6. Collaborative Decision-Making in Human-AI Versus Human-Human Teams: The Influence of Information Inquiry and Team Performance Expectations on Team Processes and Outcomes.
6. 人机团队与人际团队中的协作决策：信息询问和团队绩效期望对团队过程和结果的影响PMID:日期:2026-09-08ObjectiveWe investigate whether decision-making processes in human-AI teams mirror those in human-only teams in a task where team members hold common and unique information. Specifically, we examine how information inquiry and performance expectations influence these processes.BackgroundHuman-AI decision-making has traditionally been studied as a linear, segmented process where AI provides recommendations and humans make final decisions. However, recent advancements in large language models (LLMs) enable increasingly dynamic interactions similar to the collaborative processes in human-only teams, where members have to share and integrate unique information to reach decisions. Despite the potential for similar dynamics in human-AI teams, research on such configurations-particularly on when unique information is shared-remains limited.MethodWe conducted an experiment using a hidden profile task, a well-established paradigm in team research. We manipulated team type ( = 98; = 65, three-member teams) and information inquiry, and created conditions with differing performance expectations.ResultsInformation inquiry enhanced information sharing and, consequently, improved decision-making performance, irrespective of performance expectations and team type. Exploratory analyses suggested that while this effect was consistent across team types, information inquiry patterns may differ.ConclusionOur findings prompt avenues for future research on how humans' communication norms vary when interacting with AI versus human team members. Methodologically, this study demonstrates how established decision-making paradigms can be adapted to incorporate AI, offering insights into both human-only and human-AI team processes.ApplicationThe results have practical implications for designing AI systems that promote effective team decision-making by engaging in information inquiry.
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7. Cognitive Workload Assessment in Procedural Tasks.
7. 程序性任务中的认知负荷评估PMID:日期:2026-09-02ObjectiveTo examine how task anomalies and time pressure influence cognitive workload in procedural tasks and to identify whether workload effects are transient or sustained.BackgroundProcedural tasks often involve sequential actions for which disruptions, such as operational anomalies or time pressure, may exceed operator capacity. Understanding the temporal dynamics of workload under these conditions is essential for designing adaptive systems to effectively support human performance.MethodThirty participants performed three cooking tasks under nominal and off-nominal conditions (trials with induced operational errors and time pressure). Workload was continuously assessed using subjective ratings and physiological measures including skin conductance level (SCL), percent change in pupil size (PCPS), and blink rate (BR).ResultsSubjective workload was consistently greater in off-nominal conditions. Physiological measures varied in sensitivity to the task condition and type of task: BR reliably indicated elevated task demands, PCPS was sensitive only under multiple operational anomalies, and SCL showed limited responsiveness to all factors. Two distinct workload patterns were observed: isolated operational anomalies led to transient workload spikes, while multiple accumulating anomalies produced sustained high-workload states, reflecting a "workload history" effect.ConclusionCognitive workload responses depend on both the type and accumulation of task disruptions. BR provides a robust real-time marker, while subjective ratings capture perceived effort.ApplicationFindings inform the design of adaptive support systems capable of distinguishing between momentary operational challenges and cumulative strain, enabling tailored operator assistance in procedural and safety-critical domains.
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8. Elicitation and Assessment of Hand-Based Gestures for Information Processing in Immersive Environments.
8. 沉浸式环境中基于手势的信息处理手势的引出与评估PMID:日期:2026-08-28ObjectiveThis study aims to investigate how information processing tasks can be performed by hand-based gestures in immersive environments and to identify usable gesture designs.BackgroundWith advances in display and interaction technologies, immersive environments have evolved to support productivity-oriented activities, in which information processing plays a critical role. However, performing such tasks in immersive environments remains challenging and calls for intuitive interaction techniques aligned with users' cognitive processes. Gestures offer a promising approach by allowing users to externalize and manipulate information through bodily actions.MethodsUsing 19 cognitive processes outlined in the revised Bloom's taxonomy as representative information processing tasks, a gesture elicitation study with 15 participants explored how these tasks could be mapped to hand-based gestures (Study I). The resulting candidate gestures were subsequently validated and evaluated with 20 participants using a combination of objective and subjective measures (Study II).ResultsStudy I produced an initial set of 32 candidate gestures, which were validated and evaluated in Study II. Four recurring mapping strategies were identified: linguistic-symbolic, spatial-manipulative, metaphoric, and social-conventional mappings. A consolidation model was then introduced to identify a recommended set of 20 gestures based on their performance across multi-dimensional measures.ConclusionsInformation processing tasks can be translated into usable hand-based gestures. Future gesture design should treat information as an interactive entity while carefully considering ergonomics, semantic expressiveness, and gesture similarity to improve usability.ApplicationsThe recommended gestures, mapping strategies, and design considerations can inform the gesture design for immersive systems that involve information processing tasks.
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9. Learning and Engaging With AI: An Exploration of the Effects of Mental Workload and Search Behavior on Short-Term Computer-Based Learning With a Chatbot.
9. 通过与人工智能学习和互动：探索心理负荷和搜索行为对使用聊天机器人进行短期计算机辅助学习的影响PMID:日期:2026-08-19ObjectiveThis study examined how learning, workload and search behaviors were impacted by a chatbot during a self-regulated Web search task, as opposed to more classic search engines.BackgroundArtificial intelligence technologies, including chatbots, are becoming increasingly accessible. These tools have been demonstrated useful to support self-regulated learning, mostly in structured learning contexts. The reduction in workload they offer may, however, prevent key learning strategies from being deployed, especially for Web search.MethodSixty participants were asked to answer a set of essay questions and to rate their workload, effort deployed and literacy while either gathering information from the Internet (Web condition) or by chatting with a chatbot (LLM condition) with the possibility of verifying information on the Web. Several key search strategies and chatbot interaction measures were extracted. A surprise memory test was also presented to evaluate how they learned the content addressed in the essay questions.ResultsMeasures of effort, mental workload, search behaviors and literacy differed significantly across conditions. Performance on the memory test did not vary. Multiple relationships with memory performance, including key Web search and verification behaviors, were found.ConclusionChatbots may help reduce workload and short-term learning with a chatbot may be more influenced by the nature of the interaction with the tool, rather than the tool itself.ApplicationEffective uses of chatbots may require learners to verify the content generated by the chatbot and to show superior engagement. Engagement-promoting learning activities should be considered when using LLM-driven agents to support self-regulated, Web-based search.
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10. Cognition: Effects of Cognitive Workload on Driving Performance: A Comparison of Novice and Experienced Drivers in Underground Coal Mine Transport Vehicles.
PMID:日期:2026-08-18ObjectiveTo examine how cognitive workload influences physiological responses, driving performance, and subjective workload in underground coal mine transport, with comparisons between novice and experienced drivers.BackgroundCognitive workload is a key determinant of safety and performance in high-risk driving environments. However, little is known about how driver experience shapes responses to workload demands in underground coal mine transport operations.MethodsSixty drivers (30 novices, 30 experienced) participated in four experimental conditions (baseline, warning stimulus, distraction, dual-task). Data from 54 participants were included in the final analysis after excluding unusable recordings. Physiological indicators (heart rate, heart rate variability, respiratory rate, and variability), driving performance metrics, and subjective workload (NASA-TLX) were analyzed. Linear mixed models were applied to examine relationships among workload, physiology, and performance.ResultsIncreased workload elevated physiological strain and subjective demand while impairing driving performance. Novices exhibited a linear decline in performance, whereas experienced drivers showed possible nonlinear workload-performance patterns.ConclusionDriver experience moderates the workload-performance relationship. Integrating physiological, performance, and subjective indicators provides a more comprehensive understanding of workload effects in underground driving contexts.ApplicationFindings can guide the development of adaptive driver support systems that dynamically respond to workload fluctuations, enhancing safety, reducing errors, and improving operational efficiency in high-risk mining transport environments.