Human Factors最新文献

Objective: This experiment examined performance costs when processing two sources of information positioned at increasing distances using a flat panel display and an augmented reality head-mounted display (AR-HMD).

Background: The AR-HMD enables positioning virtual information at various distances in space. However, the proximity compatibility principle suggests that closer separation when two sources of information require mental integration assists performance, whereas increased separation between two sources hurts integration performance more than when a single source requires focused attention. Previous studies have provided inconsistent findings regarding costs associated with increased separation. Few of these experiments have examined separation for both focused and integration tasks, compared vertical and lateral separation, or measured head movements.

Method: Three experiments collectively examined these issues using a flat panel display and a virtual display presented with an HMD, where the separation of information varied laterally or vertically during a focused attention (digit reading) task and an information integration (mental subtraction) task.

Results: There was no performance cost for either display when information was increasingly separated. However, head movements mitigated performance costs by preserving accuracy at larger separations without increasing response time.

Conclusion: Head movements appear to mitigate performance costs associated with presenting information increasingly far apart on flat panel displays and HMDs. Both eye scanning and head movements appear to be less effortful than expected.

Application: These findings have important implications for design guidelines regarding the placement of information presented on flat panel displays and, more specifically, HMDs, which can present information 360° around the user.

Objective: To examine the extent to which increased automation transparency can mitigate the potential negative effects of low and high automation reliability on disuse and misuse of automated advice, and perceived trust in automation.

Background: Automated decision aids that vary in the reliability of their advice are increasingly used in workplaces. Low-reliability automation can increase disuse of automated advice, while high-reliability automation can increase misuse. These effects could be reduced if the rationale underlying automated advice is made more transparent.

Methods: Participants selected the optimal UV to complete missions. The Recommender (automated decision aid) assisted participants by providing advice; however, it was not always reliable. Participants determined whether the Recommender provided accurate information and whether to accept or reject advice. The level of automation transparency (medium, high) and reliability (low: 65%, high: 90%) were manipulated between-subjects.

Results: With high- compared to low-reliability automation, participants made more accurate (correctly accepted advice and identified whether information was accurate/inaccurate) and faster decisions, and reported increased trust in automation. Increased transparency led to more accurate and faster decisions, lower subjective workload, and higher usability ratings. It also eliminated the increased automation disuse associated with low-reliability automation. However, transparency did not mitigate the misuse associated with high-reliability automation.

Conclusion: Transparency protected against low-reliability automation disuse, but not against the increased misuse potentially associated with the reduced monitoring and verification of high-reliability automation.

Application: These outcomes can inform the design of transparent automation to improve human-automation teaming under conditions of varied automation reliability.

Objective: The objective of our research is to advance the understanding of behavioral responses to a system's error. By examining trust as a dynamic variable and drawing from attribution theory, we explain the underlying mechanism and suggest how terminology can be used to mitigate the so-called algorithm aversion. In this way, we show that the use of different terms may shape consumers' perceptions and provide guidance on how these differences can be mitigated.

Background: Previous research has interchangeably used various terms to refer to a system and results regarding trust in systems have been ambiguous.

Methods: Across three studies, we examine the effect of different system terminology on consumer behavior following a system failure.

Results: Our results show that terminology crucially affects user behavior. Describing a system as "AI" (i.e., self-learning and perceived as more complex) instead of as "algorithmic" (i.e., a less complex rule-based system) leads to more favorable behavioral responses by users when a system error occurs.

Conclusion: We suggest that in cases when a system's characteristics do not allow for it to be called "AI," users should be provided with an explanation of why the system's error occurred, and task complexity should be pointed out. We highlight the importance of terminology, as this can unintentionally impact the robustness and replicability of research findings.

Application: This research offers insights for industries utilizing AI and algorithmic systems, highlighting how strategic terminology use can shape user trust and response to errors, thereby enhancing system acceptance.

Objective: This study developed a fixation-related electroencephalography band power (FRBP) approach for situation awareness (SA) assessment in automated driving.

Background: Maintaining good SA in Level 3 automated vehicles is crucial to drivers' takeover performance when the automated system fails. A multimodal fusion approach that enables the analysis of the visual behavioral and cognitive processes of SA can facilitate real-time assessment of SA in future driver state monitoring systems.

Method: Thirty participants performed three simulated automated driving tasks. After each task, the Situation Awareness Global Assessment Technique (SAGAT) was deployed to capture their SA about key elements that could affect their takeover task performance. Participants eye movements and brain activities were recorded. Data on their brain activity after each eye fixation on the key elements were extracted and labeled according to the correctness of the SAGAT. Mixed-effects models were used to identify brain regions that were indicative of SA, and machine learning models for SA assessment were developed based on the identified brain regions.

Results: Participants' alpha and theta oscillation at frontal and temporal areas are indicative of SA. In addition, the FRBP technique can be used to predict drivers' SA with an accuracy of 88% using a neural network model.

Conclusion: The FRBP technique, which incorporates eye movements and brain activities, can provide more comprehensive evaluation of SA. Findings highlight the potential of utilizing FRBP to monitor drivers' SA in real-time.

Application: The proposed framework can be expanded and applied to driver state monitoring systems to measure human SA in real-world driving.

Objective: This work examined the relationship of the constructs measured by the trust scales developed by Chancey et al. (2017) and Jian et al. (2000) using a multilevel confirmatory factor analysis (CFA).

Background: Modern theories of automation trust have been proposed based on data collected using trust scales. Chancey et al. (2017) adapted Madsen and Gregor's (2000) trust scale to align with Lee and See's (2004) human-automation trust framework. In contrast, Jian et al. (2000) developed a scale empirically with trust and distrust as factors. However, it remains unclear whether these two scales measure the same construct.

Method: We analyzed data collected from previous experiments to investigate the relationship between the two trust scales using a multilevel CFA.

Results: Data provided evidence that Jian et al. (2000) and Chancey et al. (2017) automation trust scales are only weakly related. Trust and distrust are found to be distinct factors in Jian et al.'s (2000) scale, whereas performance, process, and purpose are distinct factors in Chancey et al.'s (2017) trust scale.

Conclusion: The analysis suggested that the two scales purporting to measure human-automation trust are only weakly related.

Application: Trust researchers and automation designers may consider using Chancey et al. (2017) and Jian et al. (2000) scales to capture different characteristics of human-automation trust.

Objective: This review surveys the literature on sensorimotor challenges impacting performance in laparoscopic minimally invasive surgery (MIS).

Background: Despite its well-known benefits for patients, achieving proficiency in MIS can be challenging for surgeons due to many factors including altered visual perspectives and fulcrum effects in instrument handling. Research on these and other sensorimotor challenges has been hindered by imprecise terminology and the lack of a unified theoretical framework to guide research questions in the field.

Method: We conducted a systematic survey of the MIS literature, focusing on studies investigating sensorimotor challenges affecting laparoscopic performance. To provide a common foundation for cross-study comparisons, we propose a standardized taxonomy that distinguishes between different experimental paradigms used in the literature. We then show how the computational motor learning perspective provides a unifying theoretical framework for the field that can facilitate progress and motivate future research along clearer, hypothesis-driven lines.

Results: The survey identified diverse sensorimotor perturbations in MIS, which can be effectively categorized according to our proposed taxonomy. Studies investigating monitor-, camera-, and tool-based perturbations were systematically analyzed, elucidating their impact on surgical performance. We also show how the computational motor learning perspective provides deeper insights and potential strategies to mitigate challenges.

Conclusion: Sensorimotor challenges significantly impact MIS, necessitating a systematic, empirically informed approach. Our proposed taxonomy and theoretical framework shed light on the complexities involved, paving the way for more structured research and targeted training approaches to enhance surgical proficiency.

Application: Understanding the sensorimotor challenges inherent to MIS can guide the design of improved training curricula and inform the configuration of setups in the operating room to enhance surgeon performance and ultimately patient outcomes. This review offers key insights for surgeons, educators, and researchers in surgical performance and technology development.

Objective: To investigate how the visual complexity of head-up displays (HUDs) influence the allocation of driver's attention in two separate visual domains (near and far domains).

Background: The types and amount of information displayed on automobile HUDs have increased. With limited human attention capacity, increased visual complexity in the near domain may lead to interference in the effective processing of information in the far domain.

Method: Near-domain and far-domain vision were separately tested using a dual-task paradigm. In a simulated road environment, 62 participants were to control the speed of the vehicle (SMT; near domain) and manually respond to probes (PDT; far domain) simultaneously. Five HUD complexity levels including a HUD-absent condition were presented block-wise.

Results: Near domain performance was not modulated by the HUD complexity levels. However, the far domain detection accuracies were impaired as the HUD complexity level increased, with greater accuracy differences observed between central and peripheral probes.

Conclusion: Increased HUD visual complexity leads to a biased deployment of driver attention toward the central visual field. Therefore, the formulation of HUD designs must be preceded by an in-depth investigation of the dynamics of human cognition.

Application: To ensure driving safety, HUD designs should be rendered with minimal visual complexity by incorporating only essential information relevant to driving and removing driving-irrelevant or additional visual details.

Background: Human performers often recruit environment-based assistance to acquire or process information, such as relying on a smartphone app, a search engine, or a conversational agent. To make informed choices between several of such extended cognitive strategies, performers need to monitor the performance of these options.

Objective: In the present study, we investigated whether participants monitor an extended cognitive strategy's performance-here, speed-more closely during initial as compared to later encounters.

Methods: In three experiments, 737 participants were asked to first observe speed differences between two competing cognitive strategies-here, two competing algorithms that can obtain answers to trivia questions-and eventually choose between both strategies based on the observations.

Results: Participants were sensitive to subtle speed differences and selected strategies accordingly. Most remarkably, even when participants performed identically with both strategies across all encounters, the strategy with superior speed in the initial encounters was preferred. Worded differently, participants exhibited a technology-use primacy effect. Contrarily, evidence for a recency effect was weak at best.

Conclusion: These results suggest that great care is required when performers are first acquainted with novel ways to acquire or process information. Superior initial performance has the potential to desensitize the performer for inferior later performance and thus prohibit optimal choice.

Application: Awareness of primacy enables users and designers of extended cognitive strategies to actively remediate suboptimal behavior originating in early monitoring episodes.

Objective: This study investigated the impact of driving styles of drivers and automated vehicles (AVs) on drivers' perception of automated driving maneuvers and quantified the relationships among drivers' perception of AV maneuvers, driver trust, and acceptance of AVs.

Background: Previous studies on automated driving styles focused on the impact of AV's global driving style on driver's attitude and driving performance. However, research on drivers' perception of automated driving maneuvers at the specific driving style level is still lacking.

Method: Sixteen aggressive drivers and sixteen defensive drivers were recruited to experience twelve driving scenarios in either an aggressive AV or a defensive AV on the driving simulator. Their perception of AV maneuvers, trust, and acceptance was measured via questionnaires, and driving performance was collected via the driving simulator.

Results: Results revealed that drivers' trust and acceptance of AVs would decrease significantly if they perceived AVs to have a higher speed, larger deceleration, smaller deceleration, or shorter stopping distance than expected. Moreover, defensive drivers perceived significantly greater inappropriateness of these maneuvers from aggressive AVs than defensive AVs, whereas aggressive drivers didn't differ significantly in their perceived inappropriateness of these maneuvers with different driving styles.

Conclusion: The driving styles of automated vehicles and drivers influenced drivers' perception of automated driving maneuvers, which influence their trust and acceptance of AVs.

Application: This study suggested that the design of AVs should consider drivers' perceptions of automated driving maneuvers to avoid undermining drivers' trust and acceptance of AVs.

Objectives: To investigate the word recognition effects of the use of all-uppercase (e.g., VALENCIA) or titled-case (e.g., Valencia) for city names in traffic signs, controlling for word size, and comparing stationary and dynamic viewing situations.

Background: Prior studies provide mixed evidence regarding the effects of word case on the recognition of city names in traffic signs. Moreover, the evidence on the potential impact of visual motion on these effects is scarce.

Method: We carried out an experimental study using simulated traffic signs. The task was to indicate, for each sign, whether it contained a given city name or not (word search task, 50% positive trials). Visual motion of signs was manipulated as a between-participants factor: stationary (the sign was still) versus dynamic (the sign expanded as if the participant was approaching to it). Word case was manipulated as a within-participants factor: all-uppercase versus two titled-case conditions varying in font size: width-matched titled-case and point size-matched titled-case.

Results: In both the stationary and dynamic conditions, all-uppercase resulted in more incorrect responses and slower latencies than width-matched titled-case. When compared to point size-matched titled-case, all-uppercase produced slower correct responses in the stationary condition, whereas faster in the dynamic condition.

Conclusion: Other factors being equal, all-uppercase city names will be recognized worse than their titled-case versions in traffic signs, both in stationary and dynamic situations.

Application: Results in the current experimental study would be of interest in the design of traffic signs and other circumstances in which text is presented in motion.