Human Factors最新文献

{"title":"Effectiveness of Safe Patient Handling Equipment and Techniques: A Review of Biomechanical Studies.","authors":"Mike Fray, Kermit G Davis","doi":"10.1177/00187208231211842","DOIUrl":"10.1177/00187208231211842","url":null,"abstract":"<p><strong>Objective: </strong>This review aimed to evaluate all studies that have evaluated the biomechanical effects when using assistive devices.</p><p><strong>Introduction: </strong>The physical demands of patient handling activities are well known. One safety strategy for the reduction of the physical risks is use of assistive devices.</p><p><strong>Method: </strong>The search process identified articles published in English-speaking journals through Google Scholar, Medline, and ISI Web of Science. The included 56 studies contained a biomechanical assessment of a patient handling activity with assistive devices.</p><p><strong>Results: </strong>The biomechanical effects included four groups: changes in body posture (spinal, other joints), subjective assessment (force, effort, discomfort), measured force (hand force, ground reaction force, spine force, joint torque), and physiological measures. The evidence showed caregivers benefited from using lift hoists, air-assisted devices, and to a lesser extent friction reducing devices for lateral transfers and repositioning, while floor and ceiling lifts were most effective for patient transfers. Some gaps were noted in the evidence and other handling tasks such as sit-to-stand, turning patient in bed, limb lifting, and repositioning and some more high hazard activities like supporting people with limited balance and those that fall need to be investigated with respect to biomechanical outcomes.</p><p><strong>Conclusion: </strong>There is a growing level of biomechanical evidence to support the use of assistive devices for many patient-handling tasks, but the benefits of equipment use in some transfers remain uninvestigated.</p><p><strong>Practical application: </strong>Evidence indicates the best way to lift patients safely is with floor or ceiling lifts, and air-assisted devices for lateral and repositioning tasks.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2283-2322"},"PeriodicalIF":2.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11382441/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72016301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Road Users Fail to Appreciate the Special Optical Properties of Retroreflective Materials.","authors":"Savana L King, Ellen C Szubski, Richard A Tyrrell","doi":"10.1177/00187208231210644","DOIUrl":"10.1177/00187208231210644","url":null,"abstract":"<p><strong>Objective: </strong>To determine whether typical road users appreciate the special optical properties of retroreflective materials.</p><p><strong>Background: </strong>Retroreflective surfaces reflect light back towards the source of the illumination. All drivers benefit from retroreflective materials, as they are required on road signs, on large trailers, in lane delineation, and other traffic control devices. Retroreflective markings can also greatly enhance the conspicuity of pedestrians at night, but pedestrians typically underuse retroreflective markings. One possible reason is that pedestrians may not appreciate the special optical properties of retroreflective materials.</p><p><strong>Method: </strong>Two experiments tested whether observers could correctly predict that retroreflective materials appear remarkably bright when illuminated by a source that is aligned with the observers' eyes. Observers used a magnitude estimation procedure to predict how bright retroreflective and non-retroreflective stimuli would appear during a demonstration designed to highlight retroreflectivity. They then judged the brightness again during the demonstration.</p><p><strong>Results: </strong>In general, observers underestimated how bright retroreflective stimuli would be and overestimated how bright diffuse reflective and fluorescent stimuli would be. The underestimates for retroreflective stimuli were particularly striking when the observers had not closely examined the stimuli in advance.</p><p><strong>Conclusion: </strong>The fact that road users do not appreciate retroreflectivity may help explain why pedestrians underuse retroreflective markings at night.</p><p><strong>Application: </strong>Educational interventions could prove useful in this domain.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2409-2427"},"PeriodicalIF":2.9,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71523530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vigilance Decrement During On-Road Partially Automated Driving Across Four Systems.","authors":"Francesco N Biondi, Amy S McDonnell, Mobina Mahmoodzadeh, Noor Jajo, Balakumar Balasingam, David L Strayer","doi":"10.1177/00187208231189658","DOIUrl":"10.1177/00187208231189658","url":null,"abstract":"<p><strong>Objective: </strong>This study uses a detection task to measure changes in driver vigilance when operating four different partially automated systems.</p><p><strong>Background: </strong>Research show temporal declines in detection task performance during manual and fully automated driving, but the accuracy of using this approach for measuring changes in driver vigilance during on-road partially automated driving is yet unproven.</p><p><strong>Method: </strong>Participants drove four different vehicles (Tesla Model 3, Cadillac CT6, Volvo XC90, and Nissan Rogue) equipped with level-2 systems in manual and partially automated modes. Response times to a detection task were recorded over eight consecutive time periods.</p><p><strong>Results: </strong>Bayesian analysis revealed a main effect of time period and an interaction between mode and time period. A main effect of vehicle and a time period x vehicle interaction were also found.</p><p><strong>Conclusion: </strong>Results indicated that the reduction in detection task performance over time was worse during partially automated driving. Vehicle-specific analysis also revealed that detection task performance changed across vehicles, with slowest response time found for the Volvo.</p><p><strong>Application: </strong>The greater decline in detection performance found in automated mode suggests that operating level-2 systems incurred in a greater vigilance decrement, a phenomenon that is of interest for Human Factors practitioners and regulators. We also argue that the observed vehicle-related differences are attributable to the unique design of their in-vehicle interfaces.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2179-2190"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10235149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Do Drivers Perceive Risks During Automated Driving Scenarios? An fNIRS Neuroimaging Study.","authors":"Jaume Perello-March, Christopher G Burns, Roger Woodman, Stewart Birrell, Mark T Elliott","doi":"10.1177/00187208231185705","DOIUrl":"10.1177/00187208231185705","url":null,"abstract":"<p><strong>Objective: </strong>Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving.</p><p><strong>Background: </strong>Although well-established during manual driving, the effects of driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers' states posits it could become a novel method for measuring risk perception.</p><p><strong>Methods: </strong>Twenty-three volunteers participated in an empirical driving simulator experiment with automated driving capability. Driving conditions involved suburban and urban scenarios with varying levels of traffic complexity, culminating in an unexpected hazardous event. Perceived risk was measured via fNIRS within the prefrontal cortical haemoglobin oxygenation and from self-reports.</p><p><strong>Results: </strong>Prefrontal cortical haemoglobin oxygenation levels significantly increased, following self-reported perceived risk and traffic complexity, particularly during the hazardous scenario.</p><p><strong>Conclusion: </strong>This paper has demonstrated that fNIRS is a valuable research tool for measuring variations in perceived risk from traffic complexity during highly automated driving. Even though the responsibility over the driving task is delegated to the automated system and dispositional trust is high, drivers perceive moderate risk when traffic complexity builds up gradually, reflected in a corresponding significant increase in blood oxygenation levels, with both subjective (self-reports) and objective (fNIRS) increasing further during the hazardous scenario.</p><p><strong>Application: </strong>Little is known regarding the effects of drivers' risk perception with automated driving. Building upon our experimental findings, future work can use fNIRS to investigate the mental processes for risk assessment and the effects of perceived risk on driving behaviours to promote the safe adoption of automated driving technology.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2244-2263"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344369/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9677120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploratory Development of Algorithms for Determining Driver Attention Status.","authors":"Eileen Herbers, Marty Miller, Luke Neurauter, Jacob Walters, Daniel Glaser","doi":"10.1177/00187208231198932","DOIUrl":"10.1177/00187208231198932","url":null,"abstract":"<p><strong>Objective: </strong>Varying driver distraction algorithms were developed using vehicle kinematics and driver gaze data obtained from a camera-based driver monitoring system (DMS).</p><p><strong>Background: </strong>Distracted driving characteristics can be difficult to accurately detect due to wide variation in driver behavior across driving environments. The growing availability of information about drivers and their involvement in the driving task increases the opportunity for accurately recognizing attention state.</p><p><strong>Method: </strong>A baseline for driver distraction levels was developed using a video feed of 24 separate drivers in varying naturalistic driving conditions. This initial assessment was used to develop four buffer-based algorithms that aimed to determine a driver's real-time attentiveness, via a variety of metrics and combinations thereof.</p><p><strong>Results: </strong>Of those tested, the optimal algorithm included ungrouped glance locations and speed. Notably, as an algorithm's performance of detecting very distracted drivers improved, its accuracy for correctly identifying attentive drivers decreased.</p><p><strong>Conclusion: </strong>At a minimum, drivers' gaze position and vehicle speed should be included when designing driver distraction algorithms to delineate between glance patterns observed at high and low speeds. Distraction algorithms should be designed with an understanding of their limitations, including instances in which they may fail to detect distracted drivers, or falsely notify attentive drivers.</p><p><strong>Application: </strong>This research adds to the body of knowledge related to driver distraction and contributes to available methods to potentially address and reduce occurrences. Machine learning algorithms can build on the data elements discussed to increase distraction detection accuracy using robust artificial intelligence.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2191-2204"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41152711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Driving Aggressively or Conservatively? Investigating the Effects of Automated Vehicle Interaction Type and Road Event on Drivers' Trust and Preferred Driving Style.","authors":"Yuni Lee, Miaomiao Dong, Vidya Krishnamoorthy, Kumar Akash, Teruhisa Misu, Zhaobo Zheng, Gaojian Huang","doi":"10.1177/00187208231181199","DOIUrl":"10.1177/00187208231181199","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to investigate the impact of automated vehicle (AV) interaction mode on drivers' trust and preferred driving styles in response to pedestrian- and traffic-related road events.</p><p><strong>Background: </strong>The rising popularity of AVs highlights the need for a deeper understanding of the factors that influence trust in AV. Trust is a crucial element, particularly because current AVs are only partially automated and may require manual takeover; miscalibrated trust could have an adverse effect on safe driver-vehicle interaction. However, before attempting to calibrate trust, it is vital to comprehend the factors that contribute to trust in automation.</p><p><strong>Methods: </strong>Thirty-six individuals participated in the experiment. Driving scenarios incorporated adaptive SAE Level 2 AV algorithms, driven by participants' event-based trust in AVs and preferences for AV driving styles. The study measured participants' trust, preferences, and the number of takeover behaviors.</p><p><strong>Results: </strong>Higher levels of trust and preference for more aggressive AV driving styles were found in response to pedestrian-related events compared to traffic-related events. Furthermore, drivers preferred the trust-based adaptive mode and had fewer takeover behaviors than the preference-based adaptive and fixed modes. Lastly, participants with higher trust in AVs favored more aggressive driving styles and made fewer takeover attempts.</p><p><strong>Conclusion: </strong>Adaptive AV interaction modes that depend on real-time event-based trust and event types may represent a promising approach to human-automation interaction in vehicles.</p><p><strong>Application: </strong>Findings from this study can support future driver- and situation-aware AVs that can adapt their behavior for improved driver-vehicle interaction.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2166-2178"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9591470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"European NCAP Driver State Monitoring Protocols: Prevalence of Distraction in Naturalistic Driving.","authors":"Megan Mulhall, Kyle Wilson, Shiyan Yang, Jonny Kuo, Tracey Sletten, Clare Anderson, Mark E Howard, Shantha Rajaratnam, Michelle Magee, Allison Collins, Michael G Lenné","doi":"10.1177/00187208231194543","DOIUrl":"10.1177/00187208231194543","url":null,"abstract":"<p><strong>Objective: </strong>examine the prevalence of driver distraction in naturalistic driving when implementing European New Car Assessment Program (Euro NCAP)-defined distraction behaviours.</p><p><strong>Background: </strong>The 2023 introduction of Occupant Status monitoring (OSM) into Euro NCAP will accelerate uptake of Driver State Monitoring (DSM). Euro NCAP outlines distraction behaviours that DSM must detect to earn maximum safety points. Distraction behaviour prevalence and driver alerting and intervention frequency have yet to be examined in naturalistic driving.</p><p><strong>Method: </strong>Twenty healthcare workers were provided with an instrumented vehicle for approximately two weeks. Data were continuously monitored with automotive grade DSM during daily work commutes, resulting in 168.8 hours of driver head, eye and gaze tracking.</p><p><strong>Results: </strong>Single long distraction events were the most prevalent, with .89 events/hour. Implementing different thresholds for driving-related and driving-unrelated glance regions impacts alerting rates. Lizard glances (primarily gaze movement) occurred more frequently than owl glances (primarily head movement). Visual time-sharing events occurred at a rate of .21 events/hour.</p><p><strong>Conclusion: </strong>Euro NCAP-described driver distraction occurs naturalistically. Lizard glances, requiring gaze tracking, occurred in high frequency relative to owl glances, which only require head tracking, indicating that less sophisticated DSM will miss a substantial amount of distraction events.</p><p><strong>Application: </strong>This work informs OEMs, DSM manufacturers and regulators of the expected alerting rate of Euro NCAP defined distraction behaviours. Alerting rates will vary with protocol implementation, technology capability, and HMI strategies adopted by the OEMs, in turn impacting safety outcomes, user experience and acceptance of DSM technology.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2205-2217"},"PeriodicalIF":2.9,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10088980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"This Is Your Brain on Autopilot 2.0: The Influence of Practice on Driver Workload and Engagement During On-Road, Partially Automated Driving.","authors":"Amy S McDonnell, Kaedyn W Crabtree, Joel M Cooper, David L Strayer","doi":"10.1177/00187208231201054","DOIUrl":"10.1177/00187208231201054","url":null,"abstract":"<p><strong>Objective: </strong>This on-road study employed behavioral and neurophysiological measurement techniques to assess the influence of six weeks of practice driving a Level 2 partially automated vehicle on driver workload and engagement.</p><p><strong>Background: </strong>Level 2 partial automation requires a driver to maintain supervisory control of the vehicle to detect \"edge cases\" that the automation is not equipped to handle. There is mixed evidence regarding whether drivers can do so effectively. There is also an open question regarding how practice and familiarity with automation influence driver cognitive states over time.</p><p><strong>Method: </strong>Behavioral and neurophysiological measures of driver workload and visual engagement were recorded from 30 participants at two testing sessions-with a six-week familiarization period in-between. At both testing sessions, participants drove a vehicle with partial automation engaged (Level 2) and not engaged (Level 0) on two interstate highways while reaction times to the detection response task (DRT) and neurophysiological (EEG) metrics of frontal theta and parietal alpha were recorded.</p><p><strong>Results: </strong>DRT results demonstrated that partially automated driving placed more cognitive load on drivers than manual driving and six weeks of practice decreased driver workload-though only when the driving environment was relatively simple. EEG metrics of frontal theta and parietal alpha showed null effects of partial automation.</p><p><strong>Conclusion: </strong>Driver workload was influenced by level of automation, specific highway characteristics, and by practice over time, but only on a behavioral level and not on a neural level.</p><p><strong>Application: </strong>These findings expand our understanding of the influence of practice on driver cognitive states under Level 2 partial automation.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2025-2040"},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11141086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41162897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The (Im)perfect Automation Schema: Who Is Trusted More, Automated or Human Decision Support?","authors":"Tobias Rieger, Luisa Kugler, Dietrich Manzey, Eileen Roesler","doi":"10.1177/00187208231197347","DOIUrl":"10.1177/00187208231197347","url":null,"abstract":"<p><strong>Objective: </strong>This study's purpose was to better understand the dynamics of trust attitude and behavior in human-agent interaction.</p><p><strong>Background: </strong>Whereas past research provided evidence for a perfect automation schema, more recent research has provided contradictory evidence.</p><p><strong>Method: </strong>To disentangle these conflicting findings, we conducted an online experiment using a simulated medical X-ray task. We manipulated the framing of support agents (i.e., artificial intelligence (AI) versus expert versus novice) between-subjects and failure experience (i.e., perfect support, imperfect support, back-to-perfect support) within subjects. Trust attitude and behavior as well as perceived reliability served as dependent variables.</p><p><strong>Results: </strong>Trust attitude and perceived reliability were higher for the human expert than for the AI than for the human novice. Moreover, the results showed the typical pattern of trust formation, dissolution, and restoration for trust attitude and behavior as well as perceived reliability. Forgiveness after failure experience did not differ between agents.</p><p><strong>Conclusion: </strong>The results strongly imply the existence of an imperfect automation schema. This illustrates the need to consider agent expertise for human-agent interaction.</p><p><strong>Application: </strong>When replacing human experts with AI as support agents, the challenge of lower trust attitude towards the novel agent might arise.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"1995-2007"},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10131069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Visual-Vestibular Model to Predict Motion Sickness for Linear and Angular Motion.","authors":"Daniel Sousa Schulman, Nishant Jalgaonkar, Sneha Ojha, Ana Rivero Valles, Monica L H Jones, Shorya Awtar","doi":"10.1177/00187208231200721","DOIUrl":"10.1177/00187208231200721","url":null,"abstract":"<p><strong>Objective: </strong>This study proposed a model to predict passenger motion sickness under the presence of a visual-vestibular conflict and assessed its performance with respect to previously recorded experimental data.</p><p><strong>Background: </strong>While several models have been shown useful to predict motion sickness under repetitive motion, improvements are still desired in terms of predicting motion sickness in realistic driving conditions. There remains a need for a model that considers angular and linear visual-vestibular motion inputs in three dimensions to improve prediction of passenger motion sickness.</p><p><strong>Method: </strong>The model combined the subjective vertical conflict theory and human motion perception models. The proposed model integrates visual and vestibular sensed 6 DoF motion signals in a novel architecture.</p><p><strong>Results: </strong>Model prediction results were compared to motion sickness data obtained from studies conducted in motion simulators as well as on-road vehicle testing, yielding trends that are congruent with observed results in both cases.</p><p><strong>Conclusion: </strong>The model demonstrated the ability to predict trends in motion sickness response for conditions in which a passenger performs a task on a handheld device versus facing forward looking ahead under realistic driving conditions. However, further analysis across a larger population is necessary to better assess the model's performance.</p><p><strong>Application: </strong>The proposed model can be used as a tool to predict motion sickness under different levels of visual-vestibular conflict. This can be leveraged to design interventions capable of mitigating passenger motion sickness. Further, this model can provide insights that aid in the development of passenger experiences inside autonomous vehicles.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"2120-2137"},"PeriodicalIF":2.9,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10571712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}