Human Factors最新文献

{"title":"Self-Generated Expectations of Hazard Prevalence Affect Virtual Search and Rescue.","authors":"Yan Shan Tai, Jacques A Grange, Robert C Honey","doi":"10.1177/00187208251410492","DOIUrl":"10.1177/00187208251410492","url":null,"abstract":"<p><p>ObjectiveTo understand how prior expectations and instructions about hazard prevalence affect high-stakes visual search in a semi-immersive virtual environment, where participants take on the role of firefighters in search and rescue missions.BackgroundInformation about target prevalence influences visual search in standard laboratory studies. However, little is known about how prior expectations and new information about target prevalence interact in simulated emergency scenarios.MethodsParticipants (<i>n</i> = 48) received training where the average number of hazards (explosive cylinders) amongst similar distractors was varied (two or six) before participants rescued a trapped person. Trial-end feedback indicated whether all targets were removed and the person rescued. They were then instructed that hazard prevalence would increase, decrease, or stay similar during test blocks. Stress was manipulated by an ongoing alarm, the threat of trial-ending explosions, and reduced movement speed. Search performance was measured by the number and type of stimuli removed and stress was assessed using self-report and physiological measures.ResultsAcross high and low stress conditions, more hazards were removed and more false positives occurred (i.e., more distractors removed) when test prevalence was lower than during training, compared to when prevalence levels remained similar. False negative errors were consistently low across conditions.ConclusionAcquired hazard expectations can override explicit instructions, leading to persistent search errors, likely due to difficulties in adjusting decision criteria.ApplicationThese results suggest that training in high-stakes hazard search should incorporate the use of tools and techniques to help mitigate the persistent influence of outdated expectations on search performance.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"580-598"},"PeriodicalIF":3.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13013653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145913897","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":"Toward a Fully Automated NIOSH Lifting Equation Using Computer Vision.","authors":"SeHee Jung, Liwei Qing, Bingyi Su, Xu Xu","doi":"10.1177/00187208261418858","DOIUrl":"10.1177/00187208261418858","url":null,"abstract":"<p><p>ObjectiveTo develop a computer vision-based method for automating the Revised NIOSH Lifting Equation (RNLE) by identifying lift start and end times and estimating key RNLE multipliers from video data.BackgroundThe RNLE is widely used to assess low back injury risk during lifting tasks; however, its traditional application relies on manual measurements that are labor-intensive and prone to human error. Recent advances in computer vision offer opportunities to automate this process.MethodThe proposed method follows a three-stage process: (1) BlazePose, a real-time pose estimation model, detects 22 key body joints, followed by low-pass filtering to reduce noise. (2) Kinematic features are extracted from these joints, and video frames are classified as lifting or nonlifting to identify lifting phases and lift start and end times. (3) RNLE multipliers are estimated at these lift timings to compute the recommended weight limit (RWL).ResultsThe method achieved a mean absolute timing error of 0.25 s for lift timing identification and showed strong correlations between estimated and ground-truth multipliers. The mean absolute error for RWL estimation was 1.58 kg, with a correlation coefficient of 0.91.ConclusionThese results demonstrate the feasibility of using computer vision to automate the RNLE. Further improvements in lift timing identification, depth estimation, and validation in more diverse workplace settings are recommended.ApplicationThe proposed method enables RNLE implementation on widely available platforms such as mobile devices and surveillance cameras, promoting safer lifting practices in real-world workplace environments.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":"68 5","pages":"563-579"},"PeriodicalIF":3.6,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147522878","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":"Efficiency Pitfalls of Explainable AI in Clinical Diagnostic and Treatment Human-AI Workflows.","authors":"Tim Hunsicker, André Schulz, Robert Andreas Leist, Stephan Kiefer, Karl T Boden, Kai Rothaus, Cornelius J König, Markus Langer","doi":"10.1177/00187208261443764","DOIUrl":"10.1177/00187208261443764","url":null,"abstract":"<p><p>ObjectiveTo investigate how AI-provided explanations impact efficiency, diagnostic accuracy, user perceptions, and workflow integration in ophthalmologists' clinical diagnostic and treatment workflows, this study explores the challenges in human-AI interaction with transparency features in time-sensitive environments.BackgroundWhile explainable AI (XAI) aims to foster trust and understanding, its introduction into complex work domains can unintentionally increase cognitive load and disrupt workflows, especially in high-stakes medical settings, potentially impairing system performance.MethodThe multi-phase, mixed-methods study included two parts. Study 1 (<i>N</i> = 32) was a between-subjects experiment in which ophthalmologists diagnosed diabetic retinopathy with AI support, with or without visual explanations (e.g., highlighting lesions). Measures included diagnostic accuracy, diagnostic time, trust, and usefulness. Study 2 (<i>N</i> = 11) employed qualitative methods, including think-aloud protocols and interviews, to explore clinicians' experiences with AI in daily (treatment) workflows.ResultsIn Study 1, explanations did not improve accuracy but increased decision time, reducing efficiency. Trends suggested lower perceived usefulness and trust in the explanation condition. Qualitative data from Study 2 supported these findings; clinicians found explanations time-consuming and disruptive, questioning their practical value, especially for routine cases.ConclusionA critical trade-off exists between pursuing AI transparency and the operational demand for efficiency. Explanations, while well-intentioned, can function as efficiency pitfalls in time-pressured clinical practice, highlighting the boundary conditions and challenges in designing effective human-AI systems.ApplicationThese insights inform future AI system design, favoring adaptable, on-demand explanations tailored to user needs. Such a user-centric approach supports complex cases without impeding routine task efficiency.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"187208261443764"},"PeriodicalIF":3.6,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147730852","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":"How Can I Trust You? The Effect of Risk and Automation Failures on Trust and Reliance Behavior.","authors":"Nikolai Ebinger, Norah Neuhuber, Bettina Kubicek","doi":"10.1177/00187208251398449","DOIUrl":"10.1177/00187208251398449","url":null,"abstract":"<p><p>ObjectiveWe examine how risk and automation failures in conditional driving automation (SAE Level 3) influence drivers' calibration of trust and reliance behavior in the form of system use and monitoring.BackgroundConditionally automated driving brings a challenging new role for drivers, who are permitted to engage in non-driving-related activities but must take back control in certain situations.MethodsParticipants completed three drives in a driving simulation with conditional driving automation. The first drive was with low risk and the second drive was with high risk implemented in the simulation. The third drive included either early or late automation failure.ResultsParticipants reported lower trust, took over manual control more often, and monitored more when driving under high risk than when driving under low risk. After experiencing an automation failure, trust decreased immediately but fully recovered over time. Driver's monitoring increased and decreased immediately as the failure started and ended. The timing of automation failure did not influence its impact on trust.ConclusionThe results indicate that drivers respond appropriately to risk. Trust develops dynamically in case of an automation failure, but failure timing does not influence this process. From an applied perspective, drivers would benefit from assistance in re-calibrating trust after automation failure.ApplicationBased on our findings, we argue that incorporating drivers' mental model formation process into the feedback loop of trust and reliance behavior calibration could enhance the theoretical understanding of trust calibration.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"409-423"},"PeriodicalIF":3.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12909603/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145662681","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":"Does the Directive to Avoid Low Back Flexion Hinder Physical Performance? Examining Isometric Strength in Postures Adopted During Light Mass Lifting.","authors":"Brendan L Pinto, Tyson A C Beach, Jack P Callaghan","doi":"10.1177/00187208251404836","DOIUrl":"10.1177/00187208251404836","url":null,"abstract":"<p><p>ObjectiveObserve how instruction to avoid rounding the low back while lifting a relatively light mass impacts isometric lifting strength.BackgroundAs opposed to manual materials handling training directives recommending whole-body techniques such as a squat lift, targeting specific body regions such as low back curvature, theoretically affords workers greater flexibility to organize the rest of the body to reduce musculoskeletal loading without reducing physical performance. However, providing these directives during sub-maximal tasks may not prompt prioritization of physical performance as individuals self-organize, eventually making the intervention ineffective.MethodsForty participants (50% female) lifted a crate with and without the instruction to avoid rounding the low back. Postures at the initiation of crate lifting were replicated to test isometric strength.ResultsAt the group-level, instruction decreased low back flexion (<i>p</i> < 0.0001) but did not change strength (<i>p</i> = 0.862). However, high heterogeneity motivated examining individual responses. Thirty-seven participants (92.5% of the sample) exhibited greater than 40% of their flexion range-of-motion during baseline lifting, a threshold below which passive tissue strain is typically minimized. Yet, 22 participants (55%) were unsuccessful in reducing low back flexion below this threshold with instruction. Independent from these postural response groups, 23 maintained (57.5%), 8 increased (20%) and 9 decreased (22.5%) isometric strength.ConclusionOn average, physical performance potential was maintained in response to a low back postural directive. However, personalized movement coaching is needed to ensure the desired response for all.ApplicationManual materials handling training should include personalized movement coaching that considers both musculoskeletal loading and performance.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"504-518"},"PeriodicalIF":3.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12909609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145709535","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":"Multitasking Tug-of-War: Exploring the Impact of Task Modality, Task Load Level, and Task Load Type on Dual-Task Interference in Virtual Reality.","authors":"Mohamad El Iskandarani, Matthew Bolton, Sara Lu Riggs","doi":"10.1177/00187208251396766","DOIUrl":"10.1177/00187208251396766","url":null,"abstract":"<p><p>ObjectiveThe present work investigates how task modality (visual, auditory), task load level (low or high), and task load type (target-distractor similarity, display rate) influence dual-task interference in virtual reality (VR).BackgroundDual-task interference is influenced by various factors including perceptual modality, where tasks that share the same modality may yield larger performance decrements than tasks that do not. Task load (i.e., level and type) can also reduce performance in one or both tasks. However, the interaction between these factors in immersive environments like VR is still unclear.MethodParticipants performed a: (a) visual tracking task, (b) visual/auditory detection task, or (c) both concurrently in two different experiments. In Experiment 1, visual tracking load was manipulated via increasing target-distractor similarity, while detection load was manipulated via increasing display rate. In Experiment 2, detection load was manipulated by increasing target-distractor similarity.ResultsIn Experiment 1, higher detection task loads induced greater dual-task costs (DTC) in the detection task regardless of task modality, whereas tracking task DTC was not influenced by higher task loads. In Experiment 2, higher detection task loads induced greater DTC in the detection task only when it was presented visually.ConclusionThe findings suggest that tasks presented in the same modality may experience greater dual-task interference in one or both tasks depending on the task load level and type.ApplicationThese findings can inform the design of multimodal interfaces in complex multitasking environments like military operations or emergency response where minimizing dual-task interference at varying workloads is crucial.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"519-539"},"PeriodicalIF":3.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145589862","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":"Multimodal Cueing in Attitude Tracking: Predicting Pilot Mental Workload Through Physiological Measurements.","authors":"Gabriele Luzzani, Madeline Fischer, Michael T Morcos, Sara Lo Vecchio, Danilo Demarchi, Giorgio Guglieri, Umberto Saetti","doi":"10.1177/00187208251396386","DOIUrl":"10.1177/00187208251396386","url":null,"abstract":"<p><p>ObjectiveTo investigate the relationship between variations in physiological signals and mental workload (MWL) during the execution of a helicopter roll-attitude compensatory tracking task.BackgroundCurrent perceptual models in human-machine piloting have been focused on visual and vestibular cues, overlooking somatosensory and auditory inputs and their interactions. This creates a knowledge gap in understanding shared perception strategies for piloting in environments with impaired sensory channels or enhanced secondary cues.MethodsFifteen healthy participants performed an attitude-tracking task under eleven cueing modalities combining visual, degraded visual, haptic, and auditory cues. Physiological signals-cardiac activity, respiration, brain activity, skin temperature, and electrodermal activity-were analyzed in relation to self-reported MWL using statistical tests and GLMM (Generalized Linear Mixed Models).ResultsParticipants reported low perceived MWL under good visual conditions, with supplementary auditory and haptic cues helping to reduce MWL with degraded or absent visual input. Physiological signals discriminated between MWL levels and multivariate analysis showed that while combined signals revealed an evident explanation of their variance, individual differences underscored the importance of personalized modeling.ConclusionMWL assessment through physiological signals validated during a helicopter tracking task demonstrated that multimodal cueing in complex scenarios can reduce cognitive load, leading to potential safety risk mitigation.ApplicationThis research has the objective of providing a novel approach for safety enhancement and mitigating risks in rotorcraft operations by integrating visual, auditory, and somatosensory cues with physiological-based MWL assessment.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"424-446"},"PeriodicalIF":3.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145544238","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":"Cybersecurity Risks and Vulnerabilities in Robotic-Assisted Surgery.","authors":"Patrick Fuller, Holden Duffie, Dan Li, Alfredo Carbonell, Nicholas Perkins, Jackie S Cha","doi":"10.1177/00187208251401735","DOIUrl":"10.1177/00187208251401735","url":null,"abstract":"<p><p>ObjectiveThis study identifies cybersecurity vulnerabilities and risks in robotic-assisted surgery (RAS) and proposes a cybersecurity framework and an assessment tool for RAS systems.BackgroundRAS systems are increasingly integrated into networks which raise cybersecurity concerns. These systems can enhance surgical outcomes but are potential cyberattack targets, which can affect clinician care, patient safety, and organizational operations.MethodSurveys and interviews were conducted with stakeholders (clinicians, researchers, cybersecurity professionals, and hospital administrators) to collect perspectives on RAS cybersecurity. Thematic analysis was used to develop an RAS cybersecurity framework. Then, stakeholders contributed to creating an RAS cybersecurity assessment tool using Failure Modes, Effects and Criticality Analysis (FMECA).ResultsSurvey responses (<i>n</i> = 84) revealed that 48.8% of respondents were familiar with RAS cybersecurity. Only 24.6% of clinical respondents were aware of their organization's cybersecurity policy. Interviews (<i>n</i> = 15) identified vulnerabilities such as inadequate training, limited communication between manufacturers and healthcare systems, and gaps in regulations. Failure modes focused on consequences of cyberattacks on RAS systems, with severity assessments related to patient health and technology reliability/integrity completed and outcome actions identified.ConclusionUnderstanding RAS cybersecurity challenges is still in its infancy. Key vulnerabilities include insufficient training, limited data sharing, and external threats. The framework illustrates the interconnectedness of stakeholders, while the FMECA assessment tool addresses current vulnerabilities in RAS systems.ApplicationRAS cybersecurity vulnerability and risks should be carefully considered when integrating systems into healthcare organizations, and the RAS cybersecurity assessment tool can be used by stakeholders to systematically identify and analyze potential cybersecurity failure modes.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"447-469"},"PeriodicalIF":3.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145662699","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":"In AI We Trust? Exploring the Role of Explainable GenAI and Expertise in Education.","authors":"Camille Safarov, Gregory Gadzinski, Stephan Schlögl","doi":"10.1177/00187208251401773","DOIUrl":"10.1177/00187208251401773","url":null,"abstract":"<p><p>ObjectiveWe examine AI trust miscalibration-the discrepancy between an individual's trust in AI and its actual performance-among university students. We assess how the length of explanations and students' expertise shape the likelihood of alignment with AI recommendations.BackgroundThe relationship between explainability and users' trust in AI systems has been scarcely addressed in the current literature, even though AI-assisted processes increasingly affect all professions and hierarchical levels. Given that human-AI relationships are often formed during education, it is crucial to understand how individual and contextual factors influence students' assessment of AI outputs.MethodWe conducted in-class experiments with 248 students from multiple universities. Participants solved GMAT questions, then viewed an AI recommendation-sometimes correct, sometimes incorrect-with varying explanation depth and eventually could revise their initial answer; student's final answer being in line with AI recommendation operationalized our measure of \"trust.\" We estimated logistic models with control variables, including mixed-effects specifications to account for repeated observations.ResultsExplanation complexity is associated with higher trust on average, but its relevance depends on who reads it and whether AI is correct. Students who previously answered correctly exhibited lower willingness to defer, especially when AI was incorrect; conversely, agreement and consistency effects significantly amplified trust. These behavioral patterns highlight conditions under which AI-generated explanations can foster critical engagement or conversely encourage uncritical acceptance.ConclusionOur results point to a \"AI knows better\" heuristic at work-especially among nonexperts-where polished presentation is easily read as reliability, encouraging uncritical agreement with incorrect recommendations; in parallel, experts benefit more from deeper rationales when AI is accurate, yet still display under-reliance of correct assistance in many cases. Overall, trust calibration is driven less by any single cue than by the alignment of student performance, AI reliability, and explanation design, with prior agreement acting as a powerful amplifier of subsequent alignment.ApplicationOur findings imply that instructional approaches should promote independent reasoning before exposure to AI, deploy concise but diagnostically informative explanations, and include brief verification steps before accepting AI recommendations, especially for nonexperts who are more prone to harmful switches. Simple monitoring tools that track helpful versus harmful changes could support a more discerning and productive use of AI tools.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"487-503"},"PeriodicalIF":3.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145671080","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":"Quantitative Analysis and Mitigation of the Impact of Network Latency on Video Conferencing Communication Efficiency.","authors":"JianXiang Chen, Zhang Tao","doi":"10.1177/00187208251398477","DOIUrl":"10.1177/00187208251398477","url":null,"abstract":"<p><p>ObjectiveTo quantitatively analyze the impact of network latency on video conferencing communication efficiency, guide network latency design standards, and evaluate mitigation strategy.BackgroundNetwork latency substantially affects the quality of video conferencing. Existing research has focused on subjective assessments. Networking designers require quantitative latency guidelines.MethodThis research involved two studies: one quantifying the effect of network latency on task performance and the other testing a mitigation strategy. Each study involved two experiments: one (<i>N</i> = 60) using a building block \"exploratory task\" and the second (<i>N</i> = 30) using a text communication \"validation task.\" We followed similar test procedures across the experiments, using a different pool of participants for each of the four. For the second study, we also modified the apparatus by adding a foot pedal and light.ResultsThe completion time for the building block experiment increased with higher latency. Linear regression analysis showed that one-way network latency added approximately 2.6 s per second of delay in each communication round. We observed similar patterns in the text-based experiment. The mitigation strategy involving low-latency communication feedback on the speaker's intent substantially reduced mean task completion time under high-latency conditions.ConclusionThis research provides equations that describe the relationship between one-way network latency and communication efficiency. Implementing low-latency communication feedback on the speaker's intent can mitigate the impact of latency.ApplicationNetwork designers can refer to the conclusions of this research to specify network latency requirements. Video conferencing manufacturers can adopt the mitigation strategy to reduce the negative impacts of high network latency.</p>","PeriodicalId":56333,"journal":{"name":"Human Factors","volume":" ","pages":"470-486"},"PeriodicalIF":3.3,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145598018","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}