1
|
Betts K, Reddy P, Galoyan T, Delaney B, McEachron DL, Izzetoglu K, Shewokis PA. An Examination of the Effects of Virtual Reality Training on Spatial Visualization and Transfer of Learning. Brain Sci 2023; 13:890. [PMID: 37371368 DOI: 10.3390/brainsci13060890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
Spatial visualization ability (SVA) has been identified as a potential key factor for academic achievement and student retention in Science, Technology, Engineering, and Mathematics (STEM) in higher education, especially for engineering and related disciplines. Prior studies have shown that training using virtual reality (VR) has the potential to enhance learning through the use of more realistic and/or immersive experiences. The aim of this study was to investigate the effect of VR-based training using spatial visualization tasks on participant performance and mental workload using behavioral (i.e., time spent) and functional near infrared spectroscopy (fNIRS) brain-imaging-technology-derived measures. Data were collected from 10 first-year biomedical engineering students, who engaged with a custom-designed spatial visualization gaming application over a six-week training protocol consisting of tasks and procedures that varied in task load and spatial characteristics. Findings revealed significant small (Cohen's d: 0.10) to large (Cohen's d: 2.40) effects of task load and changes in the spatial characteristics of the task, such as orientation or position changes, on time spent and oxygenated hemoglobin (HbO) measures from all the prefrontal cortex (PFC) areas. Transfer had a large (d = 1.37) significant effect on time spent and HbO measures from right anterior medial PFC (AMPFC); while training had a moderate (d = 0.48) significant effect on time spent and HbR measures from left AMPFC. The findings from this study have important implications for VR training, research, and instructional design focusing on enhancing the learning, retention, and transfer of spatial skills within and across various VR-based training scenarios.
Collapse
Affiliation(s)
- Kristen Betts
- School of Education, Drexel University, Philadelphia, PA 19104, USA
| | - Pratusha Reddy
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA
| | - Tamara Galoyan
- School of Education, Drexel University, Philadelphia, PA 19104, USA
| | - Brian Delaney
- School of Communication and Journalism, Auburn University, Auburn, AL 36849, USA
| | - Donald L McEachron
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA
| | - Kurtulus Izzetoglu
- School of Education, Drexel University, Philadelphia, PA 19104, USA
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA
| | - Patricia A Shewokis
- School of Education, Drexel University, Philadelphia, PA 19104, USA
- School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA 19104, USA
- College of Nursing & Health Professions, Drexel University, Philadelphia, PA 19104, USA
| |
Collapse
|
2
|
Longo L, Wickens CD, Hancock PA, Hancock GM. Human Mental Workload: A Survey and a Novel Inclusive Definition. Front Psychol 2022; 13:883321. [PMID: 35719509 PMCID: PMC9201728 DOI: 10.3389/fpsyg.2022.883321] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 05/10/2022] [Indexed: 12/05/2022] Open
Abstract
Human mental workload is arguably the most invoked multidimensional construct in Human Factors and Ergonomics, getting momentum also in Neuroscience and Neuroergonomics. Uncertainties exist in its characterization, motivating the design and development of computational models, thus recently and actively receiving support from the discipline of Computer Science. However, its role in human performance prediction is assured. This work is aimed at providing a synthesis of the current state of the art in human mental workload assessment through considerations, definitions, measurement techniques as well as applications, Findings suggest that, despite an increasing number of associated research works, a single, reliable and generally applicable framework for mental workload research does not yet appear fully established. One reason for this gap is the existence of a wide swath of operational definitions, built upon different theoretical assumptions which are rarely examined collectively. A second reason is that the three main classes of measures, which are self-report, task performance, and physiological indices, have been used in isolation or in pairs, but more rarely in conjunction all together. Multiple definitions complement each another and we propose a novel inclusive definition of mental workload to support the next generation of empirical-based research. Similarly, by comprehensively employing physiological, task-performance, and self-report measures, more robust assessments of mental workload can be achieved.
Collapse
Affiliation(s)
- Luca Longo
- Artificial Intelligence and Cognitive Load Lab, The Applied Intelligence Research Centre, School of Computer Science, Technological University Dublin, Dublin, Ireland
| | - Christoper D Wickens
- Department of Psychology, Colorado State University, Fort Collins, CO, United States
| | - Peter A Hancock
- Department of Psychology, Institute for Simulation and Training, University of Central Florida, Orlando, FL, United States
| | - Gabriela M Hancock
- Department of Psychology, California State University, Long Beach, CA, United States
| |
Collapse
|
3
|
Reddy P, Shewokis PA, Izzetoglu K. Individual differences in skill acquisition and transfer assessed by dual task training performance and brain activity. Brain Inform 2022; 9:9. [PMID: 35366168 PMCID: PMC8976865 DOI: 10.1186/s40708-022-00157-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Accepted: 03/08/2022] [Indexed: 11/23/2022] Open
Abstract
Assessment of expertise development during training program primarily consists of evaluating interactions between task characteristics, performance, and mental load. Such a traditional assessment framework may lack consideration of individual characteristics when evaluating training on complex tasks, such as driving and piloting, where operators are typically required to execute multiple tasks simultaneously. Studies have already identified individual characteristics arising from intrinsic, context, strategy, personality, and preference as common predictors of performance and mental load. Therefore, this study aims to investigate the effect of individual difference in skill acquisition and transfer using an ecologically valid dual task, behavioral, and brain activity measures. Specifically, we implemented a search and surveillance task (scanning and identifying targets) using a high-fidelity training simulator for the unmanned aircraft sensor operator, acquired behavioral measures (scan, not scan, over scan, and adaptive target find scores) using simulator-based analysis module, and measured brain activity changes (oxyhemoglobin and deoxyhemoglobin) from the prefrontal cortex (PFC) using a portable functional near-infrared spectroscopy (fNIRS) sensor array. The experimental protocol recruited 13 novice participants and had them undergo three easy and two hard sessions to investigate skill acquisition and transfer, respectively. Our results from skill acquisition sessions indicated that performance on both tasks did not change when individual differences were not accounted for. However inclusion of individual differences indicated that some individuals improved only their scan performance (Attention-focused group), while others improved only their target find performance (Accuracy-focused group). Brain activity changes during skill acquisition sessions showed that mental load decreased in the right anterior medial PFC (RAMPFC) in both groups regardless of individual differences. However, mental load increased in the left anterior medial PFC (LAMPFC) of Attention-focused group and decreased in the Accuracy-focused group only when individual differences were included. Transfer results showed no changes in performance regardless of grouping based on individual differences; however, mental load increased in RAMPFC of Attention-focused group and left dorsolateral PFC (LDLPFC) of Accuracy-focused group. Efficiency and involvement results suggest that the Attention-focused group prioritized the scan task, while the Accuracy-focused group prioritized the target find task. In conclusion, training on multitasks results in individual differences. These differences may potentially be due to individual preference. Future studies should incorporate individual differences while assessing skill acquisition and transfer during multitask training.
Collapse
Affiliation(s)
- Pratusha Reddy
- School of Biomedical Engineering, Science and Health Systems, Drexel University, 3508 Market St Suite 100, Philadelphia, PA, 19104, USA
| | - Patricia A Shewokis
- School of Biomedical Engineering, Science and Health Systems, Drexel University, 3508 Market St Suite 100, Philadelphia, PA, 19104, USA.,Nutrition Sciences Department-College of Nursing and Health Professions, Drexel University, 1601 Cherry St Free Parkway, Philadelphia, PA, 19102, USA.,School of Education, 3401 Market Street 3rd Floor Suite 3000, Philadelphia, PA, 19104, USA
| | - Kurtulus Izzetoglu
- School of Biomedical Engineering, Science and Health Systems, Drexel University, 3508 Market St Suite 100, Philadelphia, PA, 19104, USA. .,School of Education, 3401 Market Street 3rd Floor Suite 3000, Philadelphia, PA, 19104, USA.
| |
Collapse
|
4
|
Studying Brain Activation during Skill Acquisition via Robot-Assisted Surgery Training. Brain Sci 2021; 11:brainsci11070937. [PMID: 34356171 PMCID: PMC8303118 DOI: 10.3390/brainsci11070937] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Revised: 07/01/2021] [Accepted: 07/14/2021] [Indexed: 11/17/2022] Open
Abstract
Robot-assisted surgery systems are a recent breakthrough in minimally invasive surgeries, offering numerous benefits to both patients and surgeons including, but not limited to, greater visualization of the operation site, greater precision during operation and shorter hospitalization times. Training on robot-assisted surgery (RAS) systems begins with the use of high-fidelity simulators. Hence, the increasing demand of employing RAS systems has led to a rise in using RAS simulators to train medical doctors. The aim of this study was to investigate the brain activity changes elicited during the skill acquisition of resident surgeons by measuring hemodynamic changes from the prefrontal cortex area via a neuroimaging sensor, namely, functional near-infrared spectroscopy (fNIRS). Twenty-four participants, who are resident medical doctors affiliated with different surgery departments, underwent an RAS simulator training during this study and completed the sponge suturing tasks at three different difficulty levels in two consecutive sessions/blocks. The results reveal that cortical oxygenation changes in the prefrontal cortex were significantly lower during the second training session (Block 2) compared to the initial training session (Block 1) (p < 0.05).
Collapse
|