With the increasing prevalence of 3D videos, investigating the differences of viewing experiences between 2D and 3D videos has become an important issue. In this study, we explored the cognitive load induced by 2D and 3D video stimuli under various cognitive tasks utilizing electroencephalogram (EEG) data. We also introduced the Cognitive Load Index (CLI), a metric which combines θ and α oscillations to evaluate the cognitive differences. Four video stimuli, each associated with typical cognitive tasks were adopted in our experiments. Subjects were exposed to both 2D and 3D video stimuli, and the corresponding EEG data were recorded. Then, we analyzed the power within the 0.5-45 Hz frequency of EEG data, and CLI was utilized to evaluate the brain activity of different subjects. According to our experiments and analysis, videos that involve simple observational tasks (P <0.05) consistently induced a higher cognitive load in subjects when they were viewing 3D videos. However, for videos that involve calculation tasks (P >0.05), the differences in cognitive load induced by 2D and 3D video were not obvious. Thus, we concluded that 3D videos could generally induce a higher cognitive load, but the extent of the differences also depended on the contents of the video stimuli and the viewing purpose.
In this paper, we analyze the cognitive improvements that can be achieved through hacking the brain through the use of multiple methods to enhance neuroplasticity. Exposure to gaming, for example, has proven conducive for learning real-world abilities through auditory and visual stimuli. We will discuss cortical magnification and receptive field sizes, as well as topographic brain maps in the context of neuroplasticity. We finally propose more studies to be performed to improve musical talent and musical abilities.
Simulation-based training is an effective tool for acquiring practical skills, specifically to train new surgeons in a controlled and hazard-free environment, it is however important to measure participants cognitive load to decide whether they are ready to go into a real surgery. In the present study we measured performance on a surgery simulator of medical students and interns, while their brain activity was monitored by a mobile EEG device. 38 medical studentswere underwent 3 experiments undergoing a task with Simbionix simulator, while their brain activity was measured using a single-chann…
Working Memory (WM) load is an important cognitive feature that is highly correlated with mental effort. Several neurological biomarkers such as theta power and mid-frontal activity show increased activity with increasing WM load. Such correlations often break down in cognitively impaired individuals, making WM load biomarkers a valuable tool for the detection of cognitive impairment. However, most studies have used a multi-channel EEG or an fMRI, which are not massively accessible. In the present study, we evaluate the ability of novel features extracted from a single-channel EEG located on t…
Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
School of Computer Science, Technological University Dublin, Dublin, Ireland（计算机科学学院，都柏林技术大学，都柏林，爱尔兰）；Artificial Intelligence and Cognitive Load Lab, Applied Intelligence Research Centre, School of Computer Science, Technological University Dublin, Dublin, Ireland（人工智能与认知负荷实验室，应用智能研究中心，计算机科学学院，都柏林技术大学，都柏林，爱尔兰）
Fisher-Rao Distance Detects Shifts in Kinematic Profiles under Cognitive Load
Rutgers the State University of New Jersey（新泽西州立罗格斯大学）；Rutgers University Center for Cognitive Science（罗格斯大学认知科学中心）；Rutgers University Center for Biomedicine Imaging and Modelling（罗格斯大学生物医学成像与建模中心）
Sensory stimuli can be recognized more rapidly when they are expected. This phenomenon depends on expectation affecting the cortical processing of sensory information. However, virtually nothing is known on the mechanisms responsible for the effects of expectation on sensory networks. Here, we report a novel computational mechanism underlying the expectation-dependent acceleration of coding observed in the gustatory cortex (GC) of alert rats. We use a recurrent spiking network model with a clustered architecture capturing essential features of cortical activity, including the metastable activi…
The principal reason for measuring mental workload is to quantify the cognitive cost of performing tasks to predict human performance. Unfortunately, a method for assessing mental workload that has general applicability does not exist yet. This research presents a novel self-supervised method for mental workload modelling from EEG data employing Deep Learning and a continuous brain rate, an index of cognitive activation, without requiring human declarative knowledge. This method is a convolutional recurrent neural network trainable with spatially preserving spectral topographic head-maps from …
Nantes Université（南特大学）；Ecole Centrale Nantes（南特中央理工学院）；CNRS（法国国家科学研究中心）；LS2N（南特数字科学实验室）；Netflix Inc.（网飞公司）
The goal of most subjective studies is to place a set of stimuli on a perceptual scale. This is mostly done directly by rating, e.g. using single or double stimulus methodologies, or indirectly by ranking or pairwise comparison. All these methods estimate the perceptual magnitudes of the stimuli on a scale. However, procedures such as Maximum Likelihood Difference Scaling (MLDS) have shown that considering perceptual distances can bring benefits in terms of discriminatory power, observers' cognitive load, and the number of trials required. One of the disadvantages of the MLDS method is that th…
Dimensions of Vulnerability in Visual Working Memory: An AI-Driven Approach to Perceptual Comparison
Department of Biomedical Engineering, Southern University of Science and Technology（南方科技大学生物医学工程系）；Department of Psychology, University of Birmingham（伯明翰大学心理学系）
